Redesigning STEM Higher Education in the Era of Generative AI: From Curriculum Design to Classroom Practice
TL;DR
This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI.
Abstract
Generative artificial intelligence (GenAI) has moved from an emerging educational tool to a structural challenge for science, technology, engineering, and mathematics (STEM) higher education. This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI. Its distinctive contribution is to frame GenAI as a problem of curriculum and assessment validity rather than primarily as a question of tool adoption or academic integrity. Because widely available systems can generate code, solve quantitative problems, summarize literature, draft laboratory reports, and produce fluent scientific prose, conventional submitted artifacts have become weaker indicators of the reasoning and competence they are intended to demonstrate. The review therefore examines the full programme-to-classroom pathway, connecting definitions of graduate competence with course design, classroom and laboratory practice, assessment, feedback, faculty capability, technology adoption, and iterative evaluation. The analysis integrates cognitive load theory, constructive alignment, constructivist perspectives, and frameworks of faculty capability and technology adoption. The biological sciences serve as a recurring disciplinary case because they combine conceptual knowledge, laboratory practice, computational analysis, and ethical decision-making, and are also being transformed by AI-based scientific methods. A worked cell biology example, structured using the Analysis, Design, Development, Implementation, and Evaluation model, operationalizes the review’s conceptual argument and demonstrates how GenAI integration can translate into needs analysis, outcome specification, resource development, blended laboratory implementation, assessment, and iterative redesign. The resulting design logic is generalized into a transferable five-step template for STEM curriculum redesign, with recommendations at programme, course, and institutional levels.