Surfing the Tech Wave: A Mindset Intervention to Boost AI Adoption and Creativity
Bibliographic record
Abstract
Artificial intelligence and its benefits have taken the world by storm, with a myriad of novel technologies boasting millions of users daily and an outpouring of research illustrating the significant contributions of AI toward organizational processes and outcomes. Despite this enthusiasm, an overwhelming consensus against the usage of AI has developed in tandem, wherein numerous individuals have cautioned the crippling effects of AI on human creativity. Accordingly, to address these competing tensions, we identify threat to human creativity as a novel barrier to AI adoption and demonstrate how a creative growth mindset intervention efficaciously overcomes this hurdle. Through four pre-registered laboratory, lab-in-the-field, and field intervention studies (SN = 1,063), we show that fostering a creative growth mindset increases adoption of AI, both in intention (Studies 1–2) and real behavior (Studies 1 and 3). Furthermore, we find individuals become more creative in problem-solving tasks when they employ AI to a greater extent, indicating that AI adoption serves as the causal mechanism linking creative growth mindset intervention to augmented creative performance (Studies 2–4). In testing that AI threat is inherently about human creativity, we also document the positive impacts engendered by creative growth mindset (Studies 1–3)—more so than growth mindset in other domains—on a wide range of attitudinal (e.g., trust) and behavioral consequences (e.g., donation) toward AI, as well as rule out alternative mechanisms (Study 2). Taken together, our research highlights the power of creative growth mindset intervention in driving AI adoption and the associated performance benefits of incorporating AI technologies in creative work.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".