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Applied Sciences
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Generative AI for Intelligent Knowledge Systems and Adaptive Learning
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Generative AI for Intelligent Knowledge Systems and Adaptive Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 3637

Special Issue Editors

Dr. Andrej Flogie

E-Mail Website
Guest Editor
Department of Tehnical Education, University of Maribor, 2000 Maribor, Slovenia
Interests: artificial intelligence; impact of technology on society; education
Prof. Dr. Saša Mladenović

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Science, University of Split, 21000 Split, Croatia
Interests: data mining; machine learning; learning analytics; artificial intelligence; informatics education
Special Issues, Collections and Topics in MDPI journals
Dr. Igor Pesek

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Maribor, 2000 Maribor, Slovenia
Interests: learning technologies; hybrid metaheuristics; optimization algorithms; machine learning

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionizing education through its transformative applications in Educational Data Mining (EDM) and Learning Analytics (LA). This Special Issue of Applied Sciences aims to highlight cutting-edge research on leveraging AI to analyze, interpret, and optimize educational processes. EDM uncovers patterns from educational data, while LA uses data-driven insights to improve learning outcomes and instructional design.

The focus of this Special Issue is to showcase novel AI techniques, algorithms, and tools that enable understanding, prediction, and intervention in educational environments. Topics of interest include, but are not limited to, the following:

  • Machine and deep learning applications in education
  • Predictive modeling of student performance and behavior
  • Personalization of learning paths using AI
  • Visualization of learning data through AI tools
  • AI-powered strategies for student engagement and retention.

We encourage contributions addressing theoretical advances, practical implementations, and interdisciplinary perspectives. By fostering collaboration and innovation among researchers, educators, and policymakers, this Special Issue seeks to build a data-driven, inclusive, and impactful learning ecosystem that harnesses the potential of AI to shape the future of education.

Dr. Andrej Flogie
Prof. Dr. Saša Mladenović
Dr. Igor Pesek
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. 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

  • AI in education
  • At-risk students detection
  • Automatic grading systems
  • Data-driven learning ecosystem
  • Intelligent tutoring systems
  • Personalized learning
  • Predictive modeling
 

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Published Papers (4 papers)

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Research

17 pages, 274 KB  
Article
Generative AI Use Among Slovenian Lower Secondary Students: Use Patterns and Attitudes
by Barbara Arcet, Kosta Dolenc and Eva Kranjec
Appl. Sci. 2026, 16(5), 2539; https://doi.org/10.3390/app16052539 - 6 Mar 2026
Viewed by 423
Abstract
This study examined lower secondary students’ self-reported use of generative artificial intelligence (GenAI) for schoolwork across four modalities (text, image, audio, and video), and tested how attitudes grounded in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of [...] Read more.
This study examined lower secondary students’ self-reported use of generative artificial intelligence (GenAI) for schoolwork across four modalities (text, image, audio, and video), and tested how attitudes grounded in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) relates to usage. A total of 312 grade 7–9 students from Slovenian primary schools participated in the study. The final analytic sample comprised 229 students (47.2% female; Mage = 13.2 years) who reported at least minimal familiarity with GenAI. Students completed an online questionnaire assessing frequency of tool use and five attitude components (teacher support, perceived usefulness, perceived ease of use, experience, and attitudes toward learning with GenAI). Text generation tools were used more frequently than image, audio, or video generation tools. Text tool use was higher among ninth graders than seventh and eighth graders. Text tool use correlated positively with perceived usefulness, perceived ease of use, and experience, and negatively with attitudes toward learning with GenAI. In multiple regression, only perceived usefulness uniquely predicted text tool use, with attitudes explaining 13.7% of variance. Findings indicate that GenAI uptake is currently text-centric and primarily associated with perceived usefulness/performance expectancy, while perceived teacher support (facilitating conditions) shows weak links to use. Full article
(This article belongs to the Special Issue Generative AI for Intelligent Knowledge Systems and Adaptive Learning)
26 pages, 969 KB  
Article
Student Learning Outcome Prediction via Sheaflet-Based Graph Learning and LLM
by Dongmei Zhang, Zhanle Zhu, Yukang Cheng and Yongchun Gu
Appl. Sci. 2026, 16(3), 1658; https://doi.org/10.3390/app16031658 - 6 Feb 2026
Viewed by 496
Abstract
Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale structural dependencies and the rich semantic information embedded in educational materials. [...] Read more.
Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale structural dependencies and the rich semantic information embedded in educational materials. To bridge this gap, we propose EduSheaf—a unified framework that integrates large language models (LLMs) with a sheaflet-based signed graph neural network. Specifically, LLMs are employed to extract fine-grained semantic embeddings from multiple-choice questions (MCQs), thereby enriching graph representations with contextual knowledge. A signed graph is then constructed to encode student–MCQ interactions, where correct and incorrect responses are represented as positive and negative edges. On top of this, a novel sheaflet-based signed graph neural network performs multi-frequency learning through low-pass and high-pass filters, enabling the joint modeling of global consensus and local variations, while sheaf structures enforce edge-level consistency. Extensive experiments on multiple real-world educational datasets demonstrate that EduSheaf consistently outperforms state-of-the-art baselines, including both semantic-enhanced and signed graph models, in terms of prediction accuracy and robustness. Ablation studies further reveal the complementary roles of semantic embeddings and multi-frequency graph filters. Full article
(This article belongs to the Special Issue Generative AI for Intelligent Knowledge Systems and Adaptive Learning)
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24 pages, 2825 KB  
Article
Artificial Intelligence and Learning Gaps: Evaluating the Effectiveness of Personalized Pathways
by Gina Paola Barrera Castro, Andrés Chiappe, Diego Fernando Becerra Rodríguez and Felipe Sepúlveda
Appl. Sci. 2026, 16(3), 1302; https://doi.org/10.3390/app16031302 - 27 Jan 2026
Viewed by 1025
Abstract
The integration of Generative AI (GAI) in education has opened new possibilities for personalized learning, yet its effectiveness in mitigating learning gaps remains underexplored. This study examines the impact of Personalized Learning Pathways (PLPs), generated through AI models (Gemini 2.5 Pro, ChatGPT 5), [...] Read more.
The integration of Generative AI (GAI) in education has opened new possibilities for personalized learning, yet its effectiveness in mitigating learning gaps remains underexplored. This study examines the impact of Personalized Learning Pathways (PLPs), generated through AI models (Gemini 2.5 Pro, ChatGPT 5), on secondary school students’ learning outcomes. Using a short-term longitudinal panel design, the research compares homogeneous instructional strategies with AI-driven personalized learning to assess differences in knowledge acquisition and cognitive skill development. Findings indicate that AI-generated PLPs significantly reduce lower-order learning gaps, though higher-order skills remain challenging. The study also reveals that learning styles influence student engagement with AI-driven education, suggesting that hybrid models combining AI and teacher mediation may optimize outcomes. These findings contribute to the ongoing discourse on AI in education, emphasizing the need for equitable, adaptive, and ethical AI applications in learning environments. Full article
(This article belongs to the Special Issue Generative AI for Intelligent Knowledge Systems and Adaptive Learning)
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23 pages, 2384 KB  
Article
DyGAS: Dynamic Graph-Augmented Sequence Modeling for Knowledge Tracing
by Xiuyun Li, Zihao Yan, Yongchun Gu, Siwei Zhou and Shasha Yang
Appl. Sci. 2025, 15(23), 12767; https://doi.org/10.3390/app152312767 - 2 Dec 2025
Viewed by 873
Abstract
Online learning environments generate vast amounts of student interaction data. While these records capture observable behaviors, they do not directly reveal students’ underlying knowledge states, which are essential for tracking learning progress. Knowledge tracing (KT) addresses this gap by predicting students’ future performance [...] Read more.
Online learning environments generate vast amounts of student interaction data. While these records capture observable behaviors, they do not directly reveal students’ underlying knowledge states, which are essential for tracking learning progress. Knowledge tracing (KT) addresses this gap by predicting students’ future performance on exercises related to specific concepts, thereby enabling personalized learning and intelligent tutoring. Existing deep learning-based KT methods achieve promising results, but they often overemphasize either the sequential evolution of knowledge or the static structural relationships, which does not reflect the dynamic evolution of student learning. Moreover, they fail to model students’ knowledge state accurately under sparse interactions. To overcome these limitations, we propose DyGAS, a dynamic graph-augmented sequence modeling framework for knowledge tracing. The sequential module captures the dynamics pattern of knowledge acquisition and forgetting, while the structural module employs graph convolutional networks (GCN) to model inter-concept dependencies and knowledge transfer. Additionally, we propose that static knowledge modeling provides semantic priors to stabilize the representation of sparse concepts. Empirical results on three benchmark datasets demonstrate that DyGAS achieves superior performance compared to state-of-the-art methods, offering accurate and robust knowledge tracing across diverse learning scenarios. Full article
(This article belongs to the Special Issue Generative AI for Intelligent Knowledge Systems and Adaptive Learning)
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