Artificial intelligence and feedback in university education: effectiveness and student perceptions

The integration of generative artificial intelligence (AI) into Higher Education has intensified debates about the role of technology in formative assessment. This study examines the effectiveness and practical comparability of AI-generated feedback in a project-based university course, comparing two large language models (GPT-o4-mini and DeepSeek R1) with feedback provided by an expert human teacher. Adopting a quasi-experimental design, 47 student groups (N = 238) were randomly assigned to one of three feedback conditions. Changes in project performance were analysed using non-parametric tests, robust models, and non-inferiority and equivalence analyses. Students’ perceptions were also assessed through a validated questionnaire (N = 200). Results showed significant improvement in project performance from pre- to post-feedback across all conditions (rrb = 0.77), with no significant differences between feedback sources. Equivalence analyses indicated practical comparability between GPT-o4-mini and teacher feedback, while DeepSeek R1 demonstrated non-inferiority. Students’ perceptions of mastery, emotions, and satisfaction were similarly high across conditions. Findings suggest that feedback effectiveness depends less on its source than on the pedagogical architecture in which it is embedded. When supported by strong assessment literacy and explicit criteria, AI-generated feedback can function as a credible component of formative assessment in higher education.