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When Do Undergraduate Students Prefer AI? Insights into AI Scoring and Feedback
Seyma N Yildirim-Erbasli1, Munevver Ilgun Dibek2, Mackenzie L Thomas1
1Department of Psychology, Concordia University of Edmonton, Edmonton, AB T5B 4E4, Canada.
Behavioral Sciences (Basel, Switzerland)
|July 28, 2026
Summary
Students prefer AI feedback for structured writing tasks but favor human grading for subjective assignments. They advocate for hybrid approaches where AI supports, not replaces, human evaluation in higher education.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Higher Education Assessment
Background:
- Growing integration of artificial intelligence (AI) in higher education assessment.
- Prior research focused on AI accuracy, neglecting student perceptions.
- Need to understand student preferences for AI scoring and feedback.
Purpose of the Study:
- Examine undergraduate students' perceptions and preferences for AI scoring and feedback.
- Investigate the influence of context, assignment stakes, and reflections on these preferences.
- Identify optimal implementation strategies for AI in higher education assessment.
Main Methods:
- Survey administered to 93 undergraduate students.
- Included Likert-type items and scenario-based questions.
- Students generated AI feedback using ChatGPT for an assignment.
Main Results:
- Students preferred structured, moderately detailed AI feedback for grammar and organization.
- Human evaluation was favored for subjective tasks and higher-stakes assignments.
- Concerns exist regarding AI's ability to assess complex writing aspects.
Conclusions:
- Student preferences for AI in assessment are context-sensitive and task-specific.
- Hybrid models combining AI and human judgment are preferred.
- Transparency and human review are crucial for effective AI implementation.
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