The increasing presence of Artificial Intelligence (AI) in higher education has sparked discussion on its role in supporting student learning. While much attention has been given to AI-generated writing and automated assessment, less focus has been placed on how AI can help students engage with feedback effectively. Feedback is essential to student development, guiding them toward improvement and deeper understanding. However, many students struggle to interpret written comments and identify concrete steps for improvement, leading to missed learning opportunities.

Summative assessment plays a crucial role in higher education, providing students with structured feedback on completed work. However, this feedback is often underutilised, as students may lack the understanding or confidence to act on it effectively. Difficulties in engaging with feedback and reflecting on one’s own practice are common challenges across higher education. Developing these skills is essential, yet many students find it difficult without structured guidance. Traditional feedback mechanisms, including written comments and external tutoring services, can be static, delayed, or insufficiently interactive, leaving students without timely support in revising their work for future improvement.

This paper explores an alternative approach: an AI-Tutor designed to help students understand and act on feedback from summative assessments. Rather than assisting before grading or generating content, the AI-Tutor facilitates structured conversations that guide students through their feedback, helping them clarify instructor comments, recognise areas for improvement, and develop a deeper understanding of assessment expectations. By employing a question-and-answer modality, the tutor supports the development of feedback literacy, encouraging students to reflect critically on their work and make informed decisions about how to enhance it in future assessments.

This conceptual work is initially being explored in the context of Computer Science education, where structured problem-solving, technical precision, and iterative refinement are key aspects of learning. The paper details the design and interaction model of the AI-Tutor, along with its alignment to pedagogical best practices. A key focus is maintaining student agency by ensuring that AI serves as a scaffold rather than a substitute for critical thinking. The discussion also considers the broader implications of AI tutors in education, particularly in fostering independent learning and improving engagement with feedback.

As AI becomes a more common part of academic workflows, understanding its role in student learning remains an open question. This work examines how more conversational AI tutors can support students in engaging with summative assessment feedback, ensuring that AI remains a tool for guidance rather than content generation. The findings contribute to the ongoing discussion on AI’s role in education and its potential to support students in developing essential academic and professional skills.

Keywords: Artificial Intelligence (AI), Generative AI, Education Technology, Feedback Literacy, AI-Assisted Learning.