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 48
Special Issue Editors
Interests: artificial intelligence; impact of technology on society; education
Interests: data mining; machine learning; learning analytics; artificial intelligence; informatics education
Special Issues, Collections and Topics in MDPI journals
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
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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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