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Learning with agency: Gamified Physical Education and Cognitive Performance Among Chinese Secondary School Students: Cross-Sectional Moderation Mediation Study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Secondary school. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Background Educators are exploring new methods to educate beyond the classroom as global concerns about students’ cognitive, emotional, and social well-being grow. Physical education (PE) has been demonstrated to boost cognitive and psychological outcomes in several studies. Most research has neglected the benefits of gamification and artificial intelligence (AI)–based feedback in PE, focusing instead on conventional PE formats. The impacts of technologically enhanced PE settings on students’ cognitive performance through feedback and reward mechanisms remain understudied. Objective This study aimed to investigate how intrinsic motivation and AI-based feedback moderated the effects of gamified PE on students’ cognitive performance. Methods The study used a cross-sectional design. In Beijing, Shanghai, Chengdu, and Guangzhou, a total of 1029 public high school students completed a standardized questionnaire. Students in secondary school (male: n=490, 47.6% and female: n=539, 52.4%) aged 10-18 years, were recruited from urban, suburban, and rural locales. Participants were sourced from public, private, and semigovernment schools, reflecting a range of academic achievement levels and access to technology. Students participating in standard PE sessions were included, whereas those with medical conditions that restricted physical exercise were excluded. Data were gathered via standardized questionnaires during designated PE sessions. Gamified PE, cognitive performance, intrinsic motivation, teacher support, collaboration, and AI feedback were examined using standardized instruments. Trained facilitators helped younger participants understand and follow ethical norms. The study used maximum likelihood estimation for structural equation modeling. Bootstrapping was used to analyze mediation and moderation effects at a 5% significance level (α=.05). Results According to structural equation modeling, gamified PE highly predicts cognitive performance (β=.34; P<.001). Other significant factors were teacher support (β=.31; P<.001), physical exercise enjoyment (β=.28; P<.001), and teamwork (β=.26; P<.001). AI-based feedback strengthened the link between gamified PE and cognitive performance under moderation analysis (β=.18; P<.001). Mediation analysis indicated that intrinsic motivation partially mediated the relationship, resulting in a significant indirect effect (β=.21, 95% CI 0.12-0.31; SE=0.05). Conclusions This research integrates gamified PE with AI-based feedback mechanisms to evaluate students’ cognitive outcomes, a domain that has been rarely investigated experimentally. This study highlights the combined effect of intrinsic motivation and AI-generated feedback in a technology-enhanced PE context, in contrast to previous research that primarily focuses on traditional PE methods or isolated gamification elements. The findings enhance the field by demonstrating that student-centered, feedback-rich PE environments may improve cognitive abilities through social interaction, enjoyment, and instructor support. AI-assisted, gamified PE programs may enhance learning outcomes and academic performance among secondary school students.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Secondary school. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Participant ages · source passage

Students in secondary school (male: n=490, 47.6% and female: n=539, 52.4%) aged 10-18 years, were recruited from urban, suburban, and rural locales.

Study methods · source passage

This study is based on cognitive load theory (CLT) and self-determination theory (SDT), with contextual support from Constructivist Learning Theory. Deci and Ryan’s [71] self-determination theory explains how students’ sense of enjoyment in connectivity, autonomy, and competence motivates them to pursue diverse activities. SDT is helpful for this study because it shows how intrinsic motivation moderates the relationship between gamified PE and cognitive function. Gamification elements, such as points, challenges, levels, and social engagement, help PE programs fulfill students’ psychological needs and encourage enthusiastic participation. As gamified PE becomes more engaging and personalized, students’ cognitive engagement, memory, and problem-solving abilities are expected to improve [72]. CLT suggests that gamification and other instructional methods may increase working memory and decrease cognitive load, particularly when combined with real-time AI-based feedback [73]. Such feedback may help students focus, repair mistakes quickly, and learn more via adaptive responses by strengthening the gamified PE-cognition link. According to Constructivist Learning Theory, teachers can direct students’ exploration, social interaction, and feedback, while students can also help themselves through peer collaboration and instructor support [74]. Teamwork and teacher facilitation are highly effective in promoting engagement and the application of cognitive skills in PE.

Limitations and uncertainty · source passage

This study has made many essential contributions. However, there are some limitations. Although the research used a large and diverse sample from 4 metropolitan cities, its cross-sectional design limits causal inferences. SEM and bootstrapping provide more compelling findings; however, experimental or longitudinal designs are necessary to determine the long-term impact of gamified PE and AI feedback on cognitive function. Second, self-report assessments may be biased by social desirability or cognitive misunderstanding, even when administered under supervision to children aged 10-12 years. Future studies could triangulate findings and improve measurement validity by using multi-informant data, such as behavioral observations or teacher ratings. The generalizability of the results is another issue. The sample included students from urban and suburban public, private, and semigovernment schools, but not rural or low-tech institutions. This raises the question of how AI-based gamified systems can effectively serve diverse socioeconomic and geographical conditions. Future research should examine how infrastructural variations affect such efforts and the digital divide. Although this study focused on cognitive function, it did not examine emotional regulation, academic resilience, or physical health. Future multidimensional research should examine how gamified and AI-supported instructional techniques affect broader student capacities. This study focused on AI feedback as a moderator and intrinsic motivation as a mediator. Other factors, such as self-efficacy, goal orientation, and support from family and friends, influence students’ attitudes toward gamified learning. Future studies should incorporate additional mediators and moderators to gain a deeper understanding of the complex interplay among human, systemic, and technological factors. In conclusion, qualitative research methods, such as student interviews or classroom ethnographies, can complement quantitative approaches by providing contextual insights into learners’ experiences and preferences. By addressing these limitations, future research may enhance the theoretical and practical understanding of how gamified, technology-enhanced education can improve student learning and growth.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Jingtong Zhang; Sae-Sook Oh; Yaru Xu. Gamified Physical Education and Cognitive Performance Among Chinese Secondary School Students: Cross-Sectional Moderation Mediation Study. JMIR Serious Games (2026-02-02). https://doi.org/10.2196/81086

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.