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Trends in Artificial Intelligence-Supported E-Learning

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 32818

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Special Issue Editors

Dr. Todorka Glushkova

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Guest Editor

Computer Technologies, Plovdiv University, 4000 Plovdiv, Bulgaria
Interests: AI in education; context modeling; adaptive e-learning; CPSS educational platforms

Prof. Dr. Lyubka Doukovska

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Guest Editor

Intelligent Systems Department, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Interests: computational intelligence; prediction; neural networks; e-learning systems; predicative modeling
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Special Issue Information

Dear Colleagues,

Modern AI technologies have significant potential to support and develop education. Through them, it is possible to significantly facilitate the achievement of educational goals and the development of the digital competencies necessary for the modern person. In addition, these technologies have the potential to increase the activity and motivation of all participants in the educational process. This Special Issue focuses on the creation of new educational strategies related to the application of AI technologies in the creation of educational platforms for distance and e-learning.

Topics of interest include, but are not limited to, the following:

AI technologies for personalization of learning;
Semantic modeling and ontologies;
Contextual modeling;
Predicative modeling;
Innovative use of technology in the classroom, from primary to higher education;
Computer and web-based software and mobile applications for distance and blended learning;
Augmented reality and metaverse for education;
Creating cyber-physical platforms for lifelong learning.

Dr. Todorka Glushkova
Prof. Dr. Lyubka Doukovska
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

AI in education
human–computer interaction
adaptive e-learning
inclusive teaching
extended reality
cyber-physical educational platforms
lifelong learning

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Published Papers (9 papers)

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Research

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29 pages, 722 KB

Open AccessArticle

ChatGPT-Assisted Learning Effectiveness and Academic Achievement: A Mechanism-Based Model in Higher Education

by Ahmed Mohamed Hasanein and Bassam Samir Al-Romeedy

Information 2026, 17(3), 303; https://doi.org/10.3390/info17030303 - 21 Mar 2026

Viewed by 449

Abstract

This study examines the impact of ChatGPT-assisted learning on the academic achievement of hospitality and tourism students in Egyptian public universities, with particular emphasis on the mediating roles of perceived usefulness and self-regulated learning. Drawing conceptually on the Technology Acceptance Model (TAM), the study adopts a contextualized framework that emphasizes perceived usefulness while incorporating ChatGPT-assisted learning effectiveness as a learning-oriented driver within generative AI-supported educational environments. A quantitative research design was employed using an online survey administered to students who actively used ChatGPT for academic purposes. A total of 689 valid responses were collected from nine public universities and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses. The findings indicate that ChatGPT-Assisted Learning Effectiveness (CALE) has a statistically significant and positive direct effect on academic achievement (AA; β = 0.386, T = 3.946, p < 0.001, 95% CI = 0.192–0.561) and strongly predicts perceived usefulness (β = 0.673, T = 9.274, p < 0.001, 95% CI = 0.581–0.742) and self-regulated learning (β = 0.707, T = 10.734, p < 0.001, 95% CI = 0.621–0.779). In turn, PU (β = 0.281, T = 3.854, p < 0.001, 95% CI = 0.142–0.417) and SRL (β = 0.220, T = 2.418, p = 0.016, 95% CI = 0.041–0.356) significantly enhance academic achievement. Mediation analyses further confirm that PU (β = 0.189, T = 2.366, p = 0.018, 95% CI = 0.031–0.284) and SRL (β = 0.156, T = 3.699, p < 0.001, 95% CI = 0.102–0.301) partially mediate the relationship between CALE and academic achievement. These findings offer important theoretical insights by contextualizing TAM’s performance-related logic within generative AI-driven learning environments and refining its application to academic outcome settings, while highlighting self-regulated learning as a critical explanatory mechanism. From a practical perspective, the study provides valuable implications for educators and policymakers by emphasizing the need to promote students’ perceived usefulness of ChatGPT and foster learner autonomy, positioning generative AI as a powerful pedagogical support tool for enhancing academic success in hospitality and tourism education. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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18 pages, 5196 KB

Open AccessArticle

Design and Assessment of an Immersive Hydraulic Transmission Teaching Laboratory

by Chunxue Wei, Zhuoxian Chen, Anran Leng, Jiuxiang Song and Baowei Zhang

Information 2026, 17(2), 199; https://doi.org/10.3390/info17020199 - 14 Feb 2026

Viewed by 328

Abstract

Traditional hydraulic transmission education is often hindered by the subject’s theoretical complexity and abstract nature. To address these challenges, this study introduces the Immersive Hydraulic Transmission Laboratory (IHTL), a virtual teaching system designed to enhance practical learning and theoretical comprehension. The IHTL comprises three key modules: hydraulic components, disassembly experiments, and hydraulic circuits. The system’s effectiveness was evaluated through a comparative study of 80 mechanical engineering students. Results showed that the experimental group exhibited a 20% higher rate of inquiry and achieved average test scores 20.475 points higher than the control group. Statistical analysis confirms that the IHTL significantly outperforms traditional teaching methods in both stimulating student interest and improving learning outcomes. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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30 pages, 612 KB

Open AccessArticle

A KNN-Based Bilingual Book Recommendation System with Gamification and Learning Analytics

by Aray Kassenkhan

Information 2026, 17(2), 120; https://doi.org/10.3390/info17020120 - 27 Jan 2026

Viewed by 572

Abstract

The article reports on a bilingual and interpretable book recommendation platform for schoolchildren. This platform uses a lightweight K-Nearest Neighbors algorithm combined with gamification and learning analytics. This application has been designed for a bilingual learning environment in Kazakhstan, supporting learning in Kazakh and Russian languages, and is intended to improve reading engagement through culturally adjusted personalization. The recommendation engine combines content and collaborative filtering in that it leverages structured book data (genres, target age ranges, authors, languages, and semantics) and learner attributes (language of instruction, preferences, and learner history). A hybrid ranking function combines the similarity to the user and the item similarity to produce top-N recommendations, whereas gamification elements (points, achievements, and reading challenges) are used to foster sustained activity.Teacher dashboards show learners’ overall reading activity and progress through real-time data visualization. The initial calibration of the model was carried out using an open-source book collection consisting of 5197 items. Thereafter, the model was modified for a curated bilingual collection of 600 books intended for use in educational institutions in the Kazakh and Russian languages. The validation experiment was carried out on a pilot test involving 156 children. The experimental outcome suggests a stable level of recommendation in terms of the Precision@10 and Recall@10 values of 0.71 and 0.63 respectively. The computational complexity remained low. Moreover, the bilingual normalization technique increased the relevance of recommendations of non-majority language items by 12.4%. In conclusion, the proposed approach presents a scalable and transparent framework for AI-assisted reading personalization in bilingual e-learning systems. Future research will focus on transparent recommendation interfaces and more adaptive learner modeling. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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16 pages, 568 KB

Open AccessArticle

Automated Grading Method of Python Code Submissions Using Large Language Models and Machine Learning

by Mariam Mahdaoui, Said Nouh, My Seddiq El Kasmi Alaoui and Khalid Kandali

Information 2025, 16(8), 674; https://doi.org/10.3390/info16080674 - 7 Aug 2025

Viewed by 3110

Abstract

Assessment is fundamental to programming education; however, it is a labour-intensive and complicated process, especially in extensive learning contexts where it relies significantly on human teachers. This paper presents an automated grading methodology designed to assess Python programming exercises, producing both continuous and discrete grades. The methodology incorporates GPT-4-Turbo, a robust large language model, and machine learning models selected by PyCaret’s automated process. The Extra Trees Regressor demonstrated superior performance in continuous grade prediction, with a Mean Absolute Error (MAE) of 4.43 out of 100 and an R² score of 0.83. The Random Forest Classifier attained the highest scores for discrete grade classification, achieving an accuracy of 91% and a Quadratic Weighted Kappa of 0.84, indicating substantial concordance with human-assigned categories. These findings underscore the promise of integrating LLMs and automated model selection to facilitate scalable, consistent, and equitable assessment in programming education, while substantially alleviating the workload on human evaluators. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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31 pages, 1706 KB

Open AccessArticle

Enhancing EFL Speaking Skills with AI-Powered Word Guessing: A Comparison of Human and AI Partners

by Mondheera Pituxcoosuvarn, Midori Tanimura, Yohei Murakami and Jeremy Stewart White

Information 2025, 16(6), 427; https://doi.org/10.3390/info16060427 - 23 May 2025

Cited by 4 | Viewed by 5055

Abstract

This study explores the effects of interacting with AI vs. human interlocutors on English language learners’ speaking performance in a game-based learning context. We developed Taboo Talks, a word-guessing game in which learners alternated between giving and guessing clues with either an AI or a human partner. To evaluate the impact of interaction mode on oral proficiency, participants completed a story retelling task, assessed using complexity, accuracy, and fluency (CAF) metrics. Each participant engaged in both partner conditions, with group order counterbalanced. The results from the retelling task indicated modest improvements in fluency and complexity, particularly following interaction with the AI partner. Accuracy scores remained largely stable across conditions. Post-task reflections revealed that learners perceived AI partners as less intimidating, facilitating more relaxed language production, though concerns were noted regarding limited responsiveness. Qualitative analysis of the gameplay transcripts further revealed contrasting interactional patterns: AI partners elicited more structured interactions whereas human partners prompted more spontaneous and variable interactions. These findings suggest that AI-mediated gameplay can enhance specific dimensions of spoken language development and may serve as a complementary resource alongside human interaction. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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28 pages, 4157 KB

Open AccessArticle

Integrating Quantitative Analyses of Historical and Contemporary Apparel with Educational Applications

by Zlatina Kazlacheva, Daniela Orozova, Nadezhda Angelova, Elena Zurleva, Julieta Ilieva and Zlatin Zlatev

Information 2025, 16(2), 144; https://doi.org/10.3390/info16020144 - 15 Feb 2025

Viewed by 2518

Abstract

In this paper, a comparative analysis of historical and contemporary fashion designs was conducted using quantitative methods and indices. Elements such as silhouettes, color palettes, and structural characteristics were analyzed in order to identify models for reinterpretation of classic fashion costume. Clothing from four historical periods was studied: Empire, Romanticism, the Victorian era, and Art Nouveau. An image processing algorithm was proposed, through which data on the shapes and colors of historical and contemporary clothing were obtained from digital color images. The most informative of the shape and color indices of contemporary and historical clothing were selected using the RReliefF, FSRNCA, and SFCPP methods. The feature vectors were reduced using the latent variable and t-SNE methods. The obtained data were used to group the clothing according to historical periods. Using Euclidean distances, the relationship between clothing by contemporary designers and the elements of the historical costume used by them was determined. These results were used to create an educational and methodological framework for practical training of students in the field of fashion design. The results of this work can help contemporary designers in interpreting and integrating elements of historical fashion into their collections, adapting them to the needs and preferences of consumers. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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18 pages, 1638 KB

Open AccessArticle

Decoding Success: The Role of E-Learning Readiness in Linking Technological Skills and Employability in Hospitality Management Graduates

by Ibrahim A. Elshaer, Alaa M. S. Azazz, Abuelkassem A. A. Mohammad and Sameh Fayyad

Information 2025, 16(1), 47; https://doi.org/10.3390/info16010047 - 14 Jan 2025

Cited by 3 | Viewed by 4198

Abstract

Technological advancement alongside global epidemics stimulated the widescale implementation of e-learning. However, it is reported that e-learning is in the experimental phase and still requires fundamental improvements, particularly in disciplines that go beyond theoretical knowledge. The current study examines the nexus between e-learning readiness, psychological motivation, technological skills, and employability skills among hospitality management undergraduates. It also explores the moderating effects of student engagement on the linkages among these variables. To that end, this study adopted a quantitative approach and used a self-administered questionnaire survey to collect primary data. The sample included a total of 428 participants who were recruited from undergraduates of hospitality management programs in Egyptian universities using the convenience sampling technique. Data analysis included performing PLS-SEM using Smart PLS 3.0 software. The results confirm the positive effects of psychological motivation and technological skills on both e-learning readiness and the employability skills of hospitality management undergraduates. The study also underscores the mediated role of e-learning readiness in the linkages between study predictors and outcomes. Additionally, the findings highlight the moderating effect of student engagement in supporting e-learning readiness and eventually employability skills. This study adds to the hospitality management body of knowledge and provides valuable insights for education institutions and policymakers to optimize e-learning experiences. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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Review

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28 pages, 404 KB

Open AccessReview

Methodological and Technological Advancements in E-Learning

by Elias Dritsas and Maria Trigka

Information 2025, 16(1), 56; https://doi.org/10.3390/info16010056 - 15 Jan 2025

Cited by 24 | Viewed by 13723

Abstract

The present survey examines the intersection of methodological advancements and technological innovations in e-learning, emphasizing their transformative impact on modern education. It systematically explores instructional design frameworks, adaptive learning systems, immersive technologies, and data-driven analytics, highlighting their role in fostering personalized, scalable, and inclusive learning environments. Through the integration of pedagogical theories with advanced tools like artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and mixed reality (MR), this study demonstrates how e-learning systems enhance engagement, retention, and accessibility. The survey addresses critical challenges such as the digital divide, data privacy, and resistance to adoption, offering evidence-based strategies to mitigate these issues. It underscores the importance of bridging equity gaps while maintaining scalability and sustainability, particularly in underserved regions. By synthesizing state-of-the-art research and practical applications, this work provides actionable insights into the future of e-learning, advocating for a balanced approach to innovation that aligns technological capabilities with the diverse needs of global learners. The findings contribute to the broader discourse on sustainable, inclusive, and effective digital education ecosystems. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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Other

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37 pages, 6747 KB

Open AccessSystematic Review

AI-Supported Gamification in E-Learning: A Systematic Review of Adaptive Architectures and Cognitive Outcomes

by Aray Kassenkhan, Vassiliy Serbin, Roza Beisembekova, Aigerim Abshukirova and Bayan Mendekina

Information 2026, 17(3), 282; https://doi.org/10.3390/info17030282 - 12 Mar 2026

Viewed by 896

Abstract

The rapid expansion of artificial intelligence (AI) in digital education has transformed gamification from a motivational strategy into a data-driven, adaptive learning paradigm. This systematic review conceptualizes AI-supported gamification as an information-centered ecosystem integrating learning analytics, behavioral modeling, adaptive algorithms, and intelligent feedback mechanisms to enhance cognitive development and critical thinking. Following PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, ScienceDirect, Google Scholar, and ResearchGate. Peer-reviewed empirical studies published between 2020 and 2025 were considered. Studies were included if they examined gamification in educational contexts with AI-driven or adaptive system components, while non-educational contexts, duplicates, and non-English publications were excluded. After screening and eligibility assessment, 100 studies were included in the final synthesis. The review examines how AI-driven personalization, neurotechnology, predictive modeling, and generative systems reshape the design and effectiveness of gamified e-learning environments. Architectural patterns identified include recommender systems, real-time behavioral adaptation, affect-aware feedback loops, and algorithmic content generation. Across the reviewed studies, AI-supported gamified systems were frequently associated with increased engagement and moderate improvements in executive functions, higher-order reasoning, and adaptive learning pathways. However, challenges related to system transparency, data governance, algorithmic bias, cognitive load management, and equitable access remain significant. The review was not registered. By framing gamification as an adaptive information system rather than solely a pedagogical intervention, this study proposes a structured taxonomy of AI-driven gamified architectures—including data acquisition, user modeling, predictive analytics, and adaptive feedback layers—and outlines research priorities for scalable, ethically grounded, and data-informed e-learning ecosystems. Full article

(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)

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