ai-generated-feedback
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- PaperAssessment & Evaluation in Higher Education8 Jul 2026
Artificial intelligence and feedback in university education: effectiveness and student perceptions
Valentina Grion, Beatrice Doria, Daniele Agostini, Giorgia Slaviero
A quasi-experimental study with 238 students compared AI-generated feedback from GPT-o4-mini and DeepSeek R1 against expert human feedback in a project-based university course. All feedback conditions led to significant and equivalent improvements in project performance, with no differences in student perceptions. The findings indicate that the pedagogical context, rather than the feedback source, determines effectiveness, supporting AI's role in formative assessment when strong assessment literacy is present.
Original abstract
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.
- PaperAssessment & Evaluation in Higher Education29 May 2026
When technological momentum overshadows pedagogical alignment: a systematic review of AI-generated formative feedback in higher education
Ezgi Çallı, Erkan Er
This systematic review of 103 studies (2020–2025) finds that AI-generated formative feedback in higher education has expanded rapidly, but often lacks deep pedagogical alignment. Positive learner perceptions and efficiency gains are common, yet feedback quality evaluations remain indirect and short-term. The review identifies a structural alignment challenge across pedagogical theory, system design, and practice, arguing that long-term value depends on principled coordination rather than technical sophistication alone.
Original abstract
Providing timely and pedagogically meaningful formative feedback remains a persistent challenge in higher education. Advances in generative artificial intelligence (AI), particularly large language models (LLMs), have accelerated research on automating and augmenting feedback processes. This systematic review synthesises 103 empirical studies published between 2020 and 2025 to examine how AI-generated formative feedback is conceptualised, implemented, and evaluated in higher education. The analysis reveals rapid technological expansion, with AI most commonly positioned as a supplementary assistant to enhance feedback efficiency and scalability. While studies frequently report positive learner perceptions and improvements in feedback-related outcomes, evaluations of feedback quality are often indirect and grounded primarily in short-cycle interventions and perceptual measures. Theoretical grounding is uneven and instructor involvement often remains supervisory. Drawing on these patterns, the review identifies a structural alignment challenge across three interdependent layers: foundational pedagogical theory, system design, and interactional practice. The findings suggest that the long-term educational value of AI-generated formative feedback depends less on technical sophistication alone than on principled coordination between pedagogical intent, technological architecture, and human-AI collaboration. The review clarifies structural patterns in the literature and outlines priorities for theory-informed and context-sensitive implementation.