From extrinsic to intrinsic motivation: Testing an AI-powered motivational interviewing system to foster prosocial motivation
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Bibliographic record
Abstract
Scalable interventions promoting sustained behavioral change are crucial for addressing societal issues, yet traditional approaches often require intensive one-on-one therapy. We developed and tested Intrinsic AI, a motivational interviewing chatbot built on GPT-4 and tuned using self-determination theory principles, to increase prosocial behavior. In a preregistered randomized controlled trial (N=237), participants either engaged in a 15-minute conversation with Intrinsic AI about becoming more prosocial or talked freely with an unmodified version of GPT-4. We measured changes in motivation using validated self-report scales and assessed prosocial behavior through an effort-based decision-making task where participants chose between exerting cognitive effort for themselves versus charity. Compared to controls, participants who interacted with Intrinsic AI showed greater increases in motivational readiness as assessed by the motivational interviewing ruler, reporting that becoming prosocial was more important to them, that they felt more confident in their ability to change, and that they were more ready to begin. However, this motivational gain did not persist at 24-hour follow-up, translate into trait level changes in motivation, or influence prosocial effort in a behavioral task. Our findings demonstrate that theoretically grounded AI chatbots can effectively increase short-term prosocial motivation and suggest that a single brief interaction may be insufficient for creating lasting motivational change or impact actual prosocial behavior. This work provides a proof-of-concept for automated motivational interviewing while highlighting the need for more sustained AI-human interactions to achieve durable behavioral change. • Developed chatbot based on GPT-4 using motivational interviewing principles • Intrinsic AI increased short-term prosocial motivation in a randomized trial • Gains in importance, confidence, and readiness were not sustained after 24h • No impact on trait motivation or actual prosocial behavior was observed • Demonstrates potential for scalable AI-based motivational interventions
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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 | 0.001 | 0.001 |
| 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