By Ines Oldenburg, Clemens Hillenbrand and Jorge Marx Gómez
ICRAIS 2026: Track Abstract: AI and Education
Artificial Intelligence (AI) plays an increasingly important role in the digital transformation of education across all learning stages, including school education, higher education, professional training, and lifelong learning. This track focuses on technical, algorithmic, and system-oriented perspectives on AI-based educational technologies, while explicitly considering pedagogical and didactical requirements. Contributions are invited that address the development, implementation, and evaluation of AI-driven methods and systems supporting learning, teaching, and assessment. Topics of interest include, but are not limited to, machine learning and data-driven approaches for personalization and adaptation, learning analytics and educational data mining, intelligent tutoring systems, automated assessment, and AI-supported teacher assistance. The track also encourages research on cross-context learning scenarios and the transferability of AI solutions between educational domains, as well as on ethical, explainable, and trustworthy AI for education.
Example Topics (non-exhaustive)
-Machine learning models and algorithms for adaptive and personalized learning systems
-Learning analytics and educational data mining across educational contexts
-Intelligent tutoring systems and AI-based learning companions
-Conversational agents and large language models in educational applications
-Automated assessment, feedback generation, and grading techniques
-AI-based decision support systems for teachers and instructors
-Generative AI for educational content creation and curriculum support
-AI literacy, computational thinking, and skills development for lifelong learning
-Explainable, fair, and trustworthy AI in educational systems
-Data privacy, security, and ethical considerations in AI-supported education
-Evaluation frameworks, benchmarks, and performance metrics for AI in education
- Track Description – ICRAIS Submissions
(Full Papers, Short Papers, Posters)
Track: AI and Education
Artificial Intelligence (AI) is a key driver of innovation in education across all learning stages, including school education, higher education, professional training, and lifelong learning. This track invites Full Papers, Short Papers, and Posters that address AI-based methods, algorithms, and systems for educational applications from a technical and computational perspective, while explicitly considering pedagogical and didactical requirements.
Contributions may present novel models, architectures, and system designs, empirical studies, prototypes, or evaluation results related to AI-supported learning, teaching, and assessment. The track particularly welcomes work that explores the transferability and scalability of AI solutions across educational contexts, as well as approaches that contribute to trustworthy, explainable, and ethical AI in education.
Submissions should clearly articulate their technical contributions, implementation details, and evaluation methodology, as well as their relevance for educational practice and learning processes.
Topics of Interest (non-exhaustive)
-Machine learning algorithms for adaptive and personalized learning
-Learning analytics and educational data mining
-Intelligent tutoring systems and AI-based learning companions
-Conversational agents and large language models for education
-Automated assessment, feedback, and grading systems
-AI-supported decision-making tools for teachers and instructors
-Generative AI for educational content creation and curriculum support
-AI literacy and computational thinking across the lifespan
-Explainable, fair, and trustworthy AI in educational applications
-Data privacy, security, and ethical challenges in AI-driven education
-Evaluation frameworks, benchmarks, and metrics for AI in education
- Short Version – Official Call for Papers Page
AI and Education
This track invites Full Papers, Short Papers, and Posters on AI-based methods and systems for education across school education, higher education, professional training, and lifelong learning. Contributions should address technical, algorithmic, and system-level aspects of AI in education, while considering pedagogical and didactical requirements. Topics include adaptive learning, learning analytics, intelligent tutoring systems, automated assessment, generative AI, and trustworthy AI for education. Submissions presenting novel models, systems, empirical evaluations, or practical applications are welcome.