Accounting for student use of generative artificial intelligence in STEM education

Abstract

Generative Artificial Intelligence (GenAI) is increasingly embedded in Science, Technology, Engineering, and Mathematics (STEM) education, offering opportunities to support learning through personalised assistance, improved efficiency, and enhanced accessibility. At the same time, its widespread adoption raises ethical, sustainability, and pedagogical challenges, including academic integrity, algorithmic bias, and the potential erosion of higher-order thinking skills. Although these challenges are widely recognised, lecturers lack practical guidance for redesigning courses to accommodate students' GenAI use. This research addresses this gap by developing ethical and sustainability-oriented design guidelines for course design. Using a Design Science Research methodology, the study combined a comprehensive literature review with two rounds of semi-structured expert interviews and an example course redesign. The research resulted in ten design guidelines that support educational institutions in changing courses to account for GenAI use by students. Central themes include rethinking foundational knowledge and skills, supporting higher-order thinking skills (HOTS), enhancing AI literacy, adopting authentic and process-oriented assessment, and ensuring fair and human-centred learning environments. The findings further demonstrate that successful implementation depends not only on course-level redesign but also on institutional governance, clear AI policy, and collaboration. This study contributes to AI in Education (AIED) by providing an artefact that supports course managers in adapting STEM courses to student GenAI use while preserving constructive alignment. By integrating ethical and sustainability considerations, the proposed guidelines offer a practical foundation for responsible GenAI adoption.