Adaptive Gamification and Game-Based Learning in Preschool and Early Primary Education: A Systematic Literature Review

Abstract

In recent years, adaptive gamification and adaptive game-based learning (GBL) have attracted the interest of researchers and educators as a response to the “one-size-fits-all” approach of conventional gamified applications. However, their effectiveness has shown mixed results, and the literature concerning preschool and early primary education remains scattered. Therefore, we performed a systematic literature review of 19 empirical studies published between 2016 and 2026, following the PRISMA model, from a total of 5069 records identified across nine electronic databases. This review examines the methodological approaches and assessment tools employed, the content areas, educational levels, and educational contexts addressed, the theoretical frameworks and adaptive mechanisms utilised, and the learning and motivational outcomes reported for young learners. Our findings revealed a strong concentration on mathematics, a heavy reliance on researcher-developed platforms, and limited explicit theoretical grounding. Moreover, most studies adapted only the learning content, while the game elements themselves remained fixed. Although most studies reported positive learning and motivational outcomes, the results were not uniform, with prior knowledge being the most common moderating variable. Benefits are most visible when adaptive systems support children’s pacing, prior knowledge, or task difficulty, with some studies showing improvement in learning efficiency rather than learning gains. Overall, this review reveals the emerging trends and challenges in this field and provides a framework and insight for future researchers regarding the design of adaptive learning environments for young children.

1. Introduction

Today, digital tools are being woven into education and teachers’ routines, pushing the boundaries of how they teach, absorb, and measure knowledge across all educational levels. A teacher may use them to introduce an idea, a child may use them for practice, and assessment can also take place during the activity rather than only after it has finished [1,2]. For preschool children, though, the design question is very concrete: can they understand what to do, stay with the task, and use the feedback without becoming lost or frustrated? An activity made for older students may ask for too much reading, choices, or a prolonged wait between tasks and giving a response [3,4]. Games are relevant here because they already resemble much of children’s informal learning. Children try, watch the result, copy others, repeat an action, and slowly adjust what they do. A game-based task can use that rhythm, while still keeping a clear educational purpose for the teacher [5,6].

Two related but distinct approaches that have drawn researchers’ attention in education are game-based learning (GBL) and gamification. In GBL, the learning content is placed inside an actual game, so children encounter the educational material through the game’s rules, tasks, story, feedback, or challenges [7,8]. Gamification is different. It uses selected game elements in a non-game learning activity, such as points, rewards, levels, badges, narratives, or challenges, mainly to strengthen participation and motivation [9,10]. Both approaches have the potential to increase student engagement, motivation, and, in many cases, academic performance across educational levels and subject areas [11,12,13,14]. However, mixed findings regarding learning outcomes and longer-term motivation have prompted further investigation into ways to enhance their effectiveness [13,15,16]. The ad hoc use of game elements without a formal design process or theoretical grounding, and without consideration of individual differences, is often cited as a reason for these limitations [17,18]. Moreover, the commonly employed “one-size-fits-all” approach, which treats all users identically and assumes that they will respond similarly to each game element or mechanic, has also been identified as ineffective [19,20]. Ignoring learners’ needs and repeatedly using similar game elements has been shown to increase abandonment and diminish engagement over time [17,21].

Adaptive gamification and adaptive GBL are two emerging methodological directions that developed in response to the limitations of uniform designs. In practice, adaptation means that the learning environment does not treat every child identically, because game elements and mechanics will not work equally well for all learners. One child might need a simpler hint after two incorrect answers, while another might need a more difficult task because the current one has become too easy. These small changes are the kinds of decisions that adaptive gamification attempts to make using information based on the user’s actions, preferences, or personal profile [15,22]. In adaptive GBL, the same principle is usually tied more closely to the learning sequence. The environment may choose the next item, slow the pace, repeat a concept, change the feedback, or adjust the difficulty while the student is playing [23,24]. This flexibility is useful in theory, but it also makes the evidence more difficult to interpret. If a study reports improvement, it is not always clear whether the result arose from the game, the feedback, the adaptive rule, the teacher’s support, or the child’s prior knowledge. At present, the number of empirical studies remains too small to answer these questions, especially in ordinary classroom conditions [6,16]. The age group makes the issue even more complex, as preschool and early primary children are still developing language fluency, attention, self-regulation, motor control, and social understanding [25,26]. Play-based activities have the potential to support social skills, attention, engagement, and cognitive development [27,28]. However, few studies focus directly on adaptive gamification and GBL in preschool and early childhood education [4,29,30,31]. A system for this age group must therefore be judged by more than technical adaptivity. It must also consider whether children can follow the instructions, recognise the feedback, use the interface, and receive adult support when the task becomes difficult. For this reason, attention to child-friendly user interfaces, developmentally appropriate technology, and pedagogical frameworks grounded in early childhood theory is essential [32,33].

Against this background, the present review maps how adaptive gamification and GBL have been studied in early childhood education. Following a Systematic Literature Review (SLR) approach grounded in PRISMA guidelines, we examined peer-reviewed studies published in relevant databases to give researchers and educators a clearer understanding of the current state of the art, focusing on the methodologies employed, the content areas addressed, the theoretical frameworks that underpin adaptive approaches, the specific adaptation mechanisms used, and the learning outcomes reported. Consequently, the research questions were as follows:

RQ1: What methodological approaches and assessment tools have been employed in studies investigating adaptive gamification and GBL in preschool and early childhood education?

RQ2: What content areas, educational levels and educational contexts have been addressed through adaptive gamification and GBL approaches in preschool and early education settings?

RQ3: What theoretical frameworks and adaptation mechanisms have been utilised in adaptive gamification and GBL for young learners?

RQ4: What are the learning and motivational outcomes of the adaptive gamification and adaptive GBL on preschool and early childhood learners?

2. Review of the Literature

2.1. Adaptive Gamification and GBL

Educational games are now discussed less as classroom extras and more as designed learning environments. In this literature, the game is more than a setting where learning takes place; it can carry feedback, repetition, challenge, assessment, and motivation at the same time [13]. In game-based learning (GBL), the educational content is built into the activity and not placed next to it. This means that moving forward requires the learner to do something that is itself educationally meaningful, such as counting objects, comparing quantities, or working through a classification problem [8,14].

Gamification is related to this, although it does not work in the same way, since it usually takes an existing learning activity and adds selected game features to it. The most common examples are points, badges, and leaderboards, though the range also extends to avatars, narratives, level structures, and progress indicators [12,13,22]. The purpose is almost always motivational in order to keep learners engaged or to nudge behaviour in a particular direction. However, these two terms are not the same. GBL embeds learning inside the act of playing, while gamification layers game-like features on top of what is still a conventional learning task [13,14,34,35]. Keeping this distinction in mind matters when reading across studies. A child playing a GBL application meets the content through the game’s own rules, actions, and feedback. Gamification works differently because the instructional task is still front and centre, with game mechanics being layered around it to keep the students engaged, which after all is the direct goal of gamification [7,13]. The two can overlap in digital products, but their effects are not interchangeable. Evidence gathered from a full maths game, for instance, says little about what happens when a teacher adds badges to a worksheet activity.

Part of what makes GBL attractive to learners is that they stay active throughout the learning process as they have to make choices, process the feedback the gaming environment gives them, and change course when something does not work [36,37]. Yet the evidence from these studies is still inconclusive. Some interventions work well, but others produce modest or uneven results. A recurring problem that seems to come up is that many learning environments follow the same “one size fits all” approach, utilising the same pace, challenge, rewards, and social mechanics with learners, even though it is clear that they differ in prior knowledge, confidence, motivation, and preference. The result, in many cases, is that learners end up with experiences that feel repetitive or poorly matched to their current level [17,18,19,20,21,38]. This has led a growing number of researchers to call for some form of personalisation within gamified systems [15,22].

Adaptive gamification responds to this problem by adjusting the game layer on the basis of what is known about each user, their behaviour, preferences, or performance history [19,22]. The goal is not to keep adding on more mechanics but to stop assuming that all learners are motivated in the same way. A badge that motivates one child can frustrate another, and a leaderboard that makes progress visible for some children may create unwanted comparison pressure for others. Whether a particular game element helps or hinders engagement depends very much on who the learner is and what the context looks like [16,39].

Adaptive GBL works closely in a similar way, though here the focus is more on the learning path and not solely on the game features that will get adjusted. After observing how a learner behaves, the system might alter task difficulty, the type of hints it provides, pacing, or the order of activities [23]. The methods behind this vary considerably. Some researchers use item response theory or psychometric models, while some others have turned to classifiers, reinforcement learning, or AI-supported approaches that adjust instruction in real time [4,24,40,41,42]. In this way, adaptive GBL keeps the focus on the changing relationship between the learner, the learning task, and the support that the system provides.

2.2. Theories Underpinning Adaptive Gamification and GBL

A system can only adapt meaningfully if it has some account or knowledge of the learner. In adaptive gamification, that account often concerns motivation, not only accuracy or achievement. This is why player typologies appear so often in the literature. The Hexad framework is the most common example in educational adaptive gamification. Developed by Marczewski and refined by Tondello et al. [39], it lays out six player types, each associated with different motivational drivers, that can guide the selection of game elements [22,39]:

• Achievers tend to be drawn to progress, mastery, and tasks that feel demanding but solvable.
• Players are most responsive to visible incentives—points, badges, prizes, and other tangible rewards.
• Philanthropists find motivation in helping others or working toward a collective goal.
• Socializers gravitate toward interaction, collaboration, and a sense of belonging within a group.
• Free Spirits look for choice and exploration, and tend to resist overly structured paths.
• Disruptors are drawn to questioning rules, testing boundaries, and pushing against the system.

The Hexad model is useful mainly because it reminds designers that motivation is not uniform. It also sits comfortably beside self-determination theory, which helps explain its popularity in educational work. Still, it should not be used as a fixed label for children. The same learner may seek achievement in one activity, social contact in another, and freedom in a third. When it comes to adaptive design, then, the Hexad is more useful as a rough guide to what might motivate a particular learner at a given moment than as a fixed label [16,43].

Other player models exist but have gained less traction. Bartle’s taxonomy, which includes Achievers, Explorers, Socializers, and Killers, was developed for massively multiplayer online games and does not map well onto classrooms or young children [44,45]. BrainHex, which draws on neurobiological response patterns, has seen only limited uptake in educational settings [16,46]. A different strategy is to omit player types and adapt on the basis of learning styles, personality traits, age, or gender [3,15,47]. Despite these alternatives, player types were still the most common personalisation variable in the review by Oliveira et al. [16].

Self-Determination Theory (SDT) is the main motivational theory behind much of this work. Ryan and Deci [48] argue that motivation depends on three psychological needs: autonomy, competence, and relatedness [48,49]. Autonomy, in this framework, has to do with agency and the sense that one’s choices matter. Competence refers to feeling capable of meeting the demands of a task, while relatedness involves the experience of connection with other people. When these three needs are met, intrinsic motivation becomes more likely—and intrinsic motivation, in turn, has been associated with deeper learning, creative thinking, and sustained effort [48,50].

What makes SDT useful here is that game elements are not inherently beneficial. A progress bar, for example, can support a child’s sense of competence when it shows real improvement, but if the bar feels disconnected from what the child is doing, it can lose its meaning. In a similar way, choices support autonomy only when the options are really different, and collaborative tasks support relatedness only when the interaction between children is positive [13,45]. SDT further distinguishes between identified, introjected, and external regulation, which goes some way toward explaining why rewards work for certain learners and produce adverse or unintended effects for others [51,52].

Flow theory shifts attention to what the learner actually experiences while doing the activity. Csikszentmihalyi [53] characterised flow as a state of deep absorption in which the task feels both engaging and within reach [20,53]. For this state to occur, goals need to be clear, feedback needs to arrive quickly, and there has to be a reasonable match between the difficulty of the task and the learner’s current skill. Tasks that are too easy lose the child’s attention; tasks that are too hard provoke frustration [40,54]. Adaptive systems connect directly to this idea, since they can recalibrate challenge in real time. The Zone of Optimal Engagement makes a related argument. It proposes that the learner should feel stretched but not overwhelmed [55]. Goal-setting theory contributes a further piece: specific, well-understood goals tend to improve performance, provided learners also receive feedback and feel some ownership of the objective [13,45,56].

For early childhood GBL, motivational theories need to be joined with developmental theories. Young children bring developing language, symbolic thought, motor control, and attention to digital games. Vygotsky’s zone of proximal development (ZPD) fits naturally here: learning tends to be most productive when a task sits just beyond what the child can manage alone but is still within reach with some help [36,57]. Bloom’s mastery learning model points in a similar direction, since it calls for adjusting time, feedback, and instruction until the learner actually reaches understanding rather than simply moving on [36,58]. At the age range most relevant to this review, meaning roughly 2 to 7, Piaget’s preoperational stage is also worth noting, as children at this stage are heavily engaged in symbolic play, language acquisition, and concrete problem solving [26,59]. More broadly, constructivist perspectives that trace back to Piaget and Papert align well with GBL, given their emphasis on learning through active exploration rather than passive reception [1,60].

These theories also shape how adaptation gets built in practice. Hallifax et al. [15] draw a line between static and dynamic adaptation. In static approaches, the system collects information before the activity starts, through a questionnaire, a diagnostic task, or an existing learner profile, and uses that snapshot to set up the experience. Dynamic adaptation, by contrast, occurs in real time: the system watches how the learner performs, how long they spend on tasks, and sometimes how they appear to feel, and it adjusts accordingly [15]. Building a static system is more straightforward, but the initial profile can become stale as the child improves or loses interest. Dynamic systems are difficult to implement reliably, though they handle the shifting nature of learning and engagement more gracefully [15,17].

In practice, systems vary widely in how they implement adaptation. At the simpler end, rule-based systems work with thresholds, if-then conditions, and mastery gates to decide when to change what a child sees [61,62]. Elo-rating algorithms offer a more continuous approach; adapted from psychometric modelling, they have been used in games like the Number Sense Game to estimate both learner ability and item difficulty on the basis of response accuracy [24,63]. Further along the complexity spectrum, machine-learning approaches, reinforcement learning, evolutionary algorithms, and related techniques, can recommend learning paths or even generate content while taking into account how the learner has performed so far [3,40,41,64]. Some systems go beyond performance data and try to read the learner’s emotional state from facial expressions or behavioural cues, adjusting support or challenge in response [60,65]. Others use concept-effect relationship models, which map out learning problems through concept maps and recommend what to work on next [61].

The seven-layer AI architecture proposed by Kassenkhan et al. [4] shows how complex adaptive systems can become, with separate layers handling perception, student modelling, content generation, and pedagogical decision-making. Yet most existing systems for young children remain simpler, often relying on rule-based or Elo-type approaches rather than full AI pipelines. Whether more complex architectures improve outcomes for this age group is still an open question.

2.3. Adaptive Gamification and GBL in Educational Settings

Young children differ from older learners in ways that matter for game design. They bring limited reading ability, short attention spans, still-developing fine motor skills, and a heavier reliance on adult support. Games designed for this age group need to account for all of this, including simple interfaces, feedback that is visual or auditory rather than text-heavy, and sessions short enough to match what young children can sustain [32,33]. Adaptation, then, cannot stop at task difficulty. The delivery of instructions, the pace of the game, the form of feedback, the required motor precision, and the amount of scaffolding provided by an adult or the system are all candidates for adaptation.

What adaptive gamification and adaptive GBL offer, at least in principle, is a learning environment that can respond to what it observes about the child. Rather than locking every learner into the same pace, reward scheme, and difficulty level, an adaptive system can reshape the experience as information about the learner accumulates. The evidence so far is limited but not discouraging. In some studies, adaptive versions of a game produced stronger learning outcomes than their non-adaptive counterparts [63]. Others have found efficiency gains, such as learners reaching comparable outcomes but, in less time, [24]. There is also early evidence that adaptive systems may be particularly helpful for children who find mathematics difficult [66].

At present, the literature is fragmented. Studies target different age groups, cover different content areas, rely on different adaptation methods, draw on different theories, and measure different outcomes. Comparisons across studies are not easy, and generalising beyond a single platform or classroom is more difficult still. This review synthesises the methodologies, theoretical underpinnings, adaptation mechanisms, and outcomes reported to date, with the aim of establishing what the field has demonstrated and what remains to be demonstrated before adaptive gamification and adaptive GBL can be considered ready for widespread use in early childhood education.

3. Review Questions

This review was guided by four research questions, each designed to map a different aspect of the current literature on adaptive gamification in preschool and early childhood education:

RQ1: Which methodological approaches and assessment tools have researchers used when studying adaptive gamification and GBL with preschool and early childhood populations?

RQ2: What content areas, educational levels, and learning contexts have been covered by adaptive gamification and GBL research in early education so far?

RQ3: What theoretical frameworks and adaptation mechanisms appear in the literature on adaptive gamification and GBL for young learners?

RQ4: What learning and motivational outcomes have been reported for preschool and early childhood learners who used adaptive gamification or adaptive GBL?

These questions grew out of an effort to connect what is known about adaptive gamified applications in preschool and early childhood education, including their theoretical grounding, the game elements they use, the outcomes they report, and the contexts in which they have been tested, into a coherent picture. The adaptivity mechanism received particular attention throughout because how a system adjusts to each learner is the central concern that sets this review apart from earlier work on non-adaptive gamification.

4. Method

To ensure a rigorous examination of adaptive gamification and adaptive GBL in preschool and early primary education, guided by Kitchenham et al. [67], we utilised a structured method to identify, gather, filter and select research relevant to our specific questions.

Our methodology strictly adhered to the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [68] and the PRISMA 2020 checklist can be found in the Supplementary Materials. It should be noted that the present review was not registered in a systematic review database, and a separate protocol was not prepared. The research questions, eligibility criteria, search strategy, and extraction items were, however, defined in advance and are reported in full in the sections that follow, making the review process as transparent and repeatable as possible. The seven distinct steps were:

• Specifying research questions;
• Searching academic databases;
• Establishing inclusion/exclusion criteria;
• Screening and selecting studies;
• Analyzing and extracting data;
• Synthesizing the results;
• Writing the SLR report.

4.1. Journal Research Methodology

This SLR facilitated a search of nine electronic databases and academic search engines for scholarly literature and academic resources. For the sake of credibility and integrity, the review was limited exclusively to peer-reviewed articles and conference papers. The databases searched were Taylor & Francis Online, Wiley Online Library, Google Scholar, SpringerLink, Scopus, Web of Science, IEEE Xplore, Eric, and Science Direct. Google Scholar was additionally chosen for being the largest academic search engine today [69] and for providing access to relevant articles outside the scope of traditional educational journals [70]. The aforementioned database searches were conducted between 13 and 18 of April 2026.

Adaptive gamification has often been associated and referred to with different terms. For example, adaptive gamified applications, personalised game-based learning, adaptive game-based learning environments, and dynamic difficulty adjustment all refer, to some extent, to adaptive gamification content. As Cronin et al. [71] suggest, an effective SLR requires using alternative keywords to capture all relevant literature. Therefore, we applied Boolean operators (AND, OR) to combine thesaurus-derived synonyms with our core research concepts [71]. The final search strings, presented in

Table A1. Search Strings and Initial Retrieval Results by Database.

#	Database	Search Strings	Records
1	Scopus	TITLE-ABS-KEY((gamification OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious games” OR “educational games” OR “playful learning” OR “game-enhanced learning”) AND (adaptive OR personalized OR personalised OR individualized OR differentiated OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND (preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years” OR “young children” OR “elementary school” OR “primary school” OR “primary education”)) AND PUBYEAR > 2015 AND PUBYEAR < 2027 AND (LIMIT-TO(LANGUAGE, “English”))	217
2	ScienceDirect	Multi-row Advanced Search (8-connector limit per row). Row 1: gamification OR “game-based learning” OR “game elements” OR “serious games” OR “educational games”. Row 2 (OR): “playful learning” OR “game-enhanced learning” OR “game mechanics”. Row 3 (AND): adaptive OR personalized OR personalised OR individualized OR differentiated. Row 4 (OR): “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”. Row 5 (AND): preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years”. Row 6 (OR): “young children” OR “elementary school” OR “primary school” OR “primary education”.	654
3	Springer	(gamification OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious games” OR “educational games” OR “playful learning” OR “game-enhanced learning”) AND (adaptive OR personalized OR personalised OR individualized OR differentiated OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND (preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years” OR “young children” OR “elementary school” OR “primary school” OR “primary education”)	1000 *
4	Wiley	(gamification OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious games” OR “educational games” OR “playful learning” OR “game-enhanced learning”) AND (adaptive OR personalized OR personalised OR individualized OR differentiated OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND (preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years” OR “young children” OR “elementary school” OR “primary school” OR “primary education”)	1000 *
5	Taylor & Francis	(gamification OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious games” OR “educational games” OR “playful learning” OR “game-enhanced learning”) AND (adaptive OR personalized OR personalised OR individualized OR differentiated OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND (preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years” OR “young children” OR “elementary school” OR “primary school” OR “primary education”)	1000 *
6	Google Scholar	(gamification OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious games” OR “educational games” OR “playful learning” OR “game-enhanced learning”) AND (adaptive OR personalized OR personalised OR individualized OR differentiated OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND (preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years” OR “young children” OR “elementary school” OR “primary school” OR “primary education”)	1000 *
7	Web of Science	TS = (gamification OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious games” OR “educational games” OR “playful learning” OR “game-enhanced learning”) AND TS = (adaptive OR personalized OR personalised OR individualized OR differentiated OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND TS = (preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years” OR “young children” OR “elementary school” OR “primary school” OR “primary education”) AND LA = (“English”) AND PY = (2016–2026)	162
8	ERIC	(gamification OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious games” OR “educational games” OR “playful learning” OR “game-enhanced learning”) AND (adaptive OR personalized OR personalised OR individualized OR differentiated OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND (preschool OR “pre-school” OR kindergarten OR “early childhood” OR “early years” OR “young children” OR “elementary school” OR “primary school” OR “primary education”)	12
9	IEEE Xplore	(“gamification” OR “game-based learning” OR “game elements” OR “game mechanics” OR “serious game *” OR “educational game *” OR “digital game *” OR “playful learning”) AND (“adaptive” OR “personalized learning” OR “individualized” OR “differentiated” OR “customized” OR “dynamic difficulty” OR “intelligent tutoring” OR “adaptive learning”) AND (“preschool” OR “pre-school” OR “kindergarten” OR “early childhood” OR “early years” OR “young children” OR “early childhood education”)	24

* Export cap of 1000 records applied. The first 1000 most relevant results were screened for Springer, Wiley, Taylor & Francis, and Google Scholar based on relevance. All searches restricted to English-language publications, 2016–2026. ScienceDirect: Multi-row Advanced Search was used since there is an 8-connector per row limit but all three conceptual clusters preserved across rows. IEEE Xplore: Wildcards (e.g., serious game*, educational game*) added where the platform supports them. ERIC & other databases: Language and date restrictions applied via interface filters rather than embedded in the search string.

, merged these alternatives with our primary research topics, namely “adaptive gamification,” “early childhood education,” “gaming elements,” and “personalised learning.”

Following an evaluation of the specific syntax requirements for each database, a final search string was developed by integrating Boolean and basic operators within parentheses. The search strategy was organised around three concept clusters: gamification/game-based learning/game elements, adaptivity/personalisation, and preschool or early primary education. These clusters were combined using the general structure: (gamification/game-based learning/game elements terms) AND (adaptive/personalised learning terms) AND (preschool/early primary terms).

The terms shown in

Table 1. Core concepts and synonyms.

Core Concepts	Synonyms
Gamification/Game-Based Learning/Game Elements	Gamification, gamified, gamified environment, gamified application, applied game design, game-based learning, serious game, educational game, game elements, game mechanics, gaming mechanics, gaming components, game design elements, game features, gamification elements
Adaptive/Personalized	Adaptive, personalized, personalised, tailored, individualized, adaptive learning, personalized learning, dynamic difficulty adjustment, DDA, AI-driven, machine learning, intelligent tutoring
Early Childhood Education	Preschool, preschool, kindergarten, early childhood, early years, early primary, elementary education, primary education, primary school, young children, young learners

formed the main pool of keywords and synonyms used to construct the searches. However, the exact wording, use of quotation marks, wildcards, Boolean operators, and number of query rows were adjusted according to the requirements and limits of each database. In Google Scholar, Taylor & Francis Online, Wiley Online Library, and SpringerLink, only the first 1000 most relevant results were screened. Also, in databases with search constraints, such as ScienceDirect, the queries were modified through multiple rows or variations while preserving the same three concept clusters. Database-specific filters were also used where appropriate, such as language and publication-year limits. The exact search strings and retrieval numbers are presented in Table A1.

4.2. Inclusion/Exclusion Criteria

In the interest of conducting our research, we established specific eligibility criteria to systematically screen the identified literature, select studies directly aligned with our research objectives while excluding those that did not meet our predefined requirements. The inclusion and exclusion criteria were as follows:

Inclusion:

The study must be empirical (quantitative, qualitative, or mixed methods) in a learning environment. Systematic reviews, meta-analyses, literature reviews, scoping reviews, conceptual papers, framework papers, design-only papers, and opinion papers were excluded.

An adaptive or personalised gamified practice was used in the study. The gamified practice must have included at least one mechanism that adjusts to the individual learner (e.g., dynamic difficulty adjustment, learning-style adaptation, AI-driven personalisation, mastery-based progression, or procedural content generation). Static gamification without any adaptive component was excluded.

The study was conducted in a preschool or early primary education setting. Eligibility was determined primarily by educational level, that is, whether children were enrolled in kindergarten, preschool, or early primary education. When educational-level information was unavailable or ambiguous, the children’s reported age, generally 3–9 years, was used as a secondary indicator. When the reported grade and age did not align, educational level was the deciding criterion. Studies focused exclusively beyond early primary education, including secondary school students, adolescents, or higher education populations, were excluded.

The study focused on children’s learning or developmental outcomes, not exclusively on teacher perceptions, teacher adoption, or teacher education.

The article is a peer-reviewed journal paper or a conference paper.

The article is published from 2016 to 2026. Since adaptive gamification in early childhood is a relatively recent area of research, and to capture the most current trends in adaptive learning technologies, we investigated studies from the past ten years.

Exclusion:

The study is not written in English.

The study does not describe or mention the adaptive mechanism used.

The study only includes opinions about adaptive gamified practice without empirical evidence.

The study is listed in another database (duplicate).

The study is only published as an abstract.

The full text of the study is not accessible or retrievable.

4.3. Review Process

To ensure a feasible yet rigorous review process, we adopted the strategy of Petticrew and Roberts [72] by incorporating specific timeframe and logic criteria. For databases that returned extensive results, such as Wiley Online Library, Google Scholar, SpringerLink, and Taylor & Francis Online, the screening process was limited to the first 1000 most relevant entries, following the protocol suggested by Haddaway et al. [73]. All other articles were screened in databases where the total number of results was smaller. The entries in all databases were ordered by each platform’s own relevance ranking. It should be noted that this ranking, especially in Google Scholar, is not fully transparent and can shift over time, which affects reproducibility.

The initial search across all nine databases produced a total of 5069 records, as shown in

Table 2. Search results summary across databases.

Database	Total Screened	Relevant	Direct Match	Borderline	Duplicates Removed
Taylor & Francis	1000	27	19	8	—
Wiley	1000	34	25	9	—
Google Scholar	1000	65	64	1	—
Springer	1000	27	27	0	—
Scopus	217	52	42	10	—
Web of Science	162	21	8	13	—
IEEE Xplore	24	2	1	1	—
ERIC	12	2	2	0	—
ScienceDirect	654	21	18	3	—
TOTAL	5069	251	206	45	—
After Deduplication	—	192	160	32	59

. The records were exported to Zotero, put in individual files and were screened for relevance. From these, 251 articles were identified as relevant and kept for further review. Then, the records were exported from Zotero, where duplicates were identified by matching titles and/or Digital Object Identifiers (DOI). The full set was also transferred to a spreadsheet and double checked manually to validate duplicates. After the removal of 59 duplicate entries that were indexed across multiple databases, 192 unique articles remained and entered the screening process.

In the first stage, title screening was conducted on the 192 unique articles. Based on title review and relevance to the research topic, 84 articles were excluded. The exclusion reasons at this stage included studies that were not empirical, such as systematic reviews, meta-analyses, literature reviews, scoping reviews, and conceptual or framework papers (44 studies), studies focused exclusively on teachers or teacher education (7 studies), and studies with participants clearly outside the target age range (3 studies). An additional 30 borderline studies were flagged for age verification at the abstract stage but were retained at this point. The remaining 108 articles proceeded to abstract screening.

In the second stage, the abstracts of the 108 studies were carefully reviewed against all inclusion criteria, with particular emphasis on the presence of an adaptive or personalised element in the gamified intervention. This criterion proved to be the most consequential filter, as the majority of studies employed static game-based learning without any form of adaptation to the individual learner. Approximately 70 studies were excluded for lacking an adaptive or personalised component. An additional 5 studies were revealed as reviews at the abstract stage, 1 study was confirmed to focus on teachers, 1 study was confirmed to be outside the age range, and 1 study was identified as a framework paper without empirical child data. After abstract screening, 30 studies remained for full-text review.

In the third stage, the full texts of the 30 studies were obtained and thoroughly reviewed. However, 2 studies were excluded because their full-text PDFs were permanently unavailable, 7 studies were excluded because their samples extended beyond the target group or did not provide grade or age separated results, and 2 studies were also excluded because they were mainly design papers. The author kept notes and read each article multiple times to properly comprehend its content, procedures, methods and findings displayed. The 19 articles that composed the final dataset were included in the systematic review. It should be noted that, the screening, selection, and data extraction process was conducted by the main author, while a second researcher, with experience in systematic literature reviews, subsequently checked the search results, the documented screening process, the eligibility decisions across the screening stages and the final set of included studies. Any disagreements were resolved through discussion [74]. One point of disagreement concerned the inclusion of a study whose sample extended beyond the target age range. Although Benton et al. [75] had grounds for inclusion, since the children had reading difficulties and worked on early-primary curriculum content (UK Year 2, ages 6–7), placing the intervention close to the developmental level, the students’ chronological age was higher (8–11 years, M = 9.64), it was decided not to retain the study in the final corpus. This decision was made to avoid weakening the conceptual coherence of the review and to ensure that the included studies remained clearly within the preschool and early-primary scope. The PRISMA process that we followed is depicted in Figure 1.

4.4. Data Extraction and Analysis

The 19 articles (

Table 3. Articles included in the systematic review.

No.	Author (s)	Educational Level	Content Area	Educational Context
1.	Thai, Bang & Li [66]	TK/Kindergarten	Mathematics	Commercial adaptive tablet app (“My Math Academy” by Age of Learning)
2.	Vanbecelaere et al. [77]	Kindergarten	Literacy/Reading	Self-developed adaptive digital game (“Reading Game”)
3.	Debeer, Vanbecelaere et al. [24]	Grade 1	Mathematics	Self-developed adaptive digital game (“Number Sense Game”)
4.	Vanbecelaere et al. [63]	Grade 1	Mathematics	Self-developed adaptive digital game (“Number Sense Game”)
5.	Agudo, Rico & Sánchez [32]	Preschool	EFL (English vocabulary)	Self-developed adaptive hypermedia game system (“Shaiex”)
6.	Bang, Li & Flynn [36]	KG—Grade 1	Mathematics	Commercial adaptive tablet app (“My Math Academy” by Age of Learning)
7.	Wei, She & Du [64]	Preschool	Multiple/general curriculum	Self-developed AI-powered adaptive curriculum framework (GNN + GA-MOO)
8.	Junruang & Kanjug [26]	KG Year 2	CT + Creative Thinking	Self-developed constructivist personalised learning environment with AI + gamification
9.	Hooshyar, Yousefi & Lim [40]	Preschool	Early English reading (EFL)	Self-developed web-based game with PCG framework (“DLLgame”)
10.	Eng, Tsegai-Moore & Fisher [78]	Preschool	Executive Function	Self-developed gamified assessment (“Frankie’s Big Adventure”, Unity)
11.	Zourmpakis et al. [45]	Grade 3	Science (water cycle)	Self-developed adaptive gamification app (Unity3D, open-world)
12.	Zourmpakis et al. [43]	Grade 3	Science (water cycle)	Self-developed adaptive gamification app (Unity3D)
13.	Wang [41]	Preschool	Mathematics	Self-developed preschool math game (Unity, DQN backend, tablet)
14.	Feng et al. [79]	Kindergarten	Mathematics	Commercial adaptive tablet app (“My Math Academy” by Age of Learning)
15.	Zidianakis et al. [80]	Preschool	Play/Developmental skills	Self-developed AmI environment (“The Farm Game”, tangible + digital)
16.	Jagust et al. [20]	Grades 2–3	Mathematics	Self-developed mobile learning platform (“Math Widget”, tablet)
17.	Sayed et al. [3]	Grade 3	Mathematics	Self-developed Moodle-based adaptive e-learning platform (“APPEAL”)
18.	Chu et al. [61]	Grade 3	Mathematics (fractions)	Self-developed browser-based adventure game with CER diagnostic module
19.	Kaushalya et al. [60]	Kindergarten	Multiple (shapes, colours, storytelling)	Self-developed web-based platform (“Punchi”, webcam mood recognition)

) that were selected were further analysed with their data being aggregated to answer the core research questions using a triangulation method [76] for enhanced validity and credibility. At first, a thorough review and analysis of the trends of adaptive GBL and adaptive gamification studies, relevant motivational theories, methodologies and adaptive mechanisms, results, effects, and connections to early childhood education were conducted.

To bolster the integrity of this data, we utilised a systematic keyword search for cross-study comparison. The triangulation was finalised through a document analysis, which served to confirm the convergence and accuracy of the synthesised information. The following essential information was extracted from each article:

• education level;
• content area;
• educational context;
• methods and research design;
• adaptive learning mechanism;
• theoretical framework;
• gaming elements and whether the gaming elements themselves are adapted;
• motivational and educational outcomes;
• assessment tools and named instruments;
• geographic location;

neurotypical vs. non-neurotypical target population.

In the present review, we sought to distinguish between studies that adapt only content difficulty or learning paths and studies that adapt the gaming elements themselves (e.g., selecting different game mechanics based on the learner’s player type or developmental profile). This distinction is quite important, as it reveals the depth of personalisation in each adaptive gamified system and has implications for both design and learning outcomes. However, it should be noted that the screening, eligibility assessment, and initial data extraction were conducted mainly by one researcher. Yet, as previously mentioned, in order to reduce the risk of single-reviewer bias, a second researcher with previous experience in systematic literature reviews subsequently reviewed the process.

Additionally, each study was coded for whether the adaptive mechanism was rule-based, model-based, or AI/ML-driven. This classification provides insight into the technological sophistication of the adaptive systems and their evolution over time, particularly the emerging trend toward more complex, multimodal adaptive approaches observed in studies published from 2024 onward.

Methodological quality was appraised with the Mixed Methods Appraisal Tool (MMAT, version 2018) [81]. Following the MMAT guide, for each study, the two screening questions were considered together with the appropriate MMAT design category based on the methods used (

Table 4. MMAT categorization and methodological quality criteria.

No.	Author (s)	MMAT Category	Methodological Quality Criteria Met (In Line with MMAT (2018) Guidance, the Number of Criteria Met Is Reported Descriptively and Should Not Be Interpreted as an Overall Quality Score)
1.	Thai et al. [66]	2—Quantitative RCT	4/5
2.	Vanbecelaere et al. [77]	3—Quantitative non-randomised	3/5
3.	Debeer et al. [24]	2—Quantitative RCT	3/5
4.	Vanbecelaere et al. [63]	2—Quantitative RCT	4/5
5.	Agudo et al. [32]	5—Mixed methods	2/5
6.	Bang, Li & Flynn [36]	5—Mixed methods	4/5
7.	Wei, She & Du [64]	4—Quantitative descriptive	2/5
8.	Junruang & Kanjug [26]	5—Mixed methods	4/5
9.	Hooshyar et al. [40]	3—Quantitative non-randomised	3/5
10.	Eng, Tsegai-Moore & Fisher [78]	3—Quantitative non-randomised	4/5
11.	Zourmpakis et al. [45]	4—Quantitative descriptive	2/5
12.	Zourmpakis et al. [43]	3—Quantitative non-randomised	3/5
13.	Wang [41]	2—Quantitative RCT	4/5
14.	Feng et al. [79]	5—Mixed methods	4/5
15.	Zidianakis et al. [80]	5—Mixed methods	0/5
16.	Jagust et al. [20]	3—Quantitative non-randomised	3/5
17.	Sayed et al. [3]	3—Quantitative non-randomised	3/5
18.	Chu et al. [61]	3—Quantitative non-randomised	4/5
19.	Kaushalya et al. [60]	3—Quantitative non-randomised	3/5

). The studies were then appraised using the five criteria for the relevant category. All studies passed both initial screening questions. In the cases of Vanbecelaere et al. [77] and Hooshyar et al. [40], the classification was conservative, as the former used class-level/cluster assignment and the latter reported random assignment without describing the procedure in sufficient detail. For this reason, both were assessed under Category 3 (Quantitative non-randomised). Some studies provided limited or unclear information in areas such as sampling, measurement validation, attrition, and baseline comparability. This also applied to Zidianakis et al. [80], which met none of the five criteria in its category, reflecting limited methodological reporting rather than evidence against its findings. However, the number of criteria met was not used as an exclusion criterion. In line with MMAT guidance, these counts are reported descriptively and should not be interpreted as overall quality scores or used to rank the studies.

Due to the heterogeneity of the studies, a meta-analysis was not feasible, and a narrative synthesis was performed, with the results tabulated by research question. Learning and motivational outcomes were summarised as reported by the original authors, including statistical significance and effect sizes where available, while no sensitivity analyses were conducted. Similarly, no formal assessment of reporting bias or certainty of evidence was performed. Regarding the potential of publication bias, it is thoroughly discussed in the limitation section later.

5. Findings

5.1. Methodology and Assessment Tools

The first research question sought to identify the methodological approaches and assessment instruments employed in studies investigating adaptive gamification in preschool and early childhood education. The 19 included studies exhibited considerable methodological diversity, reflecting the multidisciplinary nature of this emerging field (

Table 5. Methodology and data collection instrument in studies.

No.	Author (s)	Method	Data Collection Instrument
1.	Thai et al. [66]	Controlled trials/Quantitative	Test evaluations (TEMA-3), in-game logs, and teacher surveys
2.	Vanbecelaere et al. [77]	Quasi-experimental designs with control groups/Quantitative	Test evaluations, motivation questionnaire, Self-Concept of Ability scale, and log-data
3.	Debeer et al. [24]	Controlled trials/Quantitative	Log-data (item-wise accuracy and timestamps)
4.	Vanbecelaere et al. [63]	Controlled trials/Quantitative	Test evaluations (near/far transfer tasks), math anxiety questionnaire, and log-data
5.	Agudo et al. [32]	Mixed methods	Questionnaires (Haugland Developmental Software Scale) and observation
6.	Bang, Li & Flynn [36]	M ixed methods	Test evaluations (CCSS-M aligned), in-game data, teacher surveys, teacher interviews, and classroom observations
7.	Wei, She & Du [64]	Quantitative	Test evaluations (pre/post), engagement metrics, and satisfaction questionnaire
8.	Junruang & Kanjug [26]	Quasi-experimental designs without control conditions/Mixed methods	Test evaluations (CT and CrT assessments) and semi-structured interviews
9.	Hooshyar et al. [40]	Quasi-experimental designs with control groups/Quantitative	In-game performance data (performance gain formula)
10.	Eng, Tsegai-Moore & Fisher [78]	Quantitative	Test evaluations (Flanker Task, WPPSI-P), gamified assessment, and Smileyometer
11.	Zourmpakis et al. [45]	Quasi-experimental designs without control conditions/Quantitative	Questionnaires (75-item custom Likert scale)
12.	Zourmpakis et al. [43]	Quasi-experimental designs with control groups/Quantitative	Test evaluations (custom academic achievement test, aligned with Bloom’s taxonomy)
13.	Wang [41]	Controlled trials/Quantitative	Test evaluations (EMAS), engagement scale (inCLASS), attention scale (CBQ-VSF), system logs, and facial affect data (OpenFace 2.0)
14.	Feng et al. [79]	Controlled trials/Mixed methods	Test evaluations (TEMA-3), in-game usage data, teacher interviews, classroom observations, and teacher logs
15.	Zidianakis et al. [80]	Mixed methods	Questionnaires (usability factors), professional qualitative feedback, and session recordings
16.	Jagust et al. [20]	Quasi-experimental designs with control groups/Quantitative	Game records (usage logs) and focus group interviews
17.	Sayed et al. [3]	Quasi-experimental designs without control conditions/Quantitative	Test evaluations (pre/post), exercise log data, and satisfaction survey
18.	Chu et al. [61]	Quasi-experimental designs with control groups/Quantitative	Test evaluations (pre/post), learning attitude scale, self-efficacy scale, cognitive load scale, and math anxiety scale
19.	Kaushalya et al. [60]	Quasi-experimental designs with control groups/Quantitative	Test evaluations (pre/post), usability scale (SUS), mood recognition (F1 score), and teacher feedback forms

).

Based on the above table, six studies adopted quasi-experimental designs with control groups [20,40,43,60,61,77], five comprised randomised controlled trials (RCTs), including both cluster and individual randomisation [24,41,63,66,79], while three employed quasi-experimental designs without control conditions [3,26,45]. Four studies used mixed-methods approaches [26,32,36,80], one adopted feasibility or within-subjects designs [78] and one study presented a computational AI framework with experimental validation [64]. Based on these remarks, the majority of studies follow an experimental and quasi-experimental design (14 out of 19 studies), reflecting a growing level of methodological sophistication within the field, though the relatively small number of RCTs indicates a continued need to strengthen the current body of research.

Sample sizes showed variance, ranging from 14 participants [80] to 886 [36]. Most studies (11 out of 19) used samples of fewer than 100 participants, which might raise concerns about reliability of the data and the generalisability of findings.

Standardised and researcher-developed measures were used alongside each other across the corpus. The Test of Early Mathematics Ability (TEMA-3) was the most frequently used standardised instrument, with three studies making use of it [36,66,79]. Some of the studies that were included used validated scales for affective outcomes, including the Self-Concept of Ability scale [77], the Child Behavior Questionnaire-Very Short Form (CBQ-VSF) [41], and the inCLASS observational measure [41,60]. Most studies, however, relied on researcher-developed instruments tailored to their specific contexts, which limits cross-study comparability.

Some form of system-generated data, such as learning analytics dashboards, item-response logs, time-on-task metrics, and performance trajectories, were incorporated by 17 out of the 19 studies. The use and support of in-game data reflects an inherent advantage of digital adaptive systems, i.e., the ability to capture fine-grained learning process data that pre–post assessment designs alone cannot provide.

To sum up, it is evident that experimental and quasi-experimental designs dominate (14 of 19 studies), which points to growing rigour, yet only five studies are randomised controlled trials and eleven use samples of fewer than 100 children. This shows that many findings come from small and context-specific evidence. In addition, there seems to be a pattern regarding the reliance on researcher-developed instruments and platforms, with only a few validated measures used across studies, which can limit direct comparison of results. What is more, the use of system-generated process data seems to become a trend as 17 of the 19 studies seem to utilize it as it can let adaptive research observe learning as it unfolds rather than only through pre–post testing. Research, methodologically, seems quite promising and increasingly sophisticated, but still constrained in reliability and cross-study comparability by small samples and non-standardised measures.

5.2. Content Area, Educational Levels, and Educational Context

The second research question examined the content areas, educational levels, and broader educational contexts addressed by adaptive gamification interventions in early childhood. Analysis of the 19 studies revealed distinct patterns in thematic focus, geographic distribution, and platform characteristics (

Table 6. Content area, educational levels, and educational context in studies.

Author (s)	Content Area	Educational Level	Age
Thai et al. [66]	Mathematics	TK/Kindergarten	4–6
Vanbecelaere et al. [77]	Literacy/Reading	Kindergarten	5–6
Debeer et al. [24]	Mathematics	Grade 1	6–7
Vanbecelaere et al. [63]	Mathematics	Grade 1	6–7
Agudo et al. [32]	EFL	Preschool	3–5
Bang et al. [36]	Mathematics	KG—Grade 1	5–7
Wei et al. [64]	Multiple	Preschool	3–6
Junruang & Kanjug [26]	CT + Creative Thinking	KG Year 2	5–6
Hooshyar et al. [40]	Early English (EFL)	Preschool	5–6
Eng et al. [78]	Executive Function	Preschool	3–5
Zourmpakis et al. [45]	Science	Grade 3	8–9
Zourmpakis et al. [43]	Science	Grade 3	8–9
Wang [41]	Mathematics	Preschool	4–6
Feng et al. [79]	Mathematics	Kindergarten	5–7
Zidianakis et al. [80]	Play-based developmental	Preschool	3–6
Jagust et al. [20]	Mathematics	Grades 2–3	7–8
Sayed et al. [3]	Mathematics	Grade 3	9–10 (Age range reported by the original study. The study was retained because participants were enrolled in Grade 3, which met the educational-level criterion)
Chu et al. [61]	Mathematics	Grade 3	9
Kaushalya et al. [60]	Multiple	Kindergarten	4–6

).

Mathematics was the dominant subject, highlighted in 11 studies (58%) [3,20,24,36,41,60,61,63,64,66,79]. Literacy and reading-related content were the second most popular subject, appearing in three studies (16%) [32,40,77], while science education was addressed in two studies [43,45]. Executive function [78], computational and creative thinking [26], and play-based developmental skills [80] appeared in a single study, while two studies addressed multiple content domains [60,64]. The concentration on mathematics is consistent with the broader educational technology literature, where sequential, structured domains lend themselves well to adaptive algorithms, while science, social studies, and creative arts remain underrepresented.

As shown in Table 6, ten studies (53%) targeted preschool or kindergarten children aged 3–6 years [26,32,36,40,41,60,64,66,77,78]. The remaining studies focused on early primary education: two addressed Grade 1 (ages 6–7) [24,63], and five addressed Grades 2–3 (ages 7–9) [3,20,43,45,61]. Sayed et al. [3] reported their participants as Grade 3 students aged 9–10 years. This created an ambiguity because the reported age range extended beyond that generally associated with the review’s target population. However, eligibility was determined primarily by educational level and, where necessary, by age. Consequently, the study was retained on the basis of its Grade 3 classification.

Two studies included mixed or multiple educational levels [79,80]. The concentration on the preschool–kindergarten age band is especially worth noting, as adaptive systems for children aged 3–6 face distinct design challenges related to limited reading ability, short attention spans, and developing fine motor skills.

The geographic distribution was predominantly European and North American (12 out of 19) (

Table 7. Studies origin, Platform and population.

Author (s)	Country	Continent	Platform	Population
Thai et al. [66]	USA	North America	Commercial	Neurotypical
Vanbecelaere et al. [77]	Belgium	Europe	Self-developed	Neurotypical
Debeer et al. [24]	Belgium	Europe	Self-developed	Neurotypical
Vanbecelaere et al. [63]	Belgium	Europe	Self-developed	Neurotypical
Agudo et al. [32]	Spain	Europe	Self-developed	Neurotypical
Bang et al. [36]	USA	North America	Commercial	Neurotypical
Wei et al. [64]	China	Asia	Self-developed	Neurotypical
Junruang & Kanjug [26]	Thailand	Asia	Self-developed	Neurotypical
Hooshyar et al. [40]	Korea	Asia	Self-developed	Neurotypical
Eng et al. [78]	USA	North America	Self-developed	Neurotypical
Zourmpakis et al. [45]	Greece	Europe	Self-developed	Neurotypical
Zourmpakis et al. [43]	Greece	Europe	Self-developed	Neurotypical
Wang [41]	China	Asia	Self-developed	Neurotypical
Feng et al. [79]	USA	North America	Commercial	Neurotypical
Zidianakis et al. [80]	Greece	Europe	Self-developed	Neurotypical (+1 LD)
Jagust et al. [20]	Croatia	Europe	Self-developed	Neurotypical
Sayed et al. [3]	Egypt	Africa	Self-developed	Neurotypical
Chu et al. [61]	Taiwan	Asia	Self-developed	Neurotypical
Kaushalya et al. [60]	Sri Lanka	Asia	Self-developed	Neurotypical

). Eight studies originated from Europe, with three being from Belgium [24,63,77], three from Greece [43,45,80], one from Spain [32], and one from Croatia [20]. Four studies were conducted in North America, all in the United States [36,66,78,79]. Six came from Asia, specifically from China [41,64], Korea [40], Thailand [26], Taiwan [61], and Sri Lanka [60]. Finally, one study was conducted in Africa [3]. Unfortunately, no studies from South America or Oceania were noted.

An interesting finding concerned the prevalence of self-developed platforms (Table 7). Sixteen of the 19 studies (84%) employed researcher-designed platforms, while only three studies [36,66,79] utilised a commercial product, which, in all cases, was the same application (My Math Academy by Age of Learning). All 19 platforms were digital, with only one study [80] incorporating tangible elements within an ambient intelligence environment. This near-universal reliance on self-developed tools, although raising questions regarding scalability and sustainability, also demonstrates how much easier it has been to develop such complex technological environments.

Regarding participant populations in Table 7, all 19 studies focused on neurotypical children, with only one [80] including a single child with learning difficulties in its sample [80]. This distinct focus on neurotypical populations represents a significant limitation, as it fails to take into account the diverse learning needs of children with developmental variations.

Consequently, in relation to the second research question, the evidence is concentrated rather than broad. Mathematics was addressed in more than half of the studies, whereas literacy, science, executive function, and computational or creative thinking appeared in only one to three studies each. Social and emotional learning was not represented in the reviewed literature on adaptive gamification and game-based learning in preschool and early primary education. Regarding context, two-thirds of the studies originated from Europe and North America, 84% relied on researcher-developed platforms, and, as previously explained, all samples consisted mainly of neurotypical children. The evidence therefore supports stronger conclusions for mathematics-focused interventions, preschool and early-primary settings, and mostly neurotypical learners in European and North American contexts. However, these conclusions should be generalised to other content areas, learner populations, or cultural settings with caution.

5.3. Theories Underpinning Adaptive Gamification and Game Elements

The third research question investigated the theoretical frameworks, adaptive mechanisms, and game elements employed across the reviewed studies (

Table 8. Theories, game elements, learning results, and motivational outcomes.

Author (s)	Theories	Game Elements
Thai et al. [66]	Vygotsky’s Zone of Proximal Development (ZPD) Bloom’s Mastery Learning	Levels, boss levels, scores, feedback, narrative, rewards
Vanbecelaere et al. [77]	Not explicitly stated; draws on Digital Game-Based Learning (DGBL) principles	Levels, feedback, adaptive practice, narrative
Debeer et al. [24]	Learning Efficiency framework; Item Response Theory (IRT)	Levels, progression, feedback, narrative
Vanbecelaere et al. [63]	Self-Determination Theory (SDT); Digital Game-Based Learning (DGBL)	Levels, adaptive practice, feedback, narrative
Agudo et al. [32]	Adaptive Hypermedia Theory Discovery Learning (implicit)	Exercises, feedback, audio, animation
Bang et al. [36]	Bloom’s Taxonomy/Mastery Learning Zone of Proximal Development (ZPD) Evidence-Centered Design (ECD)	Narratives, progression, feedback, 130+ activities
Wei et al. [64]	AI/ML-driven personalisation	Gamified units, challenges, progression
Junruang & Kanjug [26]	Constructivism (Piaget, Vygotsky); Zone of Proximal Development (ZPD)	Scores, badges, task challenges
Hooshyar et al. [40]	Flow Theory (Csikszentmihalyi); Gee’s Principles of Game-Based Learning	Letter/phonological games, scoring
Eng et al. [78]	Vygotsky’s Zone of Proximal Development (ZPD)	Narrative, rewards, competitor, music
Zourmpakis et al. [45]	Self-Determination Theory (SDT); Hexad Player Typology (Marczewski)	11 elements: badges, currency, storytelling, etc.
Zourmpakis et al. [43]	Self-Determination Theory (SDT); Hexad Player Typology	Same 11 elements; female role models
Wang [41]	Zone of Proximal Development (ZPD); Cognitive Load Theory (CLT)	Interactive tasks, AV feedback, replay
Feng et al. [79]	Mastery-based Learning; Evidence-Centered Design (ECD); Game-Based Learning (GBL) theory	31 games, 300+ activities, dashboard
Zidianakis et al. [80]	Vygotsky’s Theory of Play	Game levels, puzzles, 3D avatar, sounds
Jagust et al. [20]	Flow Theory	Points, narrative, competition, time, leaderboard
Sayed et al. [3]	Bloom’s Taxonomy; Mastery Learning; VARK Learning Styles Model	Market, points, progress bar, quizzes
Chu et al. [61]	Concept-Effect Relationship (CER) Theory; Constructivism	Narrative, levels, points, feedback, rewards
Kaushalya et al. [60]	Constructivism (affordance-based teaching); Self-Determination Theory (SDT)	Avatar, medals, badges, mood music

). This analysis revealed both encouraging theoretical diversity and concerning gaps in conceptual grounding.

Vygotsky’s Zone of Proximal Development (ZPD) and scaffolding theory was the framework that appeared to be used the most, as it was referenced explicitly or implicitly in six studies [26,36,41,66,78,80]. SDT was the second most common theory influencing the frameworks [43,45,60,63], while flow, another motivational theory, was used in two studies [20,40]. However, it should be mentioned that five studies followed general constructivist perspectives (Piaget, Vygotsky, Bruner) [26,32,60,61,80]. Moreover, four studies also included Bloom’s Mastery Learning [3,36,66,79] with an Evidence-Centred Design being applied in two of them [36,79]. Three studies stated no explicit theoretical framework [24,64,77], which makes it difficult to assess the design rationale and intended mechanisms of effect.

The adaptive mechanisms across studies (Table 8) were classified into 10 categories. Mastery-based dynamic difficulty adjustment (DDA) and adaptive learning trajectories were the most common as they were included in five studies [26,32,36,66,79]. Item Response Theory (IRT)-based approaches appeared in three studies [24,63,77], while rule-based DDA with performance thresholds [20,78], Hexad player-type adaptation [43,45], Deep Reinforcement Learning approaches [3,41] were each used in two studies. Consequently, five studies, i.e., Procedural Content Generation via Genetic Algorithm [40], Graph Neural Network with multi-objective optimisation [64], CER-based diagnostic and remedial adaptation [61], occupational therapy developmental profiling [80], and reverse-simplification DDA with mood-based adaptation [60], utilised unique adaptive mechanisms that were not replicated in any other research within the sample. Studies published from 2024 onwards increasingly use AI and machine learning techniques, moving away from simple rule-based systems toward multimodal adaptive architectures.

The analysis of game element adaptation revealed three levels (Table 8). Four studies implemented multidimensional adaptation, adjusting the game elements themselves to learner characteristics, and not just content difficulty [32,43,45,80]. From these studies, only two studies [43,45], both based on the same platform/environment and conducted by the same researchers, provide examples of player-type-based adaptation, using the Hexad framework to personalise 11 game elements (like badges, currency, storytelling, promotion, cooperation, challenges, etc.) according to individual player profiles. Eight studies adapted content difficulty or learning sequencing within a fixed game framework [24,26,36,40,63,64,66,79]. Six implemented partial adaptation, adjusting task difficulty based on performance while the broader gamification framework remained fixed [3,41,60,61,77,78]. One study adapted a specific game mechanic, i.e., time pressure, in real time [20]. Adaptation efforts have therefore concentrated mostly on adjusting content difficulty within fixed game structures, while the gamification elements themselves are rarely adapted.

To sum up, most studies in this SLR are explicit regarding the theories they are based on, led by the Zone of Proximal Development (six studies), Self-Determination Theory (four), mastery learning (four), and broad constructivist perspectives (five). However, a few (three) are not specific and most of them do not clearly explain how the chosen theory shaped the adaptive design. The adaptation mechanisms are similarly diverse, spanning ten categories, with mastery-based dynamic difficulty adjustment the most common, but because most mechanisms share little common ground, it is rather difficult to compare. Adaptation is overwhelmingly applied to content difficulty and sequencing within a game structure, while only four studies adapt the game elements themselves and only two use player-type adaptation. However, it was observed that studies from 2024 onwards are moving from simple rule-based systems toward AI, machine-learning, and multimodal adaptive architectures.

5.4. Adaptive Gamification’s Learning and Motivational Outcomes

The educational and motivational outcomes of adaptive gamification interventions were examined in order to answer the fourth research question (

Table 9. Learning results and motivational outcomes.

Author (s)	Learning Results	Motivational Outcomes
Thai et al. [66]	The experimental group had significantly higher learning results than the control group. Students with moderate prior knowledge demonstrated the highest learning gains	Positive interest in mathematics and increased confidence were reported. Higher engagement with the app was positively associated with greater learning gains.
Vanbecelaere et al. [77]	Both conditions showed significant improvement in auditory memory, blending, and letter knowledge. No significant learning advantage was found for the adaptive game-based learning environment over the non-adaptive environment. Students in the adaptive environment reached higher game progression levels.	No significant difference in motivational outcomes was found between the adaptive and non-adaptive environments.
Debeer et al. [24]	No significant learning advantage for the adaptive environment over the non-adaptive. Significant improvements in early numeracy skills were observed in both the adaptive and non-adaptive conditions. In the adaptive setting, students achieved comparable learning outcomes with greater efficiency (fewer items required).	Motivational outcomes were not measured.
Vanbecelaere et al. [63]	Both adaptive and non-adaptive learning groups showed significant improvement in numeracy skills and math-related outcomes. The adaptive group achieved comparable learning outcomes in significantly less time. Math anxiety was significantly reduced in both groups following the intervention.	Math anxiety decreased significantly in both groups following the intervention. No differential motivational effect was found between the adaptive and non-adaptive groups.
Agudo et al. [32]	Sentence-level tasks proved too challenging for the target age group. Learning outcomes were more limited for 3-year-olds; older children demonstrated greater language acquisition.	Intrinsic motivation was rated positively by teachers Children’s engagement and interest increased with age across the 3–5 years range. Adequate adult supervision remained necessary to sustain engagement, particularly for the youngest students.
Bang et al. [36]	Significant enhancement in early mathematics skills. Kindergarten students showed greater learning gains than first-grade students Treatment students were more likely to master advanced subtraction skills	Positive engagement and interest in mathematics were reported. High levels of sustained engagement were observed.
Wei et al. [64]	Post-test scores were significantly higher than pre-test scores across all participants. Engagement showed a moderate positive correlation with learning improvement. Error rates decreased following adaptive difficulty adjustments.	Higher satisfaction and engagement were reported in the adaptive condition. Sustained engagement was observed throughout the eight-week intervention period.
Junruang & Kanjug [26]	Strong positive correlation between computational thinking and creative thinking skills. The gamified personalised environment supported simultaneous cognitive and creative development. Computational thinking explained more than half of the variance in creative thinking outcomes.	Engagement was not formally measured. Active participation was noted qualitatively throughout the gamified activities. Students demonstrated curiosity and enthusiasm during computational thinking tasks.
Hooshyar et al. [40]	Customized adaptive scenarios led to significantly higher performance gains than uncustomized scenarios. Lower-proficiency students benefited most from the adaptive content personalization.	Motivational outcomes were not measured.
Eng et al. [78]	The gamified adaptive environment maintained construct validity with traditional measures Children exhibited higher accuracy in the adaptive environment 3-year-olds averaged significantly fewer task disengagement incidents	Children found the adaptive gamified environment significantly more enjoyable than traditional assessment tasks. The adaptive environment sustained attention and participation more effectively across all age groups.
Zourmpakis et al. [45]	Learning outcomes were not assessed.	Highly positive effect on student motivation and engagement in the adaptive gamification environment Profile-based adaptation significantly enhanced student engagement with science content. Some game elements (e.g., badges, in-game currency) induced performance-related stress in a significant part of the students (more than 1/3 of them). Cooperation-based elements had the highest positive impact (more than 4/10).
Zourmpakis et al. [43]	Significant improvement in science learning outcomes for the experimental group. Experimental group significantly outperformed the control group on post-test scores. The adaptive gamification environment helped both genders perform at the same level, whereas the standard group showed a clear difference between them.	Motivational outcomes were not assessed.
Wang [41]	Students that used the adaptive GBL environment demonstrated significantly greater mathematics learning gains. Higher task accuracy and faster response times were observed in adaptive group. A greater proportion of the students in the adaptive environment reached the highest mastery level.	Students in the adaptive group displayed higher enjoyment. Adaptive learning students engaged in more voluntary replays, indicating greater intrinsic motivation. Significantly fewer dropout incidents were recorded among students who used the adaptive environment.
Feng et al. [79]	Experimental group outperformed the control group, but the difference was not statistically significant. Strong positive correlations between engagement metrics and in-game learning outcomes.	Positive engagement with the program was consistently reported. Higher engagement with the adaptive environment was associated with greater in-program learning gains.
Zidianakis et al. [80]	No comparative learning outcomes were measured. Positive satisfaction with the game’s adaptive behaviour was reported by all stakeholder groups.	Children, parents, and professionals reported overall satisfaction with the adaptive game. Positive emotional engagement was observed during gameplay. Minor concerns raised about avatar responsiveness.
Jagust et al. [20]	Adaptive gamification outperformed the non-gamified on the measured performance Learning outcomes varied significantly depending on gamification type and classroom context. No single gamification approach was universally effective across all classes.	Adaptive gamification produced the highest task engagement among all conditions. Mixed motivational experiences were reported. Competitive elements induced stress in some students.
Sayed et al. [3]	Significant improvements in post-test scores were observed in both adaptive and non-adaptive gamification courses. Learning efficiency improved substantially in both groups Students in the adaptive gamification environment slightly outperformed participants in the non-adaptive gamification course in learning outcomes.	High satisfaction rates were reported in both groups Students in the adaptive gamification environment reported slightly higher satisfaction than those in the non-adaptive gamification course.
Chu et al. [61]	Significant improvement in mathematics learning achievement in the adaptive GBL application. Significant improvement in self-efficacy toward mathematics in the adaptive GBL application. The experimental group showed significantly reduced cognitive mental load compared to the control group.	Significant improvement in learning attitudes toward mathematics in the experimental group. Students reported a positive disposition toward the adaptive game-based learning system.
Kaushalya et al. [60]	Experimental group showed a 25% improvement in problem-solving scores, compared to 10% in the control group. High system usability supported effective and sustained learning engagement in the students in the adaptive GBL environment.	High student engagement was reported 90% of teachers rated the adaptive GBL platform as a valuable supplementary tool.

). Eighteen of these studies investigated either learning results or motivational outcomes. One study [80] focused on usability and adaptive feedback rather than comparative learning outcomes but reported satisfaction and engagement data, which are associated motivational affordances.

Based on Table 9, nine studies displayed statistically significant positive effects on learning results [26,36,40,41,43,61,64,66,78]. Effect sizes, where reported, ranged from small [36] to very significant [41], with most falling in the small-to-medium range. However, four additional studies reported positive outcomes based on descriptive statistics without inferential testing (such as t-tests) [32,60,79,80]. Two found positive effects specifically for learning efficiency. In both of these cases, adaptive conditions achieved equivalent outcomes in less time, even though they did not show more achievement gains [24,63].

However, the findings in Table 9 are not uniformly positive. One study found no significant difference between adaptive and non-adaptive conditions on cognitive outcomes [77], while two reported mixed results depending on the outcome measure or moderating variables [20,63]. Moreover, in some studies, researchers discovered that adaptive systems may confer their primary advantage through efficiency rather than effectiveness [24,63]. This means that the adaptive environments helped students to reach the learning achievements in less time (efficient) rather than improving that learning gains (effectiveness). This finding points to some direct implications for classroom implementation.

Prior knowledge was the most frequently identified moderating variable, reported in six studies [3,36,40,63,66,79]. The general pattern that was noted was that students with lower prior knowledge benefited more from adaptive gamification or GBL environments. This is consistent with the theoretical principle that adaptive systems are most valuable when they calibrate challenge to the learner’s current developmental level. However, in one of the studies [63] a more elaborate distinction was shown, where high-prior-knowledge learners benefited more from the adaptive environment, while low-prior-knowledge learners showed greater benefits under non-adaptive learning course. Consequently, it is clear that the relationship between prior knowledge and adaptive system effectiveness is not straightforward currently.

Age and developmental stage moderated outcomes in four studies [32,64,78,80], with children aged 3–4 generally needing more scaffolding and adult support to benefit from adaptive systems. Zourmpakis et al. [43] found that adaptive gamification eliminated the gender gap in science learning that persisted under traditional inquiry-based instruction. It is not clear how this was achieved, with the authors listing the possibility of the integration of positive female role models within the adaptive narrative playing some part.

Motivational and affective outcomes were assessed in fewer studies (16 out of 19) and with less standardised instruments. Where measured, engagement and enjoyment were generally higher in adaptive conditions, but there were important exceptions and conditions to consider. For example, in Zourmpakis et al. [45], badges, in-game currency, and cooperation-based elements induced performance-related stress in a considerable share of the students, while in Jagust et al. [20] competitive elements induced stress in some children.

In Chu et al. [61], students in the experimental group displayed significant improvement in learning attitudes toward mathematics in the experimental group, along with low levels of anxiety, although low anxiety was evident in both the experimental and control groups. This is an encouraging finding for adaptive game-based learning with young learners. In another study [45], in which game elements were analysed in greater detail, students reported highly positive attitudes towards learning and enjoyment while using the application, 92.5% and 91.1% respectively. However, specific game elements, including cooperation-related elements, badges, and in-game currency, generated substantial stress responses among some students. These findings indicate that motivational effects are not uniform across game elements and that particular adaptive design features may unintentionally increase affective load, even within well-structured environments. Similarly, Jagušt et al. [20] found that, although students in the adaptive gamification environment showed significantly higher engagement and outperformed participants in the control group in learning performance, they also made more errors, and some students displayed anxiety. Consequently, both studies highlight the need for careful calibration of challenge.

Several studies that investigated both learning outcomes and engagement reported strong associations between them. Students who engaged more with the adaptive learning environment also achieved stronger learning outcomes [3,41,66,78,79]. This suggests that the motivational and cognitive benefits of adaptive gamification may not be independent, as sustained engagement could be a mechanism through which adaptive design translates into improved learning outcomes. In some cases, greater engagement was also associated with stronger affective outcomes, as reported by Wang [41] and Eng et al. [78]. In Wang [41], these positive findings were accompanied by significant reductions in disengagement and off-task behaviour, indicating a broader motivational benefit beyond enjoyment alone.

Overall, learning and motivational evidence is cautiously positive but not entirely. Nine studies reported statistically significant learning gains and a further four reported positive results from descriptive data only. However, a considerable number found no advantage for the adaptive version, except for improved efficiency. Motivation outcomes are more favourable in adaptive systems, as sixteen studies showed higher results. Yet, the picture is not entirely clear as particular game elements, such as competition or badges, appeared to produce stress for some children. Prior knowledge and developmental stage seem to directly affect these outcomes, with lower-prior-knowledge and older children tending to benefit most. Consequently, adaptive gamification can possibly support learning, efficiency, and motivation for young children, but the small samples and short durations of the current evidence mean these benefits should be read as promising rather than firmly established, with more evidence needed regarding learning acquisition.

6. Discussion

The present systematic literature review aimed to map the current state of adaptive gamification and adaptive game-based learning in preschool and early childhood education. More specifically, it examined how these interventions have been deployed and studied, which content areas and educational contexts have received most attention, what theoretical frameworks and adaptive mechanisms have been used, and what kinds of learning and motivational outcomes have been reported. Overall, the 19 included empirical studies suggest that adaptive gamification and adaptive GBL can be a promising direction for young learners. At the same time, the evidence does not yet form a fully coherent or mature field. It is still characterised by methodological variety, uneven theoretical grounding, and several practical questions that remain open.

6.1. Key Findings and Implications

Regarding the first research question, which concerned methodology and assessment tools, the reviewed studies showed considerable methodological variety. Experimental and quasi-experimental designs were present in a large part of the identified studies (14 out of 19), which indicates that researchers are not relying only on descriptive or exploratory accounts. However, this should not be interpreted as evidence that the field has already reached methodological maturity. Several studies still depended on small samples, short implementation periods, researcher-developed instruments, or feasibility-oriented designs, and five studies used randomised controlled trials, with only one however from them incorporating mixed-methods components. That combination is also familiar in the wider adaptive and tailored gamification literature: promising designs are often tested alongside narrow samples, brief interventions, and a still limited accumulated evidence base [16,17].

The sample sizes give a clear example of this problem. Eleven of the 19 studies included fewer than 100 participants, while the whole range extended from very small feasibility or usability studies to one large-scale trial. In early childhood research, this is partly understandable, because recruiting young children, obtaining permissions, and implementing interventions in real classroom settings are not simple procedures. Even so, small samples restrict the level of generalization that can be made. Thus, the present evidence can support careful interpretation, but it is not yet strong enough to produce broad claims for all preschool and early primary contexts.

The assessment tools used across the studies also revealed a useful tension. Some mathematics-focused studies used standardised instruments, such as the Test of Early Mathematics Ability, whereas others relied on structured questionnaires, observational measures, or tools developed by the researchers for the specific intervention. Standardised measures give researchers a shared reference point, which makes findings easier to compare across studies. At the same time, they are often too broad to catch what changes during play: a child may try a new strategy, slow down, ask for help differently, or interact with peers in a new way. Intervention-specific tools can record those details more closely, but they bring their own trade-off, since they are harder to compare across studies and may reflect the assumptions of the research team. This issue is especially relevant in game-based learning, where gains may appear in the learning process rather than only in broad test-score changes [77].

One of the strongest aspects of the reviewed studies was the extensive use of system-generated data. Seventeen studies incorporated some form of learning analytics, including item-response logs, time-on-task metrics, accuracy traces, performance trajectories, response latency, or affective indicators. This is important because adaptive systems do not only present learning tasks. They also record how children respond to difficulty, feedback, game elements, and pacing while the learning activity is taking place. In this sense, log data can show whether children are learning more efficiently, even when a simple pre–post comparison would not make this visible [24]. At the same time, these traces should not be treated as self-evident proof of learning. For log data to carry real evidential weight, it has to be read against the intended learning goal; a score, click pattern, time measure, or engagement signal does not by itself show that learning has occurred [8].

Concerning the second research question, mathematics was the dominant content area, appearing in 11 out of the 19 studies (58%). That pattern is understandable. Early mathematics often breaks down into ordered skills, and those skills can be represented fairly easily as levels, mastery checks, or changes in difficulty. This structure also makes live adjustment easier: while a child is still playing, the system can use a recent answer, pause, or error to decide whether to offer a new prompt, repeat a concept, or move to another level [24,36,41,66]. The concentration on mathematics also shows the limits of the current evidence. Science education, literacy, executive function, computational thinking, creative thinking, and broader developmental skills have not yet received the same systematic attention.

This imbalance should not be treated as a minor detail. Broader early childhood GBL literature indicates that games can support more than early numeracy. Existing reviews report benefits for cognition, motivation, social interaction, emotional development, and engagement. Those findings, however, do not transfer automatically from one setting to the next. A design that supports one objective or age group may lose much of its value when children bring different prior knowledge, when the classroom routines differ, or when the task asks for a less structured type of thinking [14,33,82]. For this reason, the strong presence of mathematics in the present review can be read in two ways. It gives the field a relatively coherent evidence base, but it also leaves several important areas of early childhood learning underrepresented.

The even distribution between preschool-kindergarten children (ages 3–6) and early primary learners (ages 6–9) is also more than a demographic observation. Children in the younger age band usually have limited reading ability, shorter attention spans, developing fine motor skills, and a greater need for adult mediation. Studies involving very young preschoolers make this point concrete: autonomy, instructions, and interaction demands can become barriers in themselves, while developmentally tuned gamified assessment may reduce time-outs and help children stay with the activity [32,78]. Therefore, adaptation in early childhood cannot be reduced to the technical adjustment of difficulty. It also has to involve interface simplicity, feedback modality, adult support, emotional load, and the physical or motor demands of the learning environment.

The geographic distribution of the studies was mainly European and North American. The pattern was uneven: eight studies came from Europe, four from North America, six from Asia, and one from Africa, while no studies from South America or Oceania were identified. Combined with the near-exclusive focus on neurotypical children, that distribution limits how confidently the findings can be applied across cultures or to inclusive classrooms. None of the 19 studies focused specifically on children with identified special educational needs, and only one [80] involved a single child with learning difficulties. This is a significant gap, because adaptive systems are often promoted as a way to support children who do not follow one standard learning path. Research with children who have reading difficulties complicates that claim. Evidence from slightly older children with reading difficulties suggests that this promise is not straightforward [75], as instructional feedback can be experienced differently depending on children’s needs and prior support. A feedback cue that helps one child notice an error may impose additional processing demands on another child, especially when the task is demanding or the feedback format is hard to interpret [75]. However, as no studies were identified for the specific educational level and age group, more research with diverse learner populations is needed, not only to support inclusion but also to understand how adaptivity operates when learner variability is more visible.

The strong reliance on researcher-developed platforms (16 out of 19) is another finding with two sides. On the positive side, it shows that researchers are increasingly able to design complex adaptive systems for young learners, something that was considerably more difficult only a few years ago. On the other side, it creates questions about scalability, sustainability, classroom adoption, and long-term maintenance. Only three studies used a commercial product, in all cases My Math Academy by Age of Learning. This is consistent with concerns from the broader game-based assessment literature, where many educational game tools are difficult to access, reuse, or replicate [8]. For this reason, future work needs to examine not only whether an intervention can work under research conditions, but also whether it can be maintained and used in ordinary classrooms.

The third research question focused on theoretical frameworks, adaptive mechanisms, and game elements. The theoretical grounding across the reviewed studies was useful but uneven. Vygotsky’s Zone of Proximal Development and scaffolding theory was the most frequently used framework, which is expected, since adaptive learning is closely connected with matching instruction to the learner’s current level. Self-Determination Theory was also present, especially where motivation and game element design were central. Flow theory, mastery learning, constructivism, and evidence-centered design appeared in more specific cases. However, three studies did not state an explicit theoretical framework, and several others referred to theory without clearly showing how it shaped the adaptive mechanism.

This lack of explicit theoretical grounding is not a small issue. When theory is only loosely connected to design, it becomes hard to know why a system succeeded, why it failed, or whether the same approach could transfer to another content area. Reviews of tailored and adaptive gamification raise much the same problem. They describe a number of systems that appear to have been built around local design decisions, with limited follow-up evidence about whether any benefits survive after the initial intervention period [15,16,17]. The field therefore needs to spell out more clearly how theory, learner modelling, adaptive mechanisms, game elements, and outcomes fit together.

The adaptive mechanisms were classified into 10 categories, from mastery-based dynamic difficulty adjustment to newer AI and machine learning approaches such as deep reinforcement learning, graph neural networks, procedural content generation, and mood-based adaptation. The later studies in the corpus, especially those from 2024 onward, show a visible shift toward AI and machine learning: instead of relying only on preset rules, several systems attempt to model the learner and adjust content through more complex computational architectures. This development is important, but it should be interpreted cautiously. Many of these systems remain short-term, technically complex, sample-limited, or tied to a very specific context. The broader AI-supported gamification literature also indicates that early childhood still receives much less attention than higher education or general school-level settings [4].

A central contribution of the present review is the distinction between adapting learning content and adapting game elements themselves. Most included studies adapted content difficulty, pacing, feedback, or learning sequence while keeping the broader game structure fixed. Some studies implemented partial adaptation, where task difficulty changed according to performance, but the gamification framework remained the same. By contrast, only a small set of studies adjusted game elements in response to learner characteristics, and adaptation based on player type was especially uncommon [43,45]. For that reason, a large share of the work labelled adaptive gamification in early childhood looks closer to adaptive learning wrapped in game features [36,63,65,83].

This distinction matters in practice as well as in theory, because game elements carry effects of their own. In a preschool classroom, a badge, timer, avatar, reward, or leaderboard is not just decoration; it changes what children attend to and how they feel while working. One child may treat a reward as a small invitation to keep trying; another may read it as pressure to hurry, beat peers, or collect points before thinking through the task [45,84,85]. The same design feature can feel playful, irrelevant, or stressful depending on the activity, the classroom climate, and the child’s earlier experiences with games or competition [22,46,86,87]. Research on intelligent game-based learning gives a similar warning: incentives, personalised agents, and navigation tools can make an activity easier to enter, but they can also become so visible that the learning goal moves into the background [88]. The included studies show this tension as well, with positive motivational responses sometimes appearing alongside stress linked to badges, currency, cooperation, or adaptive challenge [20,45]. Adaptive gamification therefore needs a more precise question than whether game elements are motivating. It needs to ask which element supports which child, in which classroom situation, and at which level of challenge [46,87].

Regarding the fourth research question, the learning and motivational outcomes were encouraging, but uneven. Most studies reported positive learning trends, whether through statistically significant gains, improved task accuracy, learning efficiency, or descriptive performance improvements. Where effect sizes were reported, they ranged from small to quite large. Still, these findings should not be overstated. Some studies were mainly feasibility, usability, or assessment-oriented studies rather than controlled intervention trials. For that reason, they should be interpreted as part of a cautiously positive evidence base, rather than as proof that adaptive gamification is already effective for all young learners [83].

The null and mixed findings are particularly useful for interpreting the field. In at least one carefully designed comparison, the adaptive version of a game did not clearly outperform the non-adaptive version on cognitive or affective outcomes, even though both groups improved over time [77]. Several explanations could account for this result: the training may have been too brief, the standardised tools may have been too blunt, or the algorithm may not have changed the learning path enough to matter. Even so, the result is a useful reminder that adaptivity is a design hypothesis, not a built-in advantage [83]. What matters is the object being adapted, the way the mechanism operates, whether children notice and understand the change, and whether the selected outcome measure is sensitive to that change [65,89].

Learning effectiveness and learning efficiency also need to be separated. In some studies, children in the adaptive condition finished with outcomes close to those of the comparison group, but they got there with less time or fewer unnecessary steps [24,63]. For classroom practice, that difference is not trivial. A system that reduces repeated attempts, waiting time, or unnecessary challenge may be valuable even when final scores look similar. Future studies should therefore examine score gains alongside pacing, cognitive load, and the amount of classroom time needed to reach a comparable learning outcome [36,90].

Prior knowledge was the most frequently identified moderating variable. Children with lower or moderate prior knowledge sometimes appeared to gain more from adaptive systems, especially when the challenge stayed near what they could manage with support. The pattern, however, does not support a simple rule in which less prior knowledge always means greater benefit. A child who lacks basic concepts may need modelling, shorter steps, or more explicit prompts. A child who already understands part of the material may need the opposite: fewer interruptions, less explanation, and a task that stretches existing knowledge. Feedback can therefore act as support, distraction, or overload depending on the child’s starting point [40,63,66]. Prior knowledge is better treated as part of a wider profile that includes feedback, scaffolding, task difficulty, and the specific adaptive algorithm [63].

The findings regarding gender in education are also worth mentioning, but with caution. The gender-related result is suggestive rather than settled. In one science education study, adaptive gamification appeared to reduce or remove gender differences in learning gains compared with traditional inquiry-based instruction. The authors link this result to adaptive narratives and positive role models, which may have made the science task feel more supportive for female students [43]. However, this remains a limited finding, and the mechanism is not yet clear. It should therefore be treated as a promising direction for future research rather than as a firm conclusion about gender equity in adaptive gamified environments.

Motivational and affective outcomes were assessed in 16 out of 19 studies, although the instruments used were less standardised than those used for cognitive outcomes. Most findings moved in a positive direction, with enjoyment, engagement, preference, satisfaction, persistence, or willingness to continue reported across several studies. In several cases, children who spent longer with the adaptive activity also advanced further, suggesting that persistence may be part of the learning pathway rather than only a separate motivational outcome [36,41,66,78]. Still, enjoyment and engagement should not be read as automatically positive. Children can be engaged and still experience stress, anxiety, cognitive overload, or reward-focused participation. Future work should therefore examine which game elements generate meaningful engagement and which ones may create affective load.

Across the four research questions, the findings suggest a field that is active and promising, but also still unsettled. The presence of game elements, personalisation, or AI-supported adaptation is not enough on its own to guarantee stronger learning or motivation. The value of an intervention depends on several interacting conditions: the granularity of the adaptive mechanism, the developmental appropriateness of the interface, the theoretical grounding of the game elements, the sensitivity of the assessment tools, the learner’s prior knowledge, and the classroom context. This reading is close to the broader gamification literature, where mixed results are often explained by how well the intervention is implemented, what motivational assumptions guide it, and whether the mechanics are actually tied to the learning objective [13,17,88].

These findings also point to some new perspectives regarding practice. For preschool educators, adaptive systems should not be seen as tools that aim to replace teacher judgement or role, but rather as tools that can help teachers notice where children need support [22]. This is extremely valuable since children’s prior knowledge and developmental stage seem to affect how adaptivity works [77]. Also, faster progress inside a system does not always lead to stronger final learning outcomes [24]. Therefore, teachers need information that is simple, understandable and usable, such as whether a child is progressing because their learning has truly improved, or their task sequence is more efficient, or they have simply remained engaged longer. This is vital in early childhood settings, where digital games should also have child-friendly interfaces, clear visual instructions, appropriate touch interaction, and, in many cases, adult mediation [33]. As such, the practical value of adaptive gamification and game-based learning in early childhood seems to depend not only on the algorithm itself, but also on whether teachers can interpret the adaptation and connect it with classroom decisions [91].

6.2. Limitations

The present systematic literature review contains certain limitations that should be acknowledged. First, it was restricted to English-language studies, and this may have excluded relevant work published in other languages. This point is important for early childhood education, because practices around play, technology use, and teacher mediation are often culturally and linguistically situated. Some approaches may therefore be present in local research traditions without appearing in the English-language literature. In addition, for the sources that returned very large result sets, screening was limited to the first 1000 entries ordered by relevance. However, relevance is affected by the platform and can change over time, which can affect the exact reproducibility of the search the longer the time passes. What is more, the search focused on studies published between 2016 and 2026 and used selected academic databases. This date range kept the review close to current work on adaptive gamification and AI-supported personalisation, but it may have missed older foundational studies or papers that described related ideas with different terminology [45].

Regarding the process of how the SLR was conducted, the present review indeed holds some limitations as the main screening, selection, and extraction process was initially done by a single researcher, potentially increasing the risk of false exclusions, extraction errors, or unconscious bias, which can be near 5% [74]. To reduce this restriction, a second researcher with experience in systematic literature reviews checked the search results, screening decisions, extracted data, and coding decisions afterward. However, this procedure is weaker than fully independent dual screening. Therefore, the findings should be interpreted with this methodological limitation in mind.

Moreover, the included studies were heterogeneous in design, population, duration, content area, adaptive mechanism, and outcome measures. Some compared adaptive and non-adaptive versions of the same game, others compared adaptive systems with traditional instruction, and others examined feasibility, usability, or engagement without a direct comparison condition. The MMAT appraisal identified recurring uncertainties concerning sampling procedures, measurement validity, attrition, baseline comparability, and the reporting of intervention procedures. Larger cluster-randomised studies tended to report clearer procedures, baseline checks, and retention, while smaller pilot, usability, or platform studies often provided less detail in these areas. This diversity reflects the innovative character of the field, but it also limits direct cross-study comparison and makes formal meta-analysis difficult. In addition, many studies used small samples and short intervention periods. In early childhood research, that constraint is understandable, but it still narrows generalisability and leaves open the question of whether reported benefits would last over time. Gamified interventions add another complication. Badges, rewards, or classroom routines may feel exciting during the first sessions and far less meaningful once children know what to expect [15]. The review also combined journal articles and conference papers, two formats that can differ in peer-review rigour and reporting detail. In some cases, theoretical frameworks, adaptive mechanisms, or assessment procedures were not described with enough detail in the original publications, making coding and interpretation more difficult. Therefore, the findings and conclusions of the present review should be read as a synthesis of the available evidence, showing promise but not as a definitive statement on the effectiveness of each adaptive approach.

Publication bias should also be considered. Because positive or statistically significant results are easier to publish and index, the available corpus may overstate the field’s level of success. The limited number of null or mixed findings in the final corpus may therefore say as much about what reaches publication as about how often adaptive gamification succeeds in practice [20,77].

Finally, the field is moving quickly. AI, deep reinforcement learning, mood recognition, and multimodal learner modelling are now entering this area, and they may change what counts as adaptive design in the next few years [92,93,94]. Some technologies included in this review may therefore date quickly, while studies published after the search period may introduce more advanced adaptive architectures [4,93,95,96]. Future reviews will need to return to the evidence base and examine whether these newer systems produce outcomes that are not only stronger, but also durable, inclusive, and workable with young children in real classrooms [14,93,94].

7. Conclusions

The present study provides an overview of adaptive gamification and adaptive game-based learning in preschool and early childhood education. The 19 empirical studies identified from 5069 initial records show a growing field, with evidence that adaptive systems can support young learners’ learning, engagement, and motivation. The stronger examples share several features: clear learning trajectories, timely feedback, and interaction demands that young children can manage. At the same time, several studies reported null or mixed effects, some did not measure learning outcomes, and many relied on small samples or short interventions. Even so, the evidence remains mixed. A system does not become effective simply because it includes game elements, personalisation, or AI supported adaptation; those features still need careful design, implementation, and testing [97,98].

Methodologically, the reviewed studies point in a constructive direction. More researchers are using experimental and quasi-experimental designs, and system-generated data can show parts of children’s learning process that traditional pre–post designs often miss. Even so, the evidence base still depends heavily on small samples, short intervention durations, researcher-developed instruments, and context-specific platforms. Stronger comparative designs, validated assessment instruments, and longer implementation periods are needed. It is also important to separate learning effectiveness, learning efficiency, usability, engagement, and motivation, since these outcomes do not always move in the same direction.

In relation to content areas, mathematics currently dominates the evidence base, probably because its sequential structure makes it easier to translate into adaptive difficulty sequences. This concentration has produced a clearer mathematics-focused evidence base, but other early childhood domains remain much less developed in the adaptive gamification literature, including science education, literacy, computational thinking, creative thinking, and social-emotional learning. Since early childhood GBL has already shown promise across several developmental domains, future studies should move into areas where learning is exploratory, open-ended, or difficult to represent as levels and mastery gates [14,82].

The findings also show that theory still has to do more work in this area. ZPD, SDT, flow theory, mastery learning, and constructivism all offer useful perspectives, but the reviewed studies do not always show how these perspectives shaped game element design, feedback, scaffolding, learner models, or assessment. In early childhood, the theory–design connection is especially important because children experience a digital game through more than the task itself; cognitive load, interface demands, adult mediation, and emotional response all shape what the activity becomes for them [99,100,101].

One of the most important conclusions of the review concerns the difference between adapting learning content and adapting game elements. Most included studies adapted content, pacing, feedback, or learning sequence within a fixed game structure. Only a small number adapted the game elements themselves according to learner characteristics. This means that much of what is currently called adaptive gamification in early childhood may be better understood as adaptive learning with game features [63,65]. More work is needed to examine how game elements can be personalised, whether this form of personalisation produces different outcomes from content-only adaptation, and how adaptive changes can be made understandable to both children and teachers [65,87,91,102].

The learning and motivational evidence is cautiously positive, but it is not uniform. Many studies reported improved performance, learning efficiency, engagement, or satisfaction. At the same time, null and mixed findings make clear that adaptivity is not automatically beneficial. Prior knowledge, developmental stage, feedback type, challenge level, and the chosen game elements all changed how children responded to the adaptive activity [20,45,77]. Future research therefore needs to ask not only whether adaptive gamification works, but also how it works, for whom it works, and under what conditions it may create stress, overload, or superficial engagement [65,85].

For instructional designers and educational software developers, the present review indicates that adaptation should not just be about increasing or decreasing content difficulty. This form of adaptation was the most common in the included studies, but it raises questions about whether it can fully address the needs of young children, who may also need adaptation in feedback format, pacing, repetition, interface complexity, emotional load, and the way game elements are connected with the learning task [45,78]. Cognitive load, motivation, developmental suitability, and teacher support are also key design issues requiring attention, not secondary characteristics [30]. At the same time, AI-driven adaptive mechanisms seem to open new possibilities for more dynamic learner modelling and real-time personalisation, especially when systems can use interaction data to adjust learning paths more continuously [41,64]. However, this also brings important risks and ethical questions about transparency, privacy, bias, and teacher preparedness, which have also been stressed in broader work on AI-supported personalised learning and AI-enhanced gamification [95,103]. Consequently, future research should not only ask whether adaptive systems work, but also consider why they work, for whom, under what classroom conditions, and with what type of teacher support. From a policy perspective, it is clear that adaptive learning technologies for early childhood cannot be effectively integrated without appropriate teacher training and clear accessibility standards [4,104].

Finally, teachers should remain central in future work [105]. Adaptive systems can individualize tasks, collect learning data, and provide immediate feedback, but they cannot replace pedagogical judgment [91,106]. In practice, teachers still have to interpret system data, decide when to step in, support children who struggle with the interface or the adaptive challenge, and connect game-based activities with broader classroom objectives [91,106,107]. Future work should give more attention to teacher preparation, classroom integration, inclusive design, and long-term implementation [14,105,108,109]. If these issues are addressed systematically, adaptive gamification and adaptive GBL can become useful tools for early childhood education [14,30]. Even then, they should be treated as carefully designed learning environments rather than universal solutions. Their value depends on how well they fit young learners’ actual needs, abilities, and interests in context [14,46,83,97].