REIMAGINING FEEDBACK THROUGH GENERATIVE AI IN ENGINEERING EDUCATION

Abstract

The rapid integration of Generative AI (GenAI) into higher education presents both opportunities and systemic challenges, particularly in domains where feedback is central to learning. This study investigates the capacity of a large language model to generate formative feedback for student-created UML diagrams in a university software engineering course. Across two cohorts (N = 262), AI-generated, teacher-generated, and no-feedback conditions were compared, analyzing student perceptions, learning outcomes, and grading reliability. Results show that while students rated GenAI feedback as beneficial and often comparable to human comments, teacher feedback remained more effective in supporting performance gains, especially in complex modeling tasks. Linguistic analysis further revealed that GenAI feedback was more repetitive and less pedagogically rich than human feedback. Beyond these course-level findings, the study highlights broader implications for higher education systems. GenAI feedback represents not just a pedagogical tool but a cognitive partner that can reshape assessment models, curriculum design, and faculty roles. Its scalable nature offers potential to democratize access to high-quality formative feedback, while also raising equity, accountability, and policy challenges at institutional and sectoral levels. By situating empirical results into this broader frame, the study argues that GenAI is catalyzing a paradigm shift toward new systems of learning, where feedback becomes systemic, scalable, and embedded in the very structure of higher education.

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