Fairness perceptions of AI in grading systems: Examining how discontent with the status quo and outcome favorability reduce AI reluctance - S. Mo Jones-Jang; Myojung Chung; Jihyang Choi; Nuri Kim; Sangwon Lee (Computers and Education: Artificial Intelligence)
This study investigates students' aversion to AI grading systems compared to human professors, focusing on how dissatisfaction with the current evaluation system and grade outcomes affect fairness perceptions. Drawing from 228 college students in South Korea, the experiment tested three hypotheses: (1) students show AI reluctance, (2) dissatisfaction with the current system mitigates the AI reluctance, and (3) this mitigation is contingent on whether students receive high or low grades. Results confirm a general aversion to AI graders. Yet, students who are dissatisfied with the status quo system displayed increased preference for AI graders, especially when receiving low grades. In contrast, those receiving high grades continued to prefer human professors regardless of dissatisfaction. These findings suggest that students' openness to AI graders is shaped by their discontent with the current systems and their self-interest, influencing fairness perceptions in educational settings.