AI feedback does not motivate by itself: a motivational-meaning model for university physical education
- 1Department of Physical Education, Capital Normal University , ,
Abstract
Artificial intelligence (AI) can convert movement into scores, visual overlays, and corrective prompts, but technical precision does not determine how learners interpret that feedback. We develop a motivational-meaning model for university physical education (PE), integrating self-determination theory, feedback literacy, self-regulated learning, and motor-learning research. Its contribution is a context-specific account of how pedagogical framing, social visibility, and embodied verification organize the interpretation of an AI-feedback event. Informational and controlling meaning are distinguished from subsequent psychological-need experiences. Blocked translation denotes a temporary difficulty establishing a defensible basis for action, rather than a third motivational meaning. The model also proposes self-regulatory appropriation as the transfer of an externally supported feedback process into independent monitoring and adjustment; its distinctiveness from established self-regulation constructs remains an empirical question. Seven proposition groups specify separable predictions concerning framing, actionable information, contestability, evaluative exposure, discrepancy repair, skill transfer, and a secondary pathway to continued participation. The outcome structure distinguishes system engagement, acquisition performance, retention and transfer, affect and well-being, and voluntary physical activity. Continued participation is treated as a distal hypothesis requiring longitudinal evidence and attention to prior activity, health, habit, and opportunity. We outline independent measurement of event interpretation and need experience, tests that separate technical from perceived accuracy, and ethical, multilevel research designs. The model offers testable design principles rather than evidence that AI feedback improves learning or participation.
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BibTeX
@article{wang2026feedback,
title = {{AI feedback does not motivate by itself: a motivational-meaning model for university physical education}},
author = {Xiangyu Wang and Yongwei Wang},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1951652},
url = {https://europepmc.org/article/PMC/PMC13724568},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI feedback does not motivate by itself: a motivational-meaning model for university physical education
AU - Xiangyu Wang
AU - Yongwei Wang
PY - 2026
DA - 2026/10/19
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1951652
UR - https://europepmc.org/article/PMC/PMC13724568
AB - Artificial intelligence (AI) can convert movement into scores, visual overlays, and corrective prompts, but technical precision does not determine how learners interpret that feedback. We develop a motivational-meaning model for university physical education (PE), integrating self-determination theory, feedback literacy, self-regulated learning, and motor-learning research. Its contribution is a context-specific account of how pedagogical framing, social visibility, and embodied verification organize the interpretation of an AI-feedback event. Informational and controlling meaning are distinguished from subsequent psychological-need experiences. Blocked translation denotes a temporary difficulty establishing a defensible basis for action, rather than a third motivational meaning. The model also proposes self-regulatory appropriation as the transfer of an externally supported feedback process into independent monitoring and adjustment; its distinctiveness from established self-regulation constructs remains an empirical question. Seven proposition groups specify separable predictions concerning framing, actionable information, contestability, evaluative exposure, discrepancy repair, skill transfer, and a secondary pathway to continued participation. The outcome structure distinguishes system engagement, acquisition performance, retention and transfer, affect and well-being, and voluntary physical activity. Continued participation is treated as a distal hypothesis requiring longitudinal evidence and attention to prior activity, health, habit, and opportunity. We outline independent measurement of event interpretation and need experience, tests that separate technical from perceived accuracy, and ethical, multilevel research designs. The model offers testable design principles rather than evidence that AI feedback improves learning or participation.
SN - 1664-1078
ER -