Formative and summative feedback are both essential components of the educational process, each serving distinct purposes. While summative feedback provides a retrospective evaluation of student performance – typically in the form of grades or scores – formative feedback can play a crucial role in supporting ongoing learning by offering timely, specific, and actionable guidance. It can help students identify knowledge gaps, adjust their learning strategies, and progress toward their academic goals. However, particularly in higher education, the demand for individualised and constructive formative feedback often exceeds the time and resources available to educators. This contribution explores the potential of artificial intelligence (AI) to support formative feedback processes in higher education. Specifically, we examine whether AI-generated feedback can enhance learning outcomes and foster student engagement, and under which conditions AI-based tools are capable of delivering detailed, criteria-oriented feedback on written assignments – thus supporting iterative learning and revision. Our analysis draws on recent case studies from educators who have implemented AI-supported feedback in their teaching, as well as on our own experiences, observations, and reflections. We explore both the affordances and limitations of current tools, with particular attention to issues of trust, transparency, and pedagogical value. While AI has the potential to streamline routine feedback tasks and reduce administrative workload, we emphasise the continued importance of human oversight to ensure quality, relevance, and alignment with learning objectives. Therefore, educators must develop specific AI competences to adapt and contextualise these tools effectively within their teaching practices. We also reflect on how students perceive and respond to AI-generated feedback, and whether they regard it as credible, useful, and supportive of their learning. In addition, we consider the integration of AI in digital assessment platforms such as Moodle, where it can be used to provide immediate, targeted feedback on closed-format tasks. We conclude that, when thoughtfully implemented and combined with human expertise, AI tools can enhance formative feedback practices in higher education, increase feedback efficiency, and contribute to more responsive and student-centred learning environments. This contribution adds to the emerging discourse on AI-supported feedback in higher education – an area full of potential yet still in its early stage.

Keywords: Artificial Intelligence (AI), formative feedback, higher education, student engagement, personalised learning.