Exploring undergraduate students' perceptions of AI vs. human scoring and feedback

Bibliographic Data

ID21652997
AuthorsMackenzie L Thomas (Concordia University of Edmonton, corresponding author), Seyma Nur Yildirim‐Erbasli (0000-0002-8010-9414, Concordia University of Edmonton), Seyma N Yildirim-Erbasli, Shruthi Hariharan
Year2026
Volume68
Pages101052
Publication date2026-01-01
Open AccessYes
TypeARTICLE
VenueThe Internet and Higher Education (JOURNAL)
Journal identifiersISSN: 1096-7516 • E-ISSN: 1873-5525
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.iheduc.2025.101052
OpenAlexW4414030788
LanguageEN
Citations received6
References cited32

The use of artificial intelligence (AI) in educational assessment offers scalable solutions to traditional grading challenges, yet concerns about reliability, fairness, and acceptance remain, particularly in subjective domains like writing. This study examines undergraduate students' perceptions of AI-generated scoring and feedback compared to human evaluators. Participants reviewed scores and feedback provided by either AI or a human and completed a survey measuring their perceptions before and after disclosure of the source. Analyses revealed that students often struggled to accurately identify the evaluator. Additionally, while perceptions of AI scoring and feedback were generally moderate, exposure to AI significantly reduced students' confidence in AI scoring. The source of the grading and identification accuracy significantly influenced students' perceptions. Human grading was associated with more positive perceptions, while incorrect identification—when not combined with human grading—also led to more positive perceptions. However, the interaction of human grading and incorrect identification resulted in more negative perceptions. Factors such as comfort with technology, familiarity with AI, and frequency of AI use were significant predictors of students' attitudes toward AI. These findings enhance our understanding of student attitudes toward AI in educational assessment and emphasize the importance of thoughtful implementation to support acceptance in educational contexts

Mathematics education · Medical education · Perception · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Medicine · Neuroscience · Psychology

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