When Do Undergraduate Students Prefer AI? Insights into AI Scoring and Feedback

The integration of artificial intelligence (AI) into higher education assessment has prompted growing interest in how students perceive and prefer AI involvement in scoring and feedback. While prior research has largely focused on technical performance and accuracy, this study aims to fill a gap in the literature by examining students' preferences and perceptions regarding AI scoring and feedback, with particular attention to context, assignment stakes, and post-evaluation reflections. Ninety-three undergraduate students completed a survey consisting of Likert-type items, scenario-based questions, and an activity in which they generated AI-based scoring and feedback using ChatGPT. Results showed that students preferred structured, moderately detailed AI feedback, particularly for grammar and organization, but generally favoured human evaluation, especially for subjective tasks. While AI was seen as useful in lower-stakes contexts, concerns remained about its ability to assess more complex aspects of writing. Participants expressed a strong preference for hybrid approaches in which AI augments rather than replaces human judgment, along with a need for transparency and opportunities for human review. Collectively, these findings highlight that undergraduate students' preferences are highly context-sensitive and role-specific, underscoring the importance of student-centred implementation strategies of AI in higher education.