Artificial intelligence and feedback in university education
Effectiveness and student perceptions
Bibliographic Data
| ID | 21494064 |
|---|---|
| Authors | Valentina Grion (0000-0002-2051-1313, Department of Human, Education and Sport Sciences, Pegaso Telematic University), Beatrice Doria (0000-0002-3894-9460, Department of Human, Education and Sport Sciences, Pegaso Telematic University, corresponding author), Daniele Agostini (0000-0002-9919-5391, University of Trento), Giorgia Slaviero (0009-0000-9312-4683, University of Padua) |
| Year | 2026 |
| Pages | 1-20 |
| Publication date | 2026-07-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Assessment & Evaluation in Higher Education (JOURNAL) |
| Journal identifiers | ISSN: 0260-2938 • E-ISSN: 1469-297X |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/02602938.2026.2697962 |
| OpenAlex | W7167814969 |
| Language | EN |
| References cited | 41 |
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
Comparability · Formative assessment · Higher education · Literacy · Locus of control · Perception · Intelligent Tutoring Systems and Adaptive Learning · Psychometric Methodologies and Testing · Student Assessment and Feedback
Students’ voices on generative AI
Effects of self-assessment on self-regulated learning and self-efficacy
A systematic review of AI-based automated written feedback research
Developing effective assessment feedback
Evidence‐based multimodal learning analytics for feedback and reflection in collaborative learning
Student – Feedback Interaction Model
Assessment literacy and student learning
Self-assessment is about more than self
The power of internal feedback
Feedback on feedback practice
Faculty assessment development in higher education
The challenges of feedback in higher education
Self-regulation in open-ended online assignment tasks
Systematic review of research on artificial intelligence applications in higher education – where are the educators
Teacher assessment literacy in practice
Teacher assessment literacy
The development of student feedback literacy
The Power of Feedback
| Citation velocity | historical |
|---|---|
| Highly cited | No |