About the speaker
Dr. Katsampoxaki-Hodgetts is a researcher at the University of Crete has an extensive experience in teacher training and capacity building across educational levels, supporting educators in designing inclusive, reflective, and multimodal assessment practices. She currently serves as the UoC Innovation Leader for the INGENIUM European University Alliance and is coordinating the launch of a new European MOOC on assessment. Her research focuses on inclusive student-centred pedagogies, assessment as learning, learning by design, and generative AI in language and science education. Dr. Katsampoxaki-Hodgetts has authored and co-authored numerous books, textbooks, and edited volumes on educational assessment, multiliteracies, curriculum design, and AI-enhanced pedagogy, and her research has been published in international journals and conference proceedings.
About the keynote
Educational Assessment in the Age of AI: Student Agency, AI Feedback, and the Reframing of Learning
This keynote brings together insights from four interconnected empirical studies examining how assessment is being reconfigured across educational levels as generative AI enters learning, feedback, and design processes. Rather than framing AI as a technical threat to linguistic assessment, the keynote addresses questions that are central to language education: How do learners recognise and articulate their own intellectual contribution in AI-mediated tasks? What forms of meaning-making remain educative and resistant to automation? And how might assessment move from the evaluation of final products toward the documentation of learning processes, judgement, and agency?
Drawing on research conducted in higher education and pre-school contexts, the keynote synthesises evidence from analytical language tasks, student reflections on AI-generated feedback, enacted AI literacy in multimodal learning environments, and co-design practices framed through the RAIL framework. Across contexts, learners consistently valued assessment practices that foreground reasoning, dialogue, interpretive responsibility, and reflective decision-making rather than surface linguistic performance.
The keynote also draws on the CRAM model (Collaborative Reflective Assessment Model), implemented through three-stage exams combining individual work, collaborative sense-making, and teacher feedback. In AI-rich classrooms, CRAM supports Assessment as Learning by making thinking visible, socially accountable, and open to reflective comparison.
The studies presented argue for a shift toward Assessment as Learning: multimodal, dialogic, and centred on learner agency. The keynote invites language teachers to reconsider narrow, monomodal assessment traditions and shares practices that remain educationally meaningful, inclusive, and human in AI-rich classrooms.
Keywords:
educational assessment; Assessment as Learning; AI literacy; feedback literacy; student agency; collaborative assessment; multimodal language education
