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Research Article
A study of 385 Chinese athletes found that the use of artificial intelligence (AI) and self-efficacy positively influence psychological well-being, which, in turn, enhances perceived sport performance. Psychological well-being also mediates the relationships between AI/self-efficacy and performance. Findings suggest integrating AI tools with strategies to boost self-efficacy and well-being.
This study examined how AI influences psychological well-being and perceived sport performance among athletes in China. Specifically, it aimed to investigate the direct effect of artificial intelligence use on psychological well-being, the effect of athlete self-efficacy on psychological well-being, the effect of psychological well-being on perceived sport performance, and the mediating role of psychological well-being in these relationships. A quantitative research approach was adopted using a cross-sectional survey design. Data were collected from 385 competitive athletes in China using a structured questionnaire, and analyzed using the Statistical Package for the Social Sciences (SPSS) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicated that artificial intelligence use and athlete self-efficacy both positively influenced psychological well-being, while psychological well-being positively affected perceived sport performance. In addition, psychological well-being significantly mediated the relationships between artificial intelligence use and perceived sport performance, and between athlete self-efficacy and perceived sport performance. These findings implied that both technological and psychological resources were important for enhancing athlete outcomes. The study suggested that sport organizations, coaches, and policymakers should integrate AI-supported systems with strategies that strengthen athletes’ self-efficacy and psychological well-being. Overall, the study concluded that artificial intelligence contributes to sport performance not only through technical support but also through its positive influence on athletes’ psychological functioning.
Artificial intelligence is becoming a major force in contemporary sport, enabling coaches, analysts, and athletes to process large volumes of data for performance monitoring, injury prevention, tactical planning, and personalized training adaptation1. Recent reviews confirm that AI and machine learning are increasingly applied to biomechanical monitoring, workload analysis, rehabilitation, motion tracking, and training optimization, signaling a decisive shift toward data-driven, technology-supported systems of preparation and competition2. Yet, as these technologies become embedded in athletes’ daily routines, scho....
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Ethical considerations
The study was exempt from formal ethical review under the institution's policy as it met the criteria for minimal-risk, anonymous survey research. All procedures were conducted in strict accordance with the ethical standards of the Declaration of Helsinki. Prior to data collection, informed consent was obtained from all adult participants. For participants under 18 years of age, written informed consent was obtained from their parents or legal guardians, alongside written assent obtained from the minors themselves. All respondents were explicitly informed of the research purpose, their voluntary participation, their ri....
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Descriptive statistics (N = 385)
Descriptive statistics were presented to summarize the basic characteristics of the study variables and to provide an initial understanding of the data before conducting advanced statistical analysis. In quantitative research, descriptive statistics are important because they show the average responses of participants and the extent to which responses varied across the sample. In this study, the mean values indicated the general level of agreement of the athletes rega.......
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Before interpreting the substantive findings, it is necessary to clarify certain analytical and methodological boundaries of the present study. This research employs a cross-sectional survey design and analyzes the data via variance-based structural equation modeling (PLS-SEM). Consequently, the authors partitioned the data into model robustness and predictive relevance, which were assessed through bootstrapping (5,000 resamples) and the Stone-Geisser Q2 statistic via the PLSpredict procedure, whi.......
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The author of this article declares no conflicts of interest.
| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| Artificial Intelligence Use Scale (adapted for sport) | Adapted from Abbas et al. [42] | [42] | N/A |
| Athlete Self-Efficacy Scale (multidimensional) | Adapted from Koçak [45] | [45] | N/A |
| IBM SPSS Statistics (Version 28) | IBM Corp., Armonk, NY, USA | https://www.ibm.com/products/spss-statistics | SCR_002865 |
| Perceived Sport Performance Scale | Adapted from Hill et al. [44] | [44] | N/A |
| Pilot test questionnaire | In-house | In-house (instrument) | N/A |
| Psychological Well-Being Scale for Children (PWB-c) – adapted for adult athletes | Adapted from Opree et al. [43] | [43] | N/A |
| SmartPLS (Version 4.2) | SmartPLS GmbH, Oststeinbek, Germany | https://www.smartpls.com/ | SCR_022040 |
| Translation and back-translation procedure (bilingual experts) | In-house (sport psychology researchers) | In-house (procedure) | N/A |