Generative artificial intelligence as an enabler of student feedback engagement: a framework

Abstract

Despite the recognised importance of feedback in enhancing student learning, feedback practices in higher education have not achieved the expected effects. A primary issue lies in student disengagement, exacerbated by contextual constraints such as large classes and limited curriculum space and time. The advent of Generative Artificial Intelligence (GenAI) may help overcome these contextual constraints. However, GenAI also poses substantial challenges and ethical dilemmas during the feedback process. Meanwhile, it is essential to recognise that the feedback environment created by GenAI inevitably interacts with students’ personal factors, especially their feedback literacy, to jointly influence feedback engagement. Therefore, a question remains whether GenAI can be an effective enabler of student feedback engagement. To answer the question, based on a literature review and theoretical synthesis, we scrutinise student engagement with GenAI in three stages of the feedback process and discuss the interplay of student feedback literacy and the GenAI context. We suggest that the extent to which students are engaged with feedback depends on their degree of feedback literacy as orchestrated in the GenAI context. Finally, we propose a cyclical feedback framework consisting of feedback forethought, feedback control and feedback retrospect to enable student feedback engagement in a GenAI world.

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