TRACK INTRODUCTION
This track examines how AI can be designed, deployed, evaluated, and governed to strengthen teaching, learning, assessment, and educational administration. We are interested in intelligent tutoring systems, generative AI in teaching, learning analytics, personalized and self-regulated learning, automated feedback, knowledge tracing, learning-gap detection, and AI-assisted instructional design. We are also interested in what happens above the classroom. How is curriculum being redesigned, and what does AI literacy mean as a graduate attribute? What is replacing assessments that no longer work, authentic tasks, oral examinations, programmatic designs, and what is happening to academic integrity policy? Where has AI entered admissions, timetabling, student support, quality assurance and procurement, and with what results? And what is being done for staff, whose roles and workloads are changing whether anyone planned for it? Questions of ethics and governance run through all of this: institutional policy, accountability for automated decisions, regulatory compliance, data protection and consent, surveillance and proctoring, fairness, explainability, and access in low-resource and multilingual settings. We welcome demonstrations of working systems, and we ask presenters to be clear about evidence, context and limitations.
THE TOPICS INCLUDED BUT NOT LIMITED TO THE FOLLOWING
• AI-driven personalized learning
• Student success prediction models
• AI-based assessment tools
• Learning gap detection
• Automated curriculum design
• AI ethics in education
• Intelligent classroom management
The manuscript should be submitted via the Electronic Submission System or by email to icbdeconference@163.com no later than the submission deadline of August 30, 2026. When submitting, please select Track 4 as the designated track.
TRACK CHAIR
Prof. Yanguo Jing
Leeds Trinity University, UK
Bio: Professor Yanguo Jing is a Professor of Artificial Intelligence, a Principal Fellow of the Advance HE (PFHEA), a Fellow of the British Computer Society, a Certified IT Professional and a member of IEEE. His prime research interests are applied Artificial Intelligence. His recent research work focuses on the use of machine-learning to capture interaction and user behaviour patterns that can be used to develop intelligent applications. His research has been applied in Education, Manufacturing, HR, business, sports science, and assisted living settings. He had led significant research and secured funding from the likes of the Innovation UK, Cadent Gas, Welsh Government, UK’s Comic Relief charity and JISC, etc.