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Applied Sciences
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Generative Artificial Intelligence (AI) in Education
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Generative Artificial Intelligence (AI) in Education

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 1971

Editors

Prof. Dr. Igor Mekterović

E-Mail Website
Guest Editor
Department of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
Interests: computer-aided instruction; learning management systems; computer science education; database systems; data processing; data warehouses; mobile applications; website; Semantic Web
Special Issues, Collections and Topics in MDPI journals
Dr. Ivana Bosnić

E-Mail Website
Guest Editor
Department of Control and Computer Engineering, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
Interests: e-learning; education; software engineering education; learning management systems; web services; recommender systems; open systems
Prof. Dr. Ljiljana Brkić

E-Mail Website
Guest Editor
Department of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
Interests: data management in education; automated programming assessment systems; personalized and adaptive learning systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of generative artificial intelligence (AI), especially large language models and multimodal generative systems, is changing educational practices, with a particularly strong impact on higher education.

In higher education, generative AI enables scalable personalization of learning, automated and adaptive feedback, automated assessment, intelligent teaching, support for research and academic writing, generation of curricula and teaching content, etc. At the same time, it is challenging conventional approaches to assessment, quality assurance, and academic integrity, and raising important questions related to transparency and explainability. Similar challenges arise not only at universities but also in primary and secondary education, vocational training, lifelong learning, and even (massive) online courses.

We are pleased to invite researchers and practitioners to contribute to this Special Issue. The aim of this Special Issue is to present scientifically based and practically relevant research on the development, evaluation, and application of generative artificial intelligence systems in educational environments, with a primary focus on higher education, but also on other levels of education. Papers that address system design, experimental evaluation, implementation experiences, and measurable educational outcomes are especially encouraged.

Potential topics include, but are not limited to, the following:

  1. Generative AI Systems and Applications in Higher Education

Design, implementation, and evaluation of AI-driven tools for teaching and learning, including intelligent tutoring systems, personalized learning pathways, automated content and curriculum generation, and support for academic research and writing. This topic also includes IDE-integrated generative AI tools; AI-assisted algorithm design and problem solving; support for programming, debugging, refactoring, and code comprehension; and the use of generative models in UML modeling and software engineering education.

  1. Assessment, Feedback, and Academic Integrity

AI-assisted assessment models, automated and formative feedback systems, redesign of assessment practices, detection and mitigation of misuse, and validation of AI-supported evaluation methods.

  1. Human–AI Interaction and Learning Analytics

Empirical studies on student engagement, learning processes, cognitive and metacognitive effects, and data-driven evaluation of learning outcomes in AI-mediated educational environments.

  1. Multimodal, Visual, and Virtual Learning Environments

Multimodal tutoring systems combining text, code, diagrams, and visual explanations; AI-supported engineering visualization; virtual and remote engineering laboratories; and the role of generative AI in enhancing experiential, simulation-based, and laboratory-oriented learning.

  1. Scalability, Evaluation, and Real-World Deployment

Architectures, benchmarks, datasets, performance metrics, and longitudinal studies assessing the effectiveness and scalability of generative AI solutions in educational practice.

In this Special Issue, original research articles and reviews are welcome. Contributions that demonstrate robust experimental design, reproducibility, and applicability to real-world educational systems are especially valued.

We look forward to receiving your submissions.

Prof. Dr. Igor Mekterović
Dr. Ivana Bosnić
Prof. Dr. Ljiljana Brkić
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • generative AI
  • education
  • human–AI interaction
  • personalized learning
  • Artificial Intelligence in education

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Published Papers (3 papers)

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Research

21 pages, 1527 KB  
Article
Independent Engineering Transfer After Traceable Generative-AI-Assisted Learning: A Six-University Controlled Trial with Deterministic Cluster Allocation in Agricultural Engineering Education
by Yurii Syromiatnykov, Farmon Mamatov, Khurshid Chuyanov, Zafar Batirov, Dustmurod Chuyanov, Makhmatmurod Shomirzaev, Dilrabo Shadieva, Khurshid Ilkhomov, Gulandom Jo’rayeva and Mirshohid Egamov
Appl. Sci. 2026, 16(18), 8949; https://doi.org/10.3390/app16188949 - 9 Sep 2026
Viewed by 249
Abstract
Generative artificial intelligence (GenAI) can support engineering problem solving, but whether AI-assisted practice transfers to independent performance after the tool is removed remains unclear. This multicentre controlled trial evaluated a traceable five-stage GenAI-assisted learning configuration in agricultural engineering education. Twenty-eight second- and third-year [...] Read more.
Generative artificial intelligence (GenAI) can support engineering problem solving, but whether AI-assisted practice transfers to independent performance after the tool is removed remains unclear. This multicentre controlled trial evaluated a traceable five-stage GenAI-assisted learning configuration in agricultural engineering education. Twenty-eight second- and third-year classes from six universities in Uzbekistan were assigned within nine teacher blocks by deterministic constrained minimization to GenAI (14 classes) or structured active-control (14 classes) groups. Both groups completed the same 16-week module, tasks, software, contact time, feedback, and verification requirements. They differed in the source and adaptivity of a provisional alternative used after an independent attempt: bounded adaptive GenAI dialogue versus a version-controlled curated alternative. The full assigned cohort included 656 students; likelihood-based available-outcome analyses included 641 immediate and 589 delayed outcomes. Kenward–Roger analyses estimated an adjusted immediate difference of 2.78 points (95% confidence interval (CI) [2.02, 3.55]; p < 0.001; model-based d = 0.72) and a delayed difference of 2.34 points (95% CI [1.54, 3.14]; p < 0.001; d = 0.51). The results show a positive adjusted association for the evaluated traceable instructional configuration, but deterministic post-baseline allocation limits causal interpretation. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence (AI) in Education)
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33 pages, 5122 KB  
Article
Text-and-Image Materials Versus an AI-Avatar Video for Introductory Adobe Illustrator Learning: A Randomized Two-Group Study
by Željko Zeljković, Nemanja Kašiković, Miroslav Stefanović, Anja Janković Žugić, Sandra Dedijer, Saša Petrović and Ivana Jurič
Appl. Sci. 2026, 16(15), 7735; https://doi.org/10.3390/app16157735 - 4 Aug 2026
Viewed by 378
Abstract
AI-generated instructional videos are increasingly used in higher education, yet their effectiveness relative to carefully designed conventional instructional materials remains unclear, particularly for introductory procedural learning. This randomized two-group study compared two complete instructional formats, static text-and-image instruction and AI-avatar video instruction. Both [...] Read more.
AI-generated instructional videos are increasingly used in higher education, yet their effectiveness relative to carefully designed conventional instructional materials remains unclear, particularly for introductory procedural learning. This randomized two-group study compared two complete instructional formats, static text-and-image instruction and AI-avatar video instruction. Both covered the same learning objectives and worked procedure, but differed in verbal presentation, temporal pacing, navigation, visual signaling and avatar presence. Eighty-one first-year university students with no prior formal university instruction in Adobe Illustrator provided pre-test, post-test, practical-task, questionnaire and seven-day retention data. The text-and-image group achieved higher immediate post-test scores than the video group. No statistically significant group differences were detected in practical task or delayed-test scores, and the exploratory comparison of post-to-retention change was also not significant. Holm correction for three perception comparisons indicated that only perceived clarity remained significantly higher in the text-and-image group. For the materials tested, the text-and-image material provided a stronger basis for immediate learning, although this advantage was not detected in practical performance or delayed outcomes. Because the formats differed in several instructional features, the observed pattern cannot be attributed specifically to avatar presence. The results highlight the importance of pacing, segmentation, visual signaling and learner control when designing AI-supported instructional videos for novice procedural learning. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence (AI) in Education)
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17 pages, 418 KB  
Article
Evaluating the Reliability and Agreement of Rubric-Guided LLM Scoring Versus Human Grading Across Three University Courses
by Howard Kim, Sung-Tae Lee and Jongwon Lee
Appl. Sci. 2026, 16(12), 5902; https://doi.org/10.3390/app16125902 - 11 Jun 2026
Viewed by 731
Abstract
Grading open-ended student work consistently remains a persistent challenge in higher education, and the recent rise of large language models (LLMs) has renewed interest in rubric-guided automated scoring. However, a key gap remains: most studies report correlation rather than agreement, rarely benchmark models [...] Read more.
Grading open-ended student work consistently remains a persistent challenge in higher education, and the recent rise of large language models (LLMs) has renewed interest in rubric-guided automated scoring. However, a key gap remains: most studies report correlation rather than agreement, rarely benchmark models against a local human–human baseline, and seldom test whether simple post hoc calibration improves operational fit. This study addresses that gap by examining whether a rubric-guided LLM can approximate local human grading practice for text-based responses in three university courses, using agreement-oriented rather than correlation-only evidence. A total of 930 student responses from Prompt Engineering, Photoshop Design, and AI Video Production were scored by two human raters and by ChatGPT using the same five-criterion analytic rubric (Accuracy, Logical Flow, Specificity, Quality, and Originality; 0.0–3.0 each; Total 0–15). Human consensus (HC) was defined as the mean of the two human scores and was treated as a pragmatic reference rather than a ground truth. Pairwise agreement among H1, H2, AI, and HC was evaluated using ICC(3,1), Pearson correlations, mean absolute error (MAE), Bland–Altman bias and limits of agreement (LoA); a course-specific held-out calibration analysis was additionally conducted. For the Total score, human–human agreement was strong (ICC = 0.819 [0.797, 0.839]). AI–H1 and AI–H2 Total-score agreement were ICC = 0.700 [0.666, 0.732] and 0.767 [0.739, 0.792], respectively, while AI–HC agreement was ICC = 0.763 [0.735, 0.789], with MAE = 1.603 and LoA = [−4.246, 4.045]. At the trait level, AI–HC ICCs exceeded H1–H2 ICCs for all five rubric dimensions, although Quality remained weakly defined in the human baseline. On a 70/30 held-out test split, a course-specific linear calibration modestly improved Total-score ICC from 0.774 to 0.782 and reduced MAE from 1.624 to 1.215, narrowing the LoA from [−4.290, 4.188] to [−3.157, 3.329]. However, threshold-adjacent agreement remained imperfect after calibration. The principal contribution is a conservative, multi-metric agreement benchmark of rubric-guided LLM scoring against a local human baseline, together with a held-out calibration test that informs deployment. The findings concern written responses only and support a conservative conclusion: rubric-guided LLM scoring can assist human grading under fixed local rubrics, but the current evidence supports calibrated human–AI co-grading rather than unsupervised replacement. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence (AI) in Education)
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