Exploring undergraduate students' perceptions of AI vs. human scoring and feedback
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it