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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: 31 January 2026 | Viewed by 763

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 (1 paper)

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Research

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 317
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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