Professor Yan Zi

Hong Kong Research Grants Council (RGC) Senior Research Fellow
Head, Department of Curriculum and Instruction

The Education University of Hong Kong

Professor Yan Zi is an RGC Senior Research Fellow and the Head of the Department of Curriculum and Instruction at The Education University of Hong Kong. He also holds the position of Honorary Professor at the Centre for Research in Assessment and Digital Learning at Deakin University. His research primarily focuses on three interrelated areas: educational assessment in both school and higher education settings, Rasch measurement, and adaptive learning. Professor Yan is the President of the Pacific Rim Objective Measurement Society and serves as an Associate Editor for Educational Psychology, Computers and Education Open and IEEE Transactions on Learning Technologies. He has been listed among the top 2% most-cited scientists globally by Stanford University since 2021. Professor Yan’s publication, Student Self-assessment as a Process for Learning, is the first systematic account of student self-assessment research, including its conceptualisation, measurement, predictors, relationship with other learning concepts, and impact on learning outcomes and interventions. He edited the book, Assessment as Learning: Maximising Opportunities for Student Learning and Achievement, which updates new theoretical perspectives regarding assessment-as-learning and re-positions it in relation to education reform. He is also the co-author of the 4th edition of the book, Applying the Rasch Model: Fundamental Measurement in the Human Sciences. This book is a classic in the field and a bestseller in academic circles. Beyond his research endeavours, Professor Yan has many years of experience in knowledge transfer, teacher training, and school support. He has conducted over 100 professional development workshops on assessment and/or measurement for principals and teachers from Hong Kong, Macau, mainland China, and overseas.

From Adaptive Systems to Adaptive Learners: Assessment for Student Agency in AI-Enabled Classrooms

This talk explores how assessment-driven, student-centred adaptive learning can help schools develop future-ready and flourishing learners in AI-enabled classrooms. It argues that assessment must extend beyond measurement of attainment to become a formative process that equips students with actionable feedback. When learners use such evidence to recognise misconceptions, set goals, choose strategies, and monitor progress, assessment becomes a foundation for student agency.

The talk frames adaptive learning through two complementary forms of adaptivity. System adaptivity refers to the capacity of digital systems to use assessment evidence to adjust learning paths—for example, by sequencing content, calibrating challenge, recommending resources, and identifying misconceptions. Learner adaptivity refers to students’ capacity to seek and interpret feedback, regulate effort and emotion, make informed learning decisions, and persist through challenge. The talk’s central claim is that AI should not simply personalise learning for students. Its overarching purpose should be to help students become increasingly capable of personalising and regulating learning for themselves.

Drawing on EASE (Efficient Adaptive System for Education), a Hong Kong K–12 adaptive learning platform, the talk illustrates how AI-enabled assessment can connect these two forms of adaptivity. It considers how actionable evidence can support teacher judgement, productive learner–teacher dialogue, and differentiated learning without diminishing teacher agency. The talk concludes with practical principles for using AI-enabled assessment to promote self-regulated learning while safeguarding fairness, well-being, and human relationships in education.

Relevance to Curriculum, Pedagogy & Assessment: This session explores how AI-enabled formative assessment can align curriculum, pedagogy, and assessment to develop student agency and self-regulated learning. It considers how adaptive systems can provide actionable evidence and differentiated pathways, while supporting learners to set goals, select strategies, monitor progress, and make informed decisions about their own learning.