Investigating Lasting Effects of Real-Time Feedback on Originality and Evaluation Accuracy
Bibliographic record
Abstract
Previous research has highlighted the benefits of real-time automated feedback in enhancing originality in divergent thinking tasks. In this preregistered study, we sought to replicate these findings, investigating whether improvements in creative ideation persist after feedback is discontinued, and assess the impact on evaluation accuracy. A total of 230 participants were given three divergent thinking tasks (Alternate Uses tests), with or without semantic distance feedback in the first two trials. The third task was always performed without feedback. Participants were then asked to rate the originality of the ideas they produced in this last trial. Their evaluations were compared against originality scores calculated based on semantic distance and Large Language Models (LLM) for converging evidence. The results aligned with previous findings, showing that feedback was effective in improving overall levels of originality across the first two trials. Importantly, this effect carried over to the third trial after feedback was discontinued. However, feedback did not enhance evaluation accuracy, as participants in both conditions achieved relatively high levels of accuracy in rating the originality of their own ideas. We offer possible explanations for this unexpected result and discuss the study’s findings in the broader context of metacognition.
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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 | 0.005 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".