How Generative AI-Assisted Formative Feedback Is Appraised and Implemented in Online Collaborative Learning

Effective educational feedback is vital for enhancing student learning, yet providing personalized formative feedback in large-class settings remains challenging. Generative Artificial Intelligence (GAI) offers a potential means of scaling formative feedback. Guided by a self-determination theory-based model, this study examines the features, implementation, and pre-service teachers’ perceptions of generative AI-assisted formative feedback (GAI-FF) at a university in China. Participants ( n = 114) received four types of GAI-FF, varying in complexity and focus, across three feedback intervals during a semester. Feedback content and written reflections were analyzed using content analysis, epistemic network analysis, and thematic analysis. Results indicate that (1) GAI can generate formative feedback with distinct cognitive and affective feature patterns; (2) overall implementation of GAI-FF was limited; (3) feedback containing problem identification, suggestions, and solutions was more likely to be enacted, whereas feedback emphasizing affective features was less frequently implemented; and (4) participants expressed mixed perceptions, recognizing the usefulness of GAI-FF for collaborative learning while also noting constraints related to rigidity, accuracy, and practical applicability. These findings suggest that the effectiveness of GAI-FF depends on how it supports learners’ cognitive, motivational, and affective appraisals, which shape subsequent feedback use. The study also highlights the importance of deliberate instructional design and cautious integration of GAI tools in formative assessment practices.