Individualized Teaching and Personalized Learning in Higher Education: Insights and Future Directions from Systematic Mapping Review

Abstract

This study examines individualized teaching, personalized learning, and adaptive learning within the framework of constructivist pedagogy in higher education. The aim is to systematically analyze and map conceptual and empirical literature published between 2019 and 2026 to identify dominant research trends, methodological approaches, and key findings related to student-centered instructional models. A systematic mapping review was conducted using a structured research matrix aligned with PRISMA guidelines to map and compare existing studies on the selected concepts. The analysis focused on how individualized, personalized, and adaptive approaches are operationalized in higher education practice and how they contribute to student-centered learning environments. The findings indicate that although these approaches are widely discussed in the literature, they are often conceptually fragmented and inconsistently defined across studies. Several research gaps were identified, particularly regarding the integration of technological and pedagogical dimensions and the lack of coherent conceptual frameworks that connect the three approaches. Based on a synthesis of the findings, the study proposes directions for future research and suggests developing a more integrated conceptual orientation for student-centered teaching in higher education. Building on these patterns, the Transformative-Dynamic Learning and Teaching Approach (TDLTA) is introduced as a potential framework for further theoretical refinement and empirical validation.

1. Introduction

Student-centered teaching in higher education is needed today more than ever [1,2]. Learning in a modern society implies developing skills and forming attitudes because the facts are available to everyone at any time. Enhanced learning effectiveness and more efficient achievement of learning outcomes are observed when instruction is individualized and learning is personalized and adaptive, although distinctions exist among these concepts in terms of their underlying principles and implementation [3,4,5,6] concerning adaptive learning as a category of technology and environment [7,8,9,10,11]. Adaptive learning is a concept intrinsically linked to the educational environment, whose implementation depends on the integration of intelligent technologies and adaptive systems [7,12]. Adaptive learning can be academically defined as an educational approach grounded in the principles of artificial intelligence (AI) that employs intelligent technologies and algorithms to adapt learning content, instructional methods, and learning pace to the individual needs, abilities, and progress of learners in real time. Some authors [13] represented the core of adaptive learning: teaching methods enabled by technology, and some authors [7] developed the concept of personalized adaptive learning as a new pedagogical approach permitted by SLE (smart learning environment).

Personalized learning has occurred for a long time (a hundred years) but in other forms (for example, apprenticeship and mentoring), and with the development and maturation of educational technologies in the second half of the twentieth century, personalized learning emerged in the form of intelligent tutoring systems [8]. The literature provides explanations of terms related to individualized teaching and personalized learning in higher education. Given that student-centered teaching must be integrated into higher education as a paradigm of modern and contemporary instruction, the central issue addressed concerns the identification and explanation of the key elements involved in competence acquisition within this context. Students taking responsibility for their personal learning is an important element of a personalized learning environment [14,15,16]. Personalized learning can be defined as a student-centered educational approach that adapts learning processes, content, and instructional strategies to the individual needs, abilities, interests, and goals of learners, while encouraging students to take active responsibility for their own learning and competence development through the support of educational technologies and individualized instruction. Individualized teaching can be defined as an instructional approach in which teaching methods, learning activities, pace, and educational support are adjusted to the individual characteristics, abilities, and learning needs of each student in order to facilitate more effective knowledge acquisition and competence development. Conclusions drawn from the reviewed literature distinguish among adaptive learning, individualized teaching, and personalized learning, primarily in terms of the source and extent of instructional adaptation, as well as the role of technology, the learning environment, and learner agency within the educational process. Adaptive learning is grounded in technology and environment and data-driven technologies that dynamically modify instructional content, learning pathways, and task complexity in response to learners’ real-time performance and progress. In contrast, individualized teaching refers to the pedagogical adjustment of instructional methods, pace, and activities by the teacher according to the specific abilities and learning needs of individual students, while generally maintaining common learning outcomes. Personalized learning represents a broader learner-centered paradigm that not only adapts instruction to students’ abilities and needs but also incorporates their interests, preferences, goals, and active participation in directing and managing their own learning process.

Adaptive learning, individualized teaching, and personalized learning are closely connected to constructivist pedagogy because all three approaches emphasize student-centered instruction, active knowledge construction, and the adaptation of educational experiences to individual learners’ needs and prior knowledge [17,18]. This means that teaching has to be very innovative as a practice and that a constructivist learning environment exists, which is known as the constructivist teaching paradigm [19]. Learning, which implies the engagement of students in higher education, should be present in the teaching of all sciences, especially when we bear in mind that tomorrow their employers will look for developed competencies in these students, which contribute to production, sales, and the labor market, thus achieving success in business. Efficiency should be everyone’s priority. The question arises whether teachers in higher education are ready for it, and if they are competent for innovative methods in higher education.

Seven pedagogical goals of the constructivist learning environment can be found in the literature: adopt higher levels of skills—the curriculum should be created as a framework and based on the global context; students’ questions related to research are valued and expected; learning resources are research materials; learning is interactive and based on students’ initial competencies; teachers, respecting their own opinion, help students develop their competencies with questions and reflections; the teacher’s role in the teaching process is interactive and based on negotiation; evaluation of learning outcomes is based on formative evaluation that includes feedback during the learning process; the acquired knowledge is dynamic and can change with experience and practice; and group cooperation is an important part of learning [20]. There are numerous benefits of constructivism: active learning makes students more satisfied; when teaching is based on thinking and understanding, the effects are better; learning is transferable, which means that it can be better used in other situations; students can manage acquired competencies and express themselves in several ways; learning makes students more involved in asking questions; sharing experience and developing social competences is important for constructivist learning.

Students as the center figure in the learning and teaching process is a significant characteristic of personalized learning [14]. Here, it is important to emphasize that personalized learning is not synonymous with individual learning; rather, it is more closely aligned with individualized teaching, which enables the teacher to design and adapt lesson design in accordance with students’ abilities, needs, and learning characteristics. Students in personalized learning work supportively because their activities should be based on participation and connection with other students [19]. Higher education teachers are invited to prepare individualized teaching that contributes to students’ personalized learning. Some authors [21] base their judgments about personalized learning on the description that this type of learning is an increasingly student-driven model in which students engage deeply with meaningful, authentic, and rigorous challenges in order to demonstrate expected learning outcomes. They consider two views related to personalized learning to be important: personalized learning is an improved mode to achieve learning outcomes in a teaching process and is an efficient approach for acquiring competencies.

Based on the research, it can be concluded that there are four crucial attributes of personalized learning [1]. These points include voice (students’ involvement and engagement) and co-creation (students working with others to evaluate learning goals, identify assessment methods, and establish creative learning activities), social construction (students develop ideas and draw conclusions by researching with other students), and self-discovery (students create their own conclusions that they can apply in everyday life (general competences) or in developing professional competences).

In higher education, the teacher should be a reflective expert in a reflective practice [22,23,24,25,26,27], which will contribute to the successful implementation of individualized teaching and personalized learning in the teaching process. The purpose of this paper is to analyze and map the theoretical development and practical application of these concepts, drawing on relevant empirical and conceptual findings. It further aims to identify key results and research gaps, and to examine future directions for their development in the context of emerging technological challenges, particularly the use of artificial intelligence in higher education teaching. An examination and clarification of these concepts may support researchers in drawing conclusions that contribute to the improvement of teaching practices in higher education.

2. Materials and Methods

This study systematically maps the conceptual and empirical literature on personalized learning (PL) and individualized teaching (IT) in higher education (HE) published between January 2019 and January 2026. The seven-year timeframe reflects the rapid evolution of digital and AI-enabled developments in HE. The mapping addresses four research questions:

RQ1. How are the concepts of individualized teaching and personalized learning defined and used in the selected literature?

RQ2. What are the dominant research problems and goals related to these concepts?

RQ3. Are there methodological trends in research on individualized teaching and personalized learning?

RQ4. What do empirical results tell us, and how do they contribute to the further conceptualization of these concepts?

The analysis also examines the role of artificial intelligence across all four research questions, given the review’s explicit focus on technology- and AI-supported practices. The study adopts a systematic mapping review (SMR) methodology [28,29], drawing also on guidance for systematic reviews in educational research [30,31,32], and supported by the PRISMA 2020 framework [33] for the identification, screening and reporting phases. A mapping approach is appropriate where the analytical aim is to chart the structure of a heterogeneous field, combining theoretical, design-oriented and empirical contributions, and to identify dominant patterns and research gaps, rather than to weigh comparable evidence for a single intervention effect. A review protocol comprising research questions, search strategy, eligibility criteria, mapping matrix and synthesis approach was developed prior to data collection.

The search was conducted on 5 January 2026 across three sources: Scopus, Web of Science Core Collection, and Google Scholar. The search string combined two conceptual blocks joined by the Boolean operator AND. The first block captured the target concepts personalized learning and individualized teaching, including their British and American spellings, and the second the educational context, comprising higher education, university, tertiary education, and post-secondary education. Synonyms within each block were joined by OR. The search was applied to title, abstract, and keyword fields and limited to peer-reviewed articles, conference papers, and review papers published in English between 1 January 2019 and 5 January 2026. Database-specific implementations of the search string are provided in

Table 1. Database-specific search strategies.

Database	Field/Interface	Search Syntax and Filters
Scopus	TITLE-ABS-KEY	TITLE-ABS-KEY ((“personalized learning” OR “personalised learning” OR “individualized teaching” OR “individualised teaching”) AND (“higher education” OR “university” OR “tertiary education” OR “post-secondary education”)) AND PUBYEAR > 2018 AND PUBYEAR < 2027 AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”)) AND (LIMIT-TO (LANGUAGE, “English”))
Web of Science Core Collection	Topic (TS)	TS = ((“personalized learning” OR “personalised learning” OR “individualized teaching” OR “individualised teaching”) AND (“higher education” OR “university” OR “tertiary education” OR “post-secondary education”)) Filters: Publication Years 2019–2026; Document Types: Article, Proceedings Paper, Review Article; Language: English
Google Scholar	Advanced search	Same conceptual combinations applied via the advanced-search interface; period restricted to 2019–2026; results sorted by relevance and screened systematically using the same eligibility criteria.

. Google Scholar was used as a supplementary source, in line with the inclusive logic of mapping reviews, to capture gray literature, open-access conceptual contributions, and emerging work on technology and AI in higher education that is often slow to be indexed in subscription databases. The known limitations of Google Scholar are indexing transparency, ranking volatility and limited Boolean operator support [34,35].

Studies were eligible if they were peer-reviewed articles, conference papers or review papers, published between 1 January 2019 and 5 January 2026 in English, with a retrievable full text, that substantively addressed personalized learning, individualized teaching or technology- and AI-driven implementations of these in higher education—either as conceptual papers contributing to theoretical development or as empirical papers presenting original research. Studies were excluded if the full text was unavailable, if the work did not focus on higher education or was irrelevant to the research questions, if it was published in popular magazines or as a complete book or whole conference proceedings (standalone analytical chapters remained eligible as conceptual contributions), or if it addressed highly specialized technical or domain-specific applications (e.g., medical education, robotics curricula, computer-science theory) that did not engage with general HE teaching and learning.

The selection process followed the PRISMA 2020 four-stage flow and is summarized in Figure 1. The initial search yielded 1419 records (Scopus: n = 358; Web of Science: n = 202; Google Scholar: n = 859). After removal of 197 duplicates, 1222 records were screened on title and abstract; 1050 were excluded as not constituting either an empirical or a conceptual/theoretical paper as defined by the inclusion criteria. Of the remaining 172 reports sought for retrieval, 5 could not be obtained, leaving 167 assessed against the full inclusion and exclusion criteria. A further 132 reports were excluded at the eligibility stage: full text unavailable (n = 32), not correlated with higher education (n = 26), irrelevant to the research questions (n = 28), published in magazines, books or whole proceedings (n = 29), and focused on overly specialized technical thematic areas within HE (n = 17). The final corpus comprises 35 papers: 21 conceptual and 14 empirical.

Screening and selection were conducted independently by two reviewers (the authors of this paper) at every stage. Disagreements were resolved through structured discussion until consensus was reached; consistent with the inclusive aim of mapping reviews, the more inclusive judgment was adopted in the rare unresolved cases at the screening stage and the more conservative judgment at the eligibility stage.

Data extraction was structured around a mapping matrix developed prior to the analysis, which serves as the central analytical instrument of the review. The matrix categorizes each included study along ten dimensions (

Table 2. Dimensions of the mapping matrix.

	Dimension	Description
1	Bibliographic data	Authors, year, journal/proceedings, country of first author
2	Paper type	Conceptual or empirical
3	Definition of PL/IT	How the concepts are explicitly defined or implicitly used
4	Research problem and aims	For empirical papers: the stated research problem and goals
5	Conceptual contribution	For conceptual papers: the theoretical or analytical contribution
6	Methodological approach	Research design, methods, sample, data analysis (empirical)
7	Educational context	HE level, discipline, institutional type, country
8	Technology and AI element	Presence and type of technology or AI tools referenced
9	Main findings or argument	Key empirical results or central conceptual argument
10	Implications and gaps	Implications for HE practice and identified research gaps

) that jointly support the four research questions. Both reviewers independently extracted data for all 35 studies into a shared spreadsheet, with periodic cross-validation; discrepancies were resolved through discussion.

Findings from the 35 included studies were aggregated along the dimensions of the mapping matrix and synthesized using a combined descriptive and thematic–narrative approach [36,37,38]. Frequencies and proportions are reported where appropriate (e.g., the distribution of conceptual versus empirical contributions over the period, methodological designs) and complemented by a qualitative thematic interpretation oriented to the four research questions.

3. Results

3.1. Definition and Concepts of Individualized Teaching and Personalized Learning in Selected Empirical and Conceptual Papers

The 21 conceptual and 14 empirical papers display considerable variation in how they define and use the concepts of individualized teaching (IT) and personalized learning (PL). A recurring starting point in the conceptual literature is the framing of both IT and PL as elements of the constructivist teaching paradigm [39]. Across the conceptual corpus, the term “individualized teaching” appears only in a subset of papers. The term “individualization of learning” is frequently replaced either by “individual learning,” understood as the independent execution of learning tasks, or by “individualized learning,” which is in turn treated as a component of personalized learning [40]. Accordingly, individualized teaching is described as an approach in which practical problems can be solved and performs individual cognitive functions to achieve learning outcomes [41]. Within the same literature corpus, individualized learning is correspondingly explained through collaboration that enables teachers to more accurately predict and analyze student participation and to provide individualized guidance to students with such specific needs [10]. Individualized instruction is frequently associated with special education or with students who are limited in some way compared with their peers (IEPs, individualized educational plans) [8]. A further account [39] defines this type of learning as differentiated instruction, in which a teacher aligns instructional elements with students’ capabilities to foster self-paced learning and individual progress.

Another recurring strand within the conceptual literature characterizes individualized learning as the ability to study and write independently, thereby reducing the teacher’s capacity to meet students’ individualized and personalized needs [42]. In contexts of asynchronous distance learning, it is suggested that instructors can more easily offer individualized learning pathways, for example, where AI-technology creates personalized learning routes that adapt to each student’s requests, skills and goals [43]. Two further conceptual papers [44,45] characterize individualized learning by its provision for self-paced progression.

A distinct minority position within the conceptual corpus treats individualized teaching and personalized learning as synonymous. One account [46] explicitly equates “personalization” with “individualization” and uses the term “personalized education” to describe the data-based modification of any aspect of instructional activity in response to relevant learner features, with learner characteristics defined as those that predict more or less successful achievement of learning outcomes. Two further contributions [12,47] treat individualized teaching, personalized learning and adaptive learning as interchangeable. Against this conflation, a distinct conceptual proposal advances the Dynamic Integration Model [48], presented as ensuring that personalization remains responsive to dynamic student needs and moves beyond the notion of static learning styles.

Across the 14 empirical studies included in the corpus, none provided an explicit definition or operationalization of individualized teaching, indicating its empirical underdevelopment relative to personalized learning. Although the term “individualized teaching” was expected to appear in the empirical literature, the analysis shows that neither a direct nor an indirect definition of this term is found in the observed empirical works.

3.2. Addressing Research Problems and Research Goals Related to the Concepts of Individualized Teaching and Personalized Learning

The 14 empirical papers address two broad sets of research problems. The first group of studies situates personalized learning within the general context of academic teaching and higher education [12,44,49,50]. A second group links personalized learning more closely to technology-mediated learning environments, including distance education (e.g., e-learning) and learning and teaching associated with the use of artificial intelligence technologies. The range of research topics extends from the direct experiences of students and the development of self-regulation and motivational strategies [49] to personalized learning as a systemic support at the broader level of higher education work organization and higher education itself [44,48,50].

Within the first group, one contribution [44] introduces personalized learning and examines its innovative practices and impact within the teacher education system. Another study [49] examines personalized strategies as a basis for personalized learning, in order to improve students’ academic performance and motivation. A further contribution [50] uses the concept of adaptive learning to investigate the extent to which certain obstacles hinder greater implementation of such practices in teaching at universities.

3.3. Methodological Trends in Research on Individualized Teaching and Personalized Learning

Of the 14 empirical studies, nine adopt a quantitative approach, and five adopt a qualitative approach, with no single methodological orientation being dominant [40,46,48]. The quantitative studies range from classic attitude surveys [44,49,51,52,53] to a quasi-experimental research design [54]. The five qualitative studies encompass the characteristic features of this type of research, with a focus on the interview method [9], systematic literature review [16,55], or the Delphi method in expert interviews [50,56].

3.4. Contribution to the Further Conceptualization and Theoretical Development of the Concepts of Individualized Teaching and Personalized Learning

Across the empirical corpus, contributions to the further conceptualization of personalized learning cluster around three aspects: students’ psychological need satisfaction and motivation, students’ role in pedagogical decision-making, and the systemic dimension of personalized learning implementation.

The first is evident in the study [9], which reports that implementing personalized learning principles and strategies in online courses supports students’ perceived fulfillment of psychological needs (particularly autonomy and competence), increases intrinsic motivation, and enhances perceived learning engagement. The same study also reports that the application of personalized learning has a positive effect on learning outcomes, students’ control over the learning process, the strengthening of their interests, and the easier understanding of the learning material. Comparable conclusions are reported in another study [54] on the role of personalized e-learning in achieving learning outcomes.

Research on students’ participation in decision-making processes related to the organization of teaching [44] reports that the application of personalized learning gives students a more active role in the teaching process, which could be a contribution to further contextualization in a pedagogical decision-making sense. Although students still do not see themselves as equal participants in decision-making processes, the results indicate that the use of personalized learning and the opportunity for students to influence aspects of the teaching process are associated with greater academic advancement and greater engagement in learning. A related study [55,57] on the role of personalized learning systems reports that, while these systems provide valuable opportunities for adaptive learning and for meeting students’ preferences, the question of students’ motivation to learn remains the responsibility of teachers. Another study [49] also addresses student motivation and reports that the use of personalized strategies has a positive impact on students’ academic performance and motivation to learn.

Studies on learning and teaching in higher education describe activities related to personalization at a systemic level [58]. One study [52] reports that current trends show generative artificial intelligence tools are increasingly being used in the academic community for the purpose of learning. An expert review [59] concludes that personalized learning is better conceptualized as an integrated ecosystem that supports a variety of pedagogical strategies and interactive engagements. Four further contributions [48,50,60,61], using the example of adaptive learning, present personalized learning as a system that combines three dimensions: technology and infrastructure, the teaching and learning process, and organizational and implementation issues.

Four further empirical studies [9,40,45,51] do not formulate conclusions with direct implications for the conceptual development of personalized learning; their focus is rather on the technological aspect of learning environments [62].

3.5. The Role of Artificial Intelligence in Research on Individualized Teaching and Personalized Learning

Given the prominence of technology- and AI-supported practices in this mapping, the role of artificial intelligence in the studies discussed in this section warrants explicit attention. Across the studies cited in previous sections, several contributions engage directly with artificial intelligence as the object of investigation [63,64,65,66]. Empirical and review studies in this group include investigations of generative AI use in the academic community [52,53], a long-range mapping of two decades of AI in education [52], systematic reviews of AI as support for self-directed learning [16] and of personalized education and AI across national contexts [55], and an AI-based approach to adaptive and personalized learning [48]. A further contribution [12] reports on a ChatGPT-based personalized adaptive learning strategy in higher education. In addition, the conceptual contributions discussed previously emphasize the analytical use of AI in education: the three-paradigms framework for AI in education [10], a definitional treatment of AI [41], and broader handbook-style accounts of AI in higher education [42,43].

A related but analytically distinct line of inquiry approaches this field from the perspective of adaptive learning rather than through the explicit framing of artificial intelligence. This is the case for the Delphi study on the establishment of adaptive learning in higher education [50] and for the systematic review of intelligent educational technologies in individual learning [45], where AI techniques are implicit in the system architecture but are not foregrounded terminologically. The remaining empirical and conceptual contributions address technology and learning environments more broadly, including blended and online learning [9], personalized e-learning [54], digital teaching technologies and their barriers [51], and personalized learning as a pedagogical and institutional concern [8,44,49,56], without taking artificial intelligence as the central object of study.

4. Discussion

Perhaps the most significant finding from the mapping is also the one with the widest implications. Personalized learning (PL) is the dominant term in both conceptual and empirical work. Individualized teaching (IT), by contrast, appears only sporadically in the conceptual literature and is entirely absent, neither directly nor indirectly defined, from the empirical studies (Section 3.1). This is not, we reflect, a matter of vocabulary or terminology preference. It influences both the formulation of research questions in higher education and the visibility of particular forms of pedagogical practice.

The prevalence of PL reflects the broader shift from teacher-centered to learner-centered framings, in which agency increasingly sits with the learner and with the technologies that support them [7,9,10,13,60]. The empirical absence of IT, on the other hand, suggests that researchers currently lack a working vocabulary for the teacher’s side of personalization: the instructional decisions, the pacing choices, and the small acts of differentiation that teachers make. When only one side of the pedagogical relationship has a research vocabulary, the resulting evidence base will tend, almost by default, to privilege learner outcomes over teaching practices. The literature itself is divided into whether IT should be kept as a distinct construct or absorbed into PL. Some authors treat them as synonymous [12,46,47], while others insist on their separation [39,48,56]. And even when PL is the explicit object of empirical study, only one paper in our corpus [9] provides a direct definition, limiting the comparability and cumulative integration of findings across studies.

If PL and IT are not synonyms, what do each do that the other does not? Reviewed literature, read carefully, allows a distinction along three dimensions: curriculum design, teaching practice, and assessment. In curriculum design, PL builds learning options, pathways, and resources around the learner’s profile: their prior knowledge, their goals, their pace, their preferences [7,9,46,56]. IT, as it appears in conceptual literature, looks more like differentiated instruction. The teacher aligns instructional elements with the capabilities of specific learners [39] or develops structured responses to learners who need accommodation [8,41]. The distinction is not really between learner and teacher orientations. It is, rather, two different organizing principles: PL starts with the learner’s profile and constructs a flexible architecture around it; IT starts from the teacher’s instructional repertoire and differentiates its delivery.

In teaching practice, PL is closely tied to technology-mediated environments, including adaptive systems, recommendation tools, and AI-enabled learning routes [9,12,43,48,50]. In these settings, a good part of the personalization work is offloaded to algorithmic infrastructures. IT, by contrast, remains anchored in the teacher as a pedagogical decision-maker who interprets learners’ needs in the moment [10,39,42]. The two constructs therefore make different demands on the teacher’s role. PL requires teachers to design and manage personalized learning environments, whereas IT requires them to exercise differentiated judgment within specific instructional encounters.

In assessment, the literature is less developed, but a meaningful distinction can still be drawn. PL is associated with data-driven, often continuous feedback generated by the technological environment itself [48,50,55]. IT, where it engages with assessment, depends on the teacher’s qualitative judgment and on the adjustment of subsequent instruction in response to that judgment [39]. The two constructs, therefore, make assessments do different kinds of work, formative–algorithmic in one case, formative–interpretive in the other, and a mature higher education pedagogy probably needs both.

Taken together, these three dimensions suggest that PL and IT are complementary constructs that operate at different points in the curriculum, instruction, and assessment cycle. The review cannot settle which of the two is more fruitful for higher education. What it does show is that the current empirical work strongly favors PL, leaving IT theoretically acknowledged but empirically undeveloped, which is a legitimate agenda for future research.

The empirical corpus addresses two parallel sets of research problems: (1) studies that situate PL in the general context of higher education teaching, and (2) studies that link PL to technology-mediated environments, including AI-supported practices. What it means is that both tracks exist, but they rarely meet. PL is being theorized and studied at the same time as both a pedagogical reform agenda and a technological design problem, and, so far, there is too little work that puts these two framings into productive contact.

The predominance of attitude surveys in the empirical corpus means that much of the available evidence speaks to participants’ perceptions of personalized learning, that is, its perceived effects on motivation, autonomy, and engagement, rather than to causally identified outcomes. The single quasi-experimental study [54] is among the few designs that allow inference about effects rather than perceptions, while the qualitative work, by its nature, offers depth and conceptual richness rather than population-level generalization.

Statements about PL supporting student motivation, autonomy or engagement are supported by the reviewed corpus at the level of self-reported perception and at the level of small-scale experimental observation. Stronger claims, about systemic transformation or institutional success, would need a foundation the corpus does not yet provide. There is, in other words, a gap between the conceptual ambition with which PL is sometimes framed [7,60] and the comparatively modest evidence on which it currently rests. Closing this gap remains an open issue for the field, requiring more robust study designs and larger samples.

Although artificial intelligence is frequently invoked as a contextual driver in the literature on personalized learning and individualized teaching, its explicit empirical study remains limited within this corpus. The boundary between AI-supported and technology-supported personalization is not consistently drawn, and this is more than just terminology. In some studies, AI is the engine of personalization: it generates routes, recommends content, provides feedback [12,43,48]. In others, AI is the research object itself, examined through users’ attitudes or adoption patterns [52,53]. In still others, AI sits implicit in broader notions of adaptive or intelligent learning environments, without being terminologically foregrounded [45,50]. These three uses do different kinds of intellectual work, and because the demarcation among them is not always clear, comparing findings across studies, or accumulating evidence about AI-supported PL as a coherent category, becomes harder than it ought to be.

A related point is the closeness between AI-driven personalization and adaptive learning. Adaptive learning, as discussed in [45,50], works through data-driven systems that analyze learner behavior and adjust instructional content, pacing, and feedback in response. Many of the techniques on which adaptive learning depends, including pattern recognition, dynamic recommendation, and predictive modeling, are AI techniques, yet the literature does not always describe them as such. Future conceptual work would probably benefit from disentangling three nested categories: technology-supported personalization in the broadest sense, adaptive learning as an intermediate category, and AI-driven personalization in the narrow sense that requires explicit AI techniques.

This leaves open a further question: how will AI tools extend the concept of personalization in learning, and will that development prove a productive or a problematic turn for higher education? The present review cannot settle the question. What it can show is that the evidence currently available for answering it is uneven, predominantly cross-sectional or perception-based, and concentrated on a few high-visibility technologies, most prominently generative AI tools in academic settings [52,53,66]. Stronger answers will need longitudinal designs, classroom-level implementation studies, and comparative work across disciplines.

The previous interpretive observations point toward a conceptual space the existing literature does not yet occupy: the asymmetry between PL and IT, the complementary roles each construct plays across curriculum, instruction, and assessment, the parallel pedagogical and technological tracks of research, and the unstable boundary between AI-driven and technology-supported personalization. The discussion concludes by advancing a conceptual proposition that emerges from these observations, with an explicit clarification of its scope and limitations.

This positioning is consistent with a broader argument that learning supported by new media, and by the possibilities of artificial intelligence, contributes to the relativization of formal in favor of informal education, with individualization and personalization of learning as immanent elements. Across the reviewed literature, a more active and dynamic and environment of higher education teaching and learning is articulated, particularly in the context of generative AI, although it has not yet been theorized as a coherent whole. The findings of the mapping highlight four persistent gaps: (1) the empirical absence of individualized teaching, (2) the parallel but weakly integrated pedagogical and technological research tracks, (3) the conceptual ambiguity surrounding the role of artificial intelligence, and (4) the lack of integrative theoretical frameworks. These gaps point to the need for a more coherent conceptual orientation. In response, we propose the Transformative-Dynamic Learning and Teaching Approach (TDLTA) as a conceptual proposition informed by these patterns (Figure 2).

TDLTA treats technology, and AI in particular, as the contextual condition in which contemporary higher education teaching and learning is situated, rather than as a feature of the pedagogical environment. It places students’ active participation at its center, and treats the dynamic, interactive nature of the learning process as a structural property of the pedagogical situation rather than as a desirable add-on. What makes the approach transformative is not technology itself: In this reading, AI is best understood as an enabling layer operating in the background, while the transformative work is carried by the pedagogical relation and the learner’s developmental engagement with it.

TDLTA draws on three theoretical traditions that lie outside the systematically reviewed corpus. We invoke them here as resources for articulating the proposition rather than as objects of empirical analysis. From transformational teaching [67] it takes the emphasis on the relational and change-oriented nature of the teacher–student relationship, and on the capacity of educational experiences to shape students’ knowledge, values, and self-understanding. From transformative learning theory [68] it takes the emphasis on reflection, on perspective transformation, and on the revision of learners’ meaning-making frameworks through intellectually and personally challenging experiences. From dynamic assessment [69,70] it takes the principle that teaching and assessment are not separate processes but interconnected, responsive practices that support learners’ development in relation to their evolving capacities. Together, these traditions help us articulate learning and teaching as a relational, developmental, reflective, and adaptive process, in one word, transformative, oriented not only to knowledge acquisition but also to meaningful personal and intellectual change.

TDLTA is not derived inductively from the 35 reviewed studies and mapping methodology, because, after all, mapping is not designed to generate new theoretical frameworks. We offer TDLTA, rather, as a conceptual proposition informed by patterns identified in the review, including the empirical absence of individualized teaching, the parallel pedagogical and technological tracks, the conceptual ambiguity surrounding AI, and the lack of integrated frameworks that would articulate AI’s contextual role in transformative pedagogy. The theoretical resources on which it draws are external to the reviewed corpus and are invoked to articulate a direction in which the field might develop. Empirical and conceptual validation of TDLTA would require separate, dedicated research, and the present paper proposes the direction without claiming to demonstrate it.

5. Limitations and Implications for Future Research

Several limitations should be acknowledged. The search relied substantially on Google Scholar as a supplementary source, which is consistent with the inclusive logic of mapping reviews but limited by Google Scholar’s known issues with indexing transparency and ranking volatility [41,42]. The corpus is restricted to publications in English, which may under-represent contributions from non-Anglophone scholarly traditions. Although screening was conducted independently by both authors, with consensus-based resolution of disagreements, formal inter-rater reliability statistics were not calculated.

Consistent with mapping methodology, we did not apply a formal evidence-weighing quality appraisal to individual studies; a relevance and conceptual-clarity check at the eligibility stage was, in our judgment, sufficient for the descriptive aims of the review. This does mean, however, that the comparative methodological strength of individual empirical studies is not adjudicated here. Readers should therefore treat the findings as a structured characterization of the field, rather than as an evaluative ranking of its contributions.

Finally, TDLTA itself has a specific limitation worth noting. It is offered as a direction for future research. Its constituent claims, about the relational character of pedagogy, the developmental potential of students, and the enabling role of AI, would need dedicated research before they can be advanced beyond their present status as a working proposition.

The most pressing research agenda, in the present analysis, concerns the empirical underdevelopment of individualized teaching. Studies that investigate teachers’ instructional decision-making, differentiation practices, and pedagogical judgment in technology-supported environments would help address the asymmetry identified in results and begin to build the terminology for the teacher’s side of personalization that currently lacks empirical form. Alongside this, longitudinal and quasi-experimental work on personalized learning interventions in higher education would extend the evidence base well beyond the attitude surveys that currently dominate it.

If the TDLTA proposition is to be developed further, it will require both conceptual elaboration, that is, precise specification of its constituent constructs and their interrelations, and empirical investigation, plausibly through classroom-level case studies, design-based research, and longitudinal research that tracks how transformative-dynamic pedagogical configurations develop over time in AI-supported higher education settings.

For higher education practitioners, the findings suggest a need to more explicitly distinguish between personalization as a system-level design principle and individualized teaching as a pedagogical practice. For institutions, the results highlight the importance of aligning AI-driven tools with pedagogical frameworks rather than treating them as stand-alone solutions.

The review further suggests that higher education teachers and institutional designers approaching personalized learning would do well to remain attentive to the distinctions outlined in results among curriculum design, teaching practice, and assessment as distinct sites where personalization operates. It also suggests that the role of AI in personalization should be approached with terminological care: AI-enabled features of learning environments are not inherently transformative; the transformative work of pedagogy continues to depend on relational and developmental processes that technology can support but not substitute.

6. Conclusions

This paper mapped 35 studies on personalized learning (PL) and individualized teaching (IT) in higher education published between 2019 and 2026, examining how the two concepts are defined and used, what research problems and methods dominate the field, what empirical work has contributed to their further conceptualization, and how artificial intelligence figures across all of this.

What the systematic mapping review shows is a field organized around personalized learning. PL has become the leading terminology in both conceptual and empirical work, while individualized teaching, especially on the teacher’s side of the pedagogical relationship, has almost entirely receded from empirical view. Once the two concepts are placed alongside one another, they look less like competing labels and more like complementary constructs, with PL and IT doing different kinds of work across curriculum, instruction, and assessment. The empirical evidence supporting this work, however, is thinner than the conceptual ambition that often accompanies it. Much of what we know about PL comes from attitude surveys; longitudinal and causal designs remain uncommon. Finally, artificial intelligence runs through all of this, although literature uses it inconsistently. Sometimes AI is the engine of personalization, sometimes it is the research object itself, sometimes it sits implicit inside broader notions of adaptive or intelligent systems, and the boundary between AI-driven and more general technology-supported personalization is not yet drawn with any consistency.

Building on these patterns, we proposed the Transformative-Dynamic Learning and Teaching Approach (TDLTA) as the principal conceptual contribution of this paper. TDLTA was not inductively derived from the 35 reviewed studies; mapping methodology, after all, is not designed to generate new theoretical frameworks. We instead offer it as a conceptual proposition informed by the patterns we observed and articulated through theoretical resources drawn from transformational teaching, transformative learning theory, and dynamic assessment. It treats AI as a contextual enabler rather than a transformative agent and places the relational and developmental work of pedagogy at the center of teaching in higher education in an AI-supported environment. Its claims will need to be tested through further research before TDLTA can move beyond its status as a working proposition.

The findings and the proposition reflect a field that appears to be undergoing both expansion and fragmentation. The expansion is evident in the proliferation of studies on PL and AI in higher education; the fragmentation is evident in unstable terminology, in the pedagogical and technological research tracks that rarely meet, and in the gap between what is conceptually claimed and what is empirically shown. What is needed next are carefully designed empirical studies that take teachers’ pedagogical judgment seriously and draw clearer distinctions between AI-driven personalization and other forms of technology-supported learning. These studies should also examine whether the proposed transformative-dynamic orientation is reflected in higher education classrooms shaped by AI.