ChatGPT’s feedback credibility and students’ perceived learning: the role of agent-related social perceptions

Abstract

Abstract This paper examines whether students’ agent-related social perceptions of ChatGPT are associated with the perceived credibility of its feedback in academic tasks and whether such credibility, in turn, is related to students’ perceived learning. It also investigates whether the relationship between feedback credibility and perceived learning varies according to students’ sensitivity to the quality of their academic work and their sensitivity to grade-based rewards. To address this, we develop a theoretical model grounded in social perception theory and prior research on students’ experiences with AI-based tools. The model is tested using Structural Equation Modelling (SEM) on data collected from 230 students who completed an academic writing task. The findings reveal that students’ agent-related social perceptions—particularly perceived competence and human-like empathy—are positively connected to the credibility students attribute to its feedback. In turn, perceived feedback credibility is interestingly related to students’ perceived learning in the context of academic writing. Furthermore, the positive link between credibility and perceived learning is weaker among students with higher grade-based reward sensitivity. This research contributes to the educational technology literature by advancing understanding of how students evaluate the credibility of ChatGPT-generated feedback and how such evaluations are associated with learning perceptions. It also shows that agent-related social perceptions play a significant role in students’ credibility judgements. Finally, the findings offer practical implications for educators and students seeking to integrate ChatGPT effectively into academic activities.