Artificial intelligence (AI) has shown great potential in tackling numerous challenges in the field of education, both at the classroom and school management.  

At the classroom level, AI applications have been designed to support instruction through customizing learning materials, sequencing of learning activities, and providing individualized feedback and scaffolding based on the individual learners’ profiles. In this regard, AI is used to identify resources and pedagogical approaches that are considered appropriate for learners’ needs and can predict potential outcomes and recommend the next steps of the learning process for them. At the school level, AI applications are designed to support both school management and system. Some examples include reducing dropout through predictive analysis and offering timely assessment of new skillsets like higher cognitive skills.  

Despite its benefits, the use of AI applications in education has faced criticism for various reasons, such as the lack of control over their behavior (as they learn from and consider biases in their decisions), the exclusion of practitioner expertise in their design, and the lack of interpretability. Despite these concerns, AI methods are being integrated into public sector education systems through machine learning, natural language processing, image processing, and expert systems.

Improving these systems to retain public sector values involves addressing major issues, including the above-mentioned challenges.  Failing to do so is considered a huge disadvantage as, in practice, learners’ performance, grade, risk of failure, etc., predicted through such AI methods should be accurate, unbiased, and transparent, accompanied with reasons on why a specific feedback, intervention, or pedagogical tool is appropriate for a learner.

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:

• Artificial intelligence for education (AIEd),

• Text mining and web mining for education,

• Education data mining and learning analytics,

• Educational recommender systems,

• Affective computing for education,

• Neural-symbolic AI for education,

• Artificial neural networks, machine learning, and statistical and optimization methods for education,

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

• AI-based adaptivity and personalization for education,

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

• Smart Chatbots for education,

• IoT applications in education,

• Multimodal multichannel trace data for AI systems,

• The ethics of AI for education,