Artificial intelligence (AI) has demonstrated significant potential in addressing various challenges within the education sector. For example, it can customize learning materials, sequence activities, provide individualized feedback, predict learning outcomes, and identify students at risk—enabling more responsive and effective teaching and learning.

Despite these benefits, the use of AI in education has faced criticism for overlooking essential learning processes such as motivation, emotion, and (meta)cognition. Many systems are developed without meaningful involvement from domain experts and stakeholders. There is also a widespread reliance on unreliable explainable AI methods to interpret black-box models, while ethical concerns—such as data inconsistencies and algorithmic bias—are often ignored. Moreover, current AI systems tend to overemphasize automated, individualized instruction, frequently neglecting the cultivation of metacognitive and self-regulated learning skills. To retain public sector values, it is crucial to address these major issues. This includes ensuring that AI predictions—such as learners’ performance, grades, and risk of failure—are not only accurate and transparent but also ethically grounded, context-aware, and free from bias. AI systems must be designed in collaboration with educators and domain experts, support self-regulated learning, and provide interpretable, trustworthy insights aligned with real educational needs. Failure to address these concerns could lead to significant disadvantages in educational practice.

Given the growing importance of AI in society and supporting education and the existing challenges in their applications in education, this technical track focuses on AI for education. This technical track expects original research and review articles that combine computer science and informatics ideas with the social sciences. Articles can be within (but are not limited to) the following areas:

• The ethics of AI for education,

• Education data mining and learning analytics,

• Generative AI for education,

• Natural language processing and image processing for education,

• Educational recommender systems,

• Affective computing for education,

• Machine learning, and statistical and optimization methods for education,

• Evaluation of artificial intelligence, adaptive, or personalized educational systems,

• Intelligent tutoring systems, serious games, simulations, and dialog systems for education,

• Multimodal multichannel trace data for AI systems,

• Responsible development and use of AI for education,