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Review

Trends in Publications on AI Tools and Applications in Learning Design to Personalization of Learning—A Scoping Review

by
Jacoba Munar-Garau
,
Bárbara De-Benito-Crosetti
and
Jesus Salinas
*
Institute for Educational Research and Innovation, University of the Balearic Islands, Cra. de Valldemossa, km 7.5, 07122 Palma, Spain
*
Author to whom correspondence should be addressed.
Information 2025, 16(12), 1065; https://doi.org/10.3390/info16121065
Submission received: 1 November 2025 / Revised: 25 November 2025 / Accepted: 1 December 2025 / Published: 3 December 2025
(This article belongs to the Special Issue AI Technology-Enhanced Learning and Teaching)
Browse Figures
Versions Notes

Abstract

The continuous evolution of learning design (LD) necessitates a systematic review to comprehensively map the available tools that support educational practice, thereby highlighting current trends and development gaps. This study aimed to classify and analyze the features, evolution, and technological maturity of tools supporting the LD process. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines, analyzing fifty-six tools identified from major academic databases based on their support level (design, implementation, evaluation), user focus, and other characteristics. The analysis revealed a clear transition from static, desktop-based applications to dynamic, web-based, and open-source platforms. Crucially, most tools heavily focus on the initial design phase, exhibiting significant deficiencies in supporting the subsequent implementation and, particularly, the evaluation phases. The findings conclude that while the LD tool landscape is diverse, its development is uneven, suggesting a critical need for future tools to offer more robust, end-to-end lifecycle support and integrate current educational technological innovations such as Generative AI.

1. Introduction

The possibilities of digital technology to support instructional design constitute a field of research that offers various perspectives in the field of Educational Technology. Among these, the development of tools that support the representation of such designs, their publication, or their exchange and adaptive reuse have been common lines of development [1,2,3,4]. Thus, there are already different graphic tools available for learning design (Compendium LD, IAMEL, LDTool, ScentEdit, etc.), authoring (LAMS, LDSE, Web Collage, PyramidApp, OpenGLM, CADMOS, etc.), for collaborative design (SyncrLD, LsShake, Cloudworks), or, like ILDE, that seek to integrate several of them [5,6,7].
Artificial intelligence (AI) applications are another field of research in this area, which has experienced exponential growth in recent years. In some cases, these applications have taken paths independent of the development of learning design representation tools, but in others they have optimized the possibilities of such tools.
Whether it is design representation tools or AI applications, a preferred area of experimentation has been the possibilities for personalization that can occur in the design process, interaction, or evaluation. In this case, what is interesting is to review the actual advances that have been made in both cases in the personalization process.

1.1. Framework

Research on teachers’ design work in a digital world, on the practices that comprise it, and the supports that improve it, has moved between ‘instructional design’, ‘learning design’, and ‘pedagogical patterns’. Learning design (LD) has its roots in instructional design, and both share the goal of systematizing the process of finding effective solutions to educational problems.

1.1.1. Learning Design

Learning design (LD) is the process of creating learning experiences to help learners achieve educational goals. This requires teachers to deliberately choose content, activities, assessment, and technologies. We could define ‘learning design’ as a construct that explicitly documents a set of learning tasks, which can have different granularities, from a single task to a course, with the set of resources and tools that support the completion of the tasks [7,8].
LD mainly involves teachers, considering them ‘teachers as designers’. In this role, they must address both product- and process-oriented aspects of strategic planning [9,10,11]. LD facilitates the exchange, adaptation, and reuse of teachers’ pedagogical ideas, while also serving as a tool for reflection on practice [12]. Experts in the field of LD [8,13,14] argue that an LD should be shared and reused within the community.
Research into teachers’ design work in a digital world, the practices that comprise it, and the supports that improve it, has moved between ‘instructional design’, ‘learning design’, and ‘pedagogical patterns’. Learning design has its roots in instructional design, and both share the goal of systematizing the process of finding effective solutions to educational problems. Instructional Systems Design, User Experience Design, and Design Thinking, among others, can also be considered precedents [15,16].
Learning design involves choosing strategies to create specific products such as lesson plans or instructional materials, as well as implementing and managing the overall design process [15,17]. One of the key strategies for improving the quality of teaching lies in effective learner-centered design, in the possibility of personalizing learning pathways [12].
To support teachers in the process of documenting their teaching practices by making their ideas explicit and shareable, various learning design tools have been developed [12,14].

1.1.2. Tools

Over the last 25 years, a range of learning design tools has been developed to help teachers create and share effective teaching ideas [5]. These tools have been classified according to their main functionality, with the aim of enabling widespread adoption. The Larcana Declaration on Learning Design [18] identifies three main areas of research in this field:
•
strategies for representing learning designs for teachers promoting sharing and adaptive reuse;
•
technical specifications for interoperable, machine-readable design descriptions;
•
tools and strategies that guide teachers’ design processes and studies on the use and effectiveness of these tools and strategies.
For their part, Ref. [1] have focused their research on developing supports and tools for technology-enhanced learning, as well as on the design practices and processes currently used by teachers. According to these authors, efforts have been directed in three directions:
  • Systematically representing designs by formally documenting their pedagogical characteristics;
  • Sharing and exchanging these representations so that other teachers can adopt and adapt them to their contexts, improve them, and share them again;
  • Developing technological tools to support the creation, representation, exchange, and adaptation of designs.
Along with research in this field, one of the important aspects to consider has been and continues to be the trajectory of the various tools, especially the process of their adoption and generalization. According to [19], the aspects that seem to affect adoption belong to three areas:
  • Characteristics of LD tools: e.g., flexibility, support for all phases of the design process and for teachers as members of designer communities [20,21,22];
  • Teachers’ mindset [23];
  • Adequate training (or the lack thereof) [24].
The aim is to synthesize existing work, both in relation to areas of application and to this adoption process, but also to know if that synthesis can serve to advance research in the discipline. This is why our work has been a review of the literature.
Over the last 20 years, there have been various attempts to review the literature; however, as Ref. [15] pointed out, there is a wide range of LD tools, making it difficult to present a comprehensive evaluation of them. The same author [16] proposed an evaluation framework and reviewed a limited number of LD tools. Ref. [13], for his part, reviewed seven learning design tools. Ref. [18] subsequently presented a wide range of LD tools, although they were unable to cover them all. Subsequent to the work of [18], several reviews are available [5,25].
In terms of organizing tools into different categories, ref. [16] classified tools as creation environments, execution environments, and integrated environments. Ref. [13] distinguished LD tools into visualization tools, pedagogical planners, generic tools, and learning design resources. Regarding the representation of learning design used in the tools, within the same study, ref. [13] organized the tools into the following two groups: textual representation and visual representation. More recently, ref. [26] classified LD tools according to their functionality into reflection tools and pedagogical planners, creation and sharing tools, repositories, and delivery tools. This characterization is the one used by [5] in their review.
Ref. [5] describes learning design approaches by conducting a systematic review of the literature, presenting the key theoretical concepts that underpin their development.
For their part, ref. [26] conduct a systematic review around the following four key points: (1) incorporating flexibility, (2) stimulating interaction, (3) facilitating student learning processes, and (4) fostering an affective learning climate.
Jayashanka et al. [27] present a systematic review of the literature addressing the critical success factors associated with developing a partnership between learning design and learning analytics to improve higher education. Within this framework, the frameworks/models that have been used will be discussed, along with the opportunities that could arise because of developing a partnership between learning design and learning analytics. Both domains complement each other and offer different advantages. On the one hand, LA techniques provide valuable information that can be used to make decisions regarding the effectiveness of LD. This effectiveness also helps to decide on improvements to be made to LD. On the other hand, LD establishes pedagogical plans and objectives as a framework that sets the criteria for LA evaluation. Therefore, there is growing interest in bringing the two domains together to improve learning effectiveness and optimize learning environments, which in turn improves higher education.
However, the design of educational interventions for teachers and individual designers remains a craft that Ref. [14] aptly compares to the performance of a juggler who needs to strike a balance between educational objectives, the characteristics of the target population, the possibilities of available technology, and the limitations of the learning context [26].

1.1.3. Artificial Intelligence

We can observe the exponential growth of various AI technologies, such as machine learning, deep learning, natural language processing, and computer vision. Such AI technologies are extending their influence to various sectors, including healthcare, finance, and, in this case, education.
These AI technologies are becoming indispensable in various activities, and in education they have multiple applications and advances: text generation and completion, intelligent tutoring, content creation, translation, writing assistance, gamification, etc. [28]. In this field, the personalization of learning processes has become one of the recurring areas of research [29,30,31]. The application of AI in design has also been the subject of research and development for years. Today, the application of artificial intelligence technologies is an emerging field that is transforming design and all aspects of the learning process. Ref. [32] proposes the use of AI in education in three categories as follows:
•
Acting as a new subject, using tools to make decisions and simulate human behavior, such as social robots.
•
Functioning as an intermediary in the educational process, such as intelligent tutors.
•
Playing the role of a supplementary assistant and providing support to the educational process, as in the case of learning analytics.
The role that AI has developed and can continue to do so in areas such as the personalization of learning, which is one of its preferred fields of application, but the possibilities for application in education are manifold. Essentially, the growth and increase in the implications of AI lies in the creation of computer systems or machines with the ability to perform tasks and make decisions, imitating human intelligence.

1.1.4. Personalization

Focusing on how to personalize learning pathways, various researchers have considered personalization parameters [29,33,34,35,36,37]. For [37], in almost all cases, these parameters can be classified as why learn, what to learn, and how to learn. If the aim is to address the learning objective and motivation as individual differences among users, we would be dealing with parameters related to ‘why learn’. If the aim is to personalize the learning path based on the user’s prior knowledge, level of competence, limitations, and requested information, then we would be dealing with parameters related to ‘what to learn’. If we consider the individual differences in users in terms of how they approach learning scenarios, then the parameters would be those related to ‘how to learn’.

1.2. Objectives

This study aims to identify patterns and trends in publications on AI tools, prototypes, and applications in learning design, especially when it is geared toward personalizing learning.
In this context, this review seeks to answer the following questions:
  • Is there evidence of AI tools and applications that support the representation of learning designs?;
  • To what extent do AI applications support learning design representation tools?;
  • What personalization parameters are taken into account in studies on learning tools and AI applications?
Regarding the parameters, for this research the classification established by [37] has been followed.

2. Materials and Methods

This review uses the scoping review methodology, following the recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 [38]. This approach helps to identify and map research results on a specific topic, such as the possibilities, applications, and tools that contribute to learning design in the context of higher education.

2.1. Literature Search

To conduct the literature search, articles were extracted from two prominent databases: Web of Science and Scopus. The literature search was conducted in June 2025 and covers articles and conference papers published between 1 January 2016 and 31 May 2025.
The period studied covers publications on graphical learning design tools subsequent to the review by [5], including the emergence and ongoing development of generative AI (Gen AI) in the field of learning design.
We searched the databases using the following string: (“learning design” OR “design for learning”) AND (intelligent OR “artificial intelligence” OR AI). Regarding the inclusion and exclusion criteria (Table 1), our study includes all English publications recorded in Web of Science or Scopus, between 1 January 2016 and 31 May 2025.
The search terms yielded 821 publications (see Figure 1), all of which underwent further selection processes for refinement and analysis.

2.2. Summary of Publications

Figure 2 shows the number of publications on AI tools, prototypes, and applications in learning design compiled in this review. A general upward trend in the number of publications has been observed, revealing the growing development of this field over the years. Specifically, there was a considerable increase in relevant articles starting in 2021, followed by a sharp increase in recent years. Research in this field has gained momentum, apparently due to the development of GenAI applications, and the upward trend is expected to continue in the coming years.
Of the 40 documents selected for the study, they have been classified according to their type of publication as follows (Table 2): articles in scientific journals (24), conference papers (28), and other publications (2). In addition, the topics of each publication are presented according to the characteristics of interest for the study (Table 3).

3. Results

3.1. Research Question 1. Is There Evidence of AI Tools and Applications That Support the Representation of Learning Designs?

To answer this question, we consider developments in the field of research (especially since [5]). The current review includes works dedicated to the development of tools that support the representation of learning designs, their publication, or their exchange and adaptive reuse. These had been common lines of development until the review by [5]. Table 2 lists the forty selected articles. Figure 3 shows that the production of tools seems to follow the same trend in the early years of this review as in previous years (even intensifying), until 2020, when they begin to coincide with GenAI applications and are gradually replaced by them, especially since 2024.
Figure 4 shows the distribution of articles that describe the different specific tools designed for LD (9), the functions that improve its capability (7), the prompt construction or algorithm involved (19), and other aspects (4).
Among the articles studied, sixteen refer to tools of different types and levels of specificity. Of these, nine refer to specific tools designed and developed for application to learning design: ALD-AUHS [40], DLPaper2Code [44], N-Fold [47], LEAGUE using cards [48], STEMFREAK [50], edCrumbe [53], Kipulearn [61], Curio [64], LGVAE [67].
The other seven make generic reference to the functions they perform, but without referring to their design or development.
Another nineteen refer to the construction of prompts or algorithms to be used in design, either in the entire learning design process or in partial aspects (interaction, feedback, evaluation, etc.): [43,46,51,54,56,58,61,63,68,69,70,71,72,73,74,75,76,77,78].
Alongside these IAG applications, we have six that refer to the creation of chatbots (very specific aspects of design or pattern creation): [52,59,62,65,66,68]. The remaining four refer to other aspects such as teacher training in the creation and improvement of skills related to learning design, rubrics, etc. Specifically, Ref. [49] studies the use of the DIKSHA portal for the acquisition of skills by teachers, Ref. [39] presents a mobile knowledge management platform, ref. [57] how instructors can incorporate AI tools into professional learning to create flexibility, choice, and multimodal content, and Ref. [76] presents an adaptation of the Moodle LMS for scenarios that combine universal design for learning (UDL) and personalization.
In terms of the area of knowledge in which the generated designs are applied, in four cases the area is STEM [49,64,72,73], in three other cases it is language (Second Language L2) [65,69,76], and in three more cases, training in learning design skills with teachers or future teachers [49,66,73]. Two cases deal with business [71,78], one with engineering [51], and another with computer science [63]. The rest do not specify the area in which they are applied.

3.2. Research Question 2. To What Extent Do AI Applications Support Learning Design Representation Tools?

In general, the use of GenAI applications in the field of learning design representation is not applied as support or complement to existing tools, nor is there any mention of it evolving from one of them. Only eight cases refer to previously used tools, and another five are aimed at training or updating teachers in the use of GenAI in this field. In twenty-seven cases, there is no reference to previous tools. Among the eight cases that refer to previous tools, both Refs. [46,51,61] make the tool and use of GenAI compatible by incorporating prompts, and Ref. [42] proposes a hybrid recommender system along the same lines. For their part, both Refs. [43,57] focus their research on the incorporation of GenAI tools into the LMS used by teachers. Ref. [53] proposes the incorporation of a knowledge-based visualization tool into edCrumbe to improve learning design. Ref. [41] discusses a generic design tool that is currently in progress.

3.3. Research Question 3. What Personalization Parameters Are Considered in Learning Design Applications?

To study the personalization proposals, we rely on the organization of personalization parameters proposed by [42], which address the following questions: why learn, what to learn, and how to learn.
•
User time constraints refer to a user’s available time or learning pace.
•
User mastery learning: Mastery learning, which is a rigorous form of competency-based education, indicates the extent to which users have mastered the knowledge and skills (competencies) necessary for a particular course or task.
•
User learning style: Indicates how a user learns and how they like to learn.
•
User prior knowledge: Considers the knowledge users have acquired before receiving recommendations.
•
User goals: Learning goals are applied to design and plan the learning process and to organize learning objects into paths that meet user goals. Depending on the users, learning goals may differ.
Among the articles studied, there are few references to these specific personalization parameters (or others). References to personalization are generic (involvement, attitude, adaptation to students’ needs, etc.) or associated with improving the learning process without specifying further. This occurs in twenty-eight of the studies. Among them, we find three that aim to adapt designs to disabilities (indicated with an asterisk (*) in Table 3) [54,74,77], and two that are designed to improve co-learning [52,62].
Of the nine that specify personalization parameters [76], consider pace, style, prior knowledge, and personal goals. There are four studies [55,58,65,69] that focus on adapting designs to domain-based learning, i.e., how knowledge and skills are mastered, from the perspective of competency-based education. Three other studies, in addition to that of [76], consider how a user learns and how they like to learn [42,46,61]. Ref. [69] considers not only mastery but also users’ prior knowledge.
We also found four other studies that consider different personal learning objectives [43,50,60,63].

4. Discussion

The purpose of this review was to better understand existing patterns and trends in design tools, prototypes, and AI applications in learning design, especially when it is geared toward personalizing learning.
To this end, we first sought evidence of design tools and AI applications that support the representation of learning designs, analyzing the evolution of the field of learning design in the period studied (2016–2025), and the design tools and AI applications used to carry it out, especially when these designs take into account the personalization of learning.
A chronology of these tools was created and is presented alongside their classification (Table 3). This chronology identifies different tools and AI applications for learning design from the literature of the period studied. It clearly shows the emergence of GenIA applications, in the form of prompts, algorithms, or chatbots, in this field of research.
This review has found a significant number of studies dealing with the development of tools for representing learning designs, for publication, or for the exchange and adaptive reuse of such designs. It could be said that, in line with the findings of [5], the production of tools and research on them continues to be of constant interest (see Table 3), which is being transferred to GenAI applications (prompts, algorithms, chatbots), but which do not abandon the line of adapting designs to flexibility, transferring control of the process to the learner, and adapting to the needs of the student [79].
The expansion experienced by GenAI applications in learning design since 2024 is also evident in Table 3, which shows that 13 of the 40 studies since that year refer exclusively to such GenAI applications.
In general, both in terms of learning design tools and GenAI applications, various studies fall into the first two types of research proposed in ‘The larcana declaration on learning design’ [18], which highlights the importance of research on tools and strategies that guide teachers’ design processes and studies on the use and effectiveness of these tools and strategies.
Secondly, the extent to which AI applications reinforce learning design representation tools has been studied. In this case, no evidence has been found that GenAI applications have been incorporated into previous tools or that they refer to experiences of using them. Only 8 of the 40 articles contained references to the replacement or complementarity of GenAI applications with respect to existing tools. Therefore, in some cases, these applications have followed paths independent of the development of learning design representation tools, but in others, they have optimized the possibilities of these tools.
In this regard, one of the important aspects to consider has been and continues to be the trajectory of the different tools. For future research, it would be important to monitor the tools and, especially, the process of their adoption and generalization. In this sense, it seems important to pay attention to the factors that affect adoption, as pointed out by [20].
Finally, references to different personalization parameters that are considered in learning design applications have been analyzed. For this analysis, we used the personalization parameters proposed by [37], which refer to why learn, what to learn, and how to learn. To this end, the analysis was organized into the following six options: time and pace, proficiency, style, prior knowledge, and personal goals, in addition to generic references to personalization. There are few references to these specific personalization parameters (or others). References to personalization are generic (involvement, attitude, adaptation to students’ needs, etc.) or associated with unspecified improvements to the learning process, except for nine cases in which one or more of the parameters are specified.

5. Conclusions

The aim of this review has been to better understand the evolution of the field of learning design and the GenAI tools and applications used to carry it out, especially when such designs consider the personalization of learning.
In this article, we have described the evolution of these tools and applications, analyzing them according to their functionality and providing a chronology of the introduction and evolution of GenAI applications in the field of learning design. Various learning design tools have been identified in the current literature on learning design, but above all, the emergence of GenAI applications in the form of prompts, algorithms, or chatbots in this field of research is evident.
It has also been found that in many cases, GenAI applications have followed paths independent of the development of learning design representation tools, but in others they have optimized the possibilities of these tools. The degree to which these GenAI applications reinforce learning design representation tools has been studied. Although there is little evidence that GenAI applications have been incorporated into previous tools, the cases in which references have been found to the replacement or complementarity of GenAI applications with respect to existing tools give an idea of what the future of research in this field may look like.
It has been found that the various GenAI tools and applications are intended to facilitate the personalization of learning, but they rarely go into detail about how to do so or the personalization parameters that are considered in learning design applications. We understand that the role that GenAI has developed and can continue to develop in areas such as learning personalization, which is one of the preferred fields of application, suggesting why the possibilities for personalizing learning designs need to be the focus of study.
If the aim is to synthesize existing work, both in relation to areas of application and to this adoption process, and if that synthesis can serve to advance research in the discipline, a useful way to achieve this is through future literature reviews. In any case, it is necessary to provide details about the learning environment and the pedagogical approaches used, where improvements in learning design experiences are analyzed.

Funding

Grant PID2024-157113OB-100 a funded by MICIU/AEI/ 10.13039/501100011033 and by “ERDF/EU”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
GenAIGenerative Artificial Intelligence
LDLearning Design

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Information 16 01065 g001
Figure 1. Process of the search results and screening.
Figure 1. Process of the search results and screening.
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Information 16 01065 g002
Figure 2. Number of publications on AI tools. Onlyshown publications of 2025 up to May.
Figure 2. Number of publications on AI tools. Onlyshown publications of 2025 up to May.
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Information 16 01065 g003
Figure 3. Research in LD tools throughout the years (2016–2025).
Figure 3. Research in LD tools throughout the years (2016–2025).
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Information 16 01065 g004
Figure 4. Distribution of articles by topic.
Figure 4. Distribution of articles by topic.
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Table 1. Inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria.
Inclusion CriteriaExclusion Criteria
Published after 1 January 2016Published before 1 January 2016
Articles and papers in WoS and ScopusNot recorded in WoS or Scopus
English languageNot in English
Table 2. Type of publications.
Table 2. Type of publications.
Type of PublicationNumber
Journal articles15
Conference papers23
Others2
Table 3. Characteristics of the publications.
Table 3. Characteristics of the publications.
ApplicationSupport Personalization
AuthorsYearToolPromptChatbotOtherYesNoOtherTimeAreaStylePreviousObjectivesGeneric
[39]2016   X X      X
[40]2017X    X      X
[41]2017X   X       X
[42]2017X   X    X   
[43]2018    X      X 
[44]2018X    X      X
[45]2019X     X     X
[46]2020XX  X        
[47]2020X    X   X  X
[48]2020X    X      X
[49]2021   X X      X
[50]2021X    X     X 
[51]2021XX  X       X
[52]2022  X  X      X
[53]2022X   X       X
[54]2022 X   X      X
[55]2022X    X  X    
[56]2023 X    X     X
[57]2024   XX       X
[58]2024 X   X  X    
[59]2024  X   X     X
[60]2024X    X     X 
[61]2024XX  X    X   
[62]2024  X  X      X
[63]2024 X   X     X 
[64]2024X    X      X
[65]2024  X  X  X    
[66]2024  X   X     X
[67]2024X    X      X
[68]2024 XX  X      X
[69]2024 X   X  X X  
[70]2024 X   X       
[71]2024 X   X      X
[72]2024 X   X      X
[73]2024 X    X     X
[74]2025 X   X      X
[75]2025 X   X      X
[76]2025 X X X X XXX 
[77]2025 X   X      X
[78]2025 X   X      X
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Munar-Garau, J.; De-Benito-Crosetti, B.; Salinas, J. Trends in Publications on AI Tools and Applications in Learning Design to Personalization of Learning—A Scoping Review. Information 2025, 16, 1065. https://doi.org/10.3390/info16121065

AMA Style

Munar-Garau J, De-Benito-Crosetti B, Salinas J. Trends in Publications on AI Tools and Applications in Learning Design to Personalization of Learning—A Scoping Review. Information. 2025; 16(12):1065. https://doi.org/10.3390/info16121065

Chicago/Turabian Style

Munar-Garau, Jacoba, Bárbara De-Benito-Crosetti, and Jesus Salinas. 2025. "Trends in Publications on AI Tools and Applications in Learning Design to Personalization of Learning—A Scoping Review" Information 16, no. 12: 1065. https://doi.org/10.3390/info16121065

APA Style

Munar-Garau, J., De-Benito-Crosetti, B., & Salinas, J. (2025). Trends in Publications on AI Tools and Applications in Learning Design to Personalization of Learning—A Scoping Review. Information, 16(12), 1065. https://doi.org/10.3390/info16121065

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