From Design to Co-Design: Pre-service Science Teachers’ Iterative Lesson Planning with GenAI through a Multiliteracies Lens; insights for educational assessment
Kallia Katsampoxaki-Hodgetts, University of Ioannina - [email protected]
Artificial Intelligence and Innovation in Education: Ethical and Technological Dimensions
AI-Education 2025 Official Conference Proceedings
in https://ai-education2025.gr/?page_id=2080
Abstract- This study examines how generative artificial intelligence (GenAI) can
mediate reflective lesson design among pre-service science teachers. Drawing on the
Learning by Design framework and the pedagogy of multiliteracies, the research
explores how fifteen student teachers engaged in iterative dialogue with GenAI while
redesigning science lessons. Each participant first created a lesson plan in class and
then re-worked it through a recorded series of prompts and responses using AI tools
such as ChatGPT, Copilot, and Magic School. The resulting data corpus, fifteen
samples of prompts with design rationale and reflective responses, was analysed
qualitatively through content analysis guided by the four knowledge processes:
Experiencing, Conceptualizing, Analyzing, Applying. Findings showed that AI-
supported lesson re-design fostered epistemic awareness, multimodal thinking, and a
re-articulation of pedagogical roles. Participants moved from procedural task planning
to design reasoning characterised by reflexivity, ethical sensitivity, and multimodal
expansion of scientific meaning. In parallel, the iterative prompting sequences revealed
how teachers began to reconsider what counts as meaningful evidence of learning,
pointing to the potential of GenAI to shape emerging educational assessment thinking
skills. The paper argues that iterative human–AI co-design constitutes a process-based
design space in which aspects of planning, monitoring, and evaluative judgement
become visible as assessment-relevant forms of reasoning.
Keywords. Science education, Learning by Design, Multiliteracies, Generative AI,
TPACK, Teacher education, Prompt engineering, educational assessment.
Publication Date: 2026-06-17