Student reactions to AI versus human feedback in teamwork skills assessment
Bibliographic record
Abstract
Abstract As AI technologies become increasingly integrated into education, this research investigates how students react to AI-generated versus human feedback in teamwork skills assessment. In Study 1, 108 students completed a virtual teamwork simulation and received assessment feedback framed as either AI- or human-generated. Students showed a clear preference for human feedback over AI feedback, revealing a bias against machine-generated evaluations. Study 2, a scenario-based experiment involving 322 students, confirmed these findings and tested whether enhancing AI feedback with credibility and empathy cues could improve perceptions. These enhancements significantly improved reactions to AI feedback, and when both credibility and empathy were emphasized, reactions approached those for unenhanced human feedback. However, even with enhancements, AI feedback did not fully match the positive perceptions of human feedback. These findings highlight the need for thoughtful design to mitigate biases against AI feedback and suggest that blending AI and human feedback offers an effective approach for improving acceptance and engagement in educational contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".