Enhancing learner-centered feedback with AI: teachers’ practices and perceptions
Ahmad Ari Aldino, Bhagya Maheshi, Yuheng Li, Ying Zhou, Yi‐Shan Tsai, Dragan Gašević, Guanliang Chen · Assessment & Evaluation in Higher Education · 2026
Learner-centered feedback, emphasising future improvement, sensemaking, and student agency, has been increasingly recognised as effective, yet remains challenging for educators to offer in large classes with diverse learner needs. Recent advances in generative artificial intelligence (GenAI) offer new ways to support feedback practice, but limited research has examined how teachers use and perceive GenAI in real-world feedback practices, particularly for learner-centered feedback. Twenty-one higher education teachers were recruited to provide written feedback on a student presentation and then use a GenAI-powered feedback tool to analyse their feedback to identify learner-centered components (using BERT) and to generate enhanced drafts (using ChatGPT), and to explore their perceptions of its use. We analysed how teachers engaged with the BERT’s classification of teacher-written feedback text into learner-centered components, its suggestions to address missing components (which teachers could adopt or reject), and how teachers revised ChatGPT-enhanced feedback text. Findings indicated that teachers frequently adopted BERT’s suggestions and extensively revised ChatGPT outputs, often moderating praise, encouragement, and relationship-building statements. Interviews indicated that teachers valued GenAI for identifying missing components, improving language and structure, and promoting reflection, while also noting concerns about tone, trust, and the need for human editing.