Responsible Use of AI-Generated Feedback and Sustainable Learning Engagement Among Chinese University Students: A Dual-Path Model of Feedback Literacy and Feedback Dependence

Abstract

As generative artificial intelligence (GenAI) becomes increasingly embedded in university learning, AI-generated feedback offers more accessible and efficient support. However, its educational value depends on how students evaluate and process the feedback they receive. From the perspective of responsible use of AI-generated feedback, this study develops a capability–risk dual-path model linking Student Feedback Literacy (SFL) and Dependence on AI-Generated Feedback (AIFD) to self-reported Sustainable Learning Engagement (SLE) through Critical Engagement with AI-Generated Feedback (CEAF) and Uncritical Acceptance of AI-Generated Feedback (UAAF). Survey data from 368 Chinese university students were analyzed using structural equation modeling and bootstrap analysis of indirect associations. Higher SFL was associated with greater CEAF and lower UAAF, whereas higher AIFD showed the opposite pattern. CEAF was positively associated with SLE, while UAAF was negatively associated with SLE. Significant indirect associations with SLE were also observed through both feedback-processing pathways. These findings suggest that the educational implications of AI-generated feedback should be considered not only in terms of efficiency but also in terms of whether students remain actively involved in evaluating, reflecting on, and selectively using AI suggestions rather than becoming overly reliant on them.