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.
Çallı et al. (Fri,) studied this question.