Actionable companion · Motivation & engagement
From AI evidence to responsible practice: Associations between generative AI use frequency, technology acceptance, attitudes toward AI, and reported learning preference patterns among students in physical education classes
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
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): Higher education. Verify the participants, intervention and comparator before applying this study in a new setting.
Original authors’ abstract, reproduced under the linked source license:
Abstract Introduction Physical education combines embodied practice with cognitive learning, yet generative artificial intelligence is increasingly used as an academic support tool. This cross-sectional study investigated associations between self-reported generative AI use frequency, reported VARK learning preference patterns, attitudes toward AI, and Technology Acceptance Model beliefs among undergraduate students in PE classes. Methods A cross-sectional survey was conducted with 1,084 full-time PE undergraduates (age 19–28 years). Participants completed PE-adapted instruments assessing reported learning preferences patterns using the VARK framework, technology acceptance beliefs based on the Technology Acceptance Model, attitudes toward generative AI, and self-reported generative AI use frequency. The TAM and VARK instruments were adapted to PE-relevant academic and movement-related tasks. Data were analyzed using descriptive statistics, reliability testing, ANOVA, and multiple regression models. Results Students reported moderate acceptance of generative AI and positive attitudes toward its academic use. Kinesthetic learning remained the dominant preference, consistent with the movement-based nature of PE. However, higher self-reported AI usage frequency was associated with higher visual and reading-writing scores and lower kinesthetic scores (β = .34–.40; β = −.39, p < .001). Significant differences emerged across learning-style groups for all TAM and AI attitude dimensions (p < .001, η2 = .086–.142). Regression analysis showed that perceived usefulness, ease of use, and positive attitudes toward AI were the strongest predictors of AI usage, explaining 48% of variance. Frequent AI engagement was associated with greater concentration in in reported VARK score patterns (β = −.62, p < .001). Conclusions In this cross-sectional sample, self-reported generative AI use was associated with differences in reported learning preference patterns and with stronger technology acceptance beliefs among higher education physical education students. While kinesthetic scores remained highest overall, more frequent AI use was associated with higher visual and reading-writing scores and lower kinesthetic scores. These findings are associative, not causal, and do not show changes in stable learning preference patterns. Future research should examine whether pedagogically grounded AI integration can support diverse learners, particularly students who report stronger kinesthetic preferences. Keywords: attitudes toward AI, cross-sectional survey, generative artificial intelligence, learning preferences, physical education, technology acceptance model
What kind of evidence is this?
Supporting AI learning science · Survey / observational study
Stage: Higher education. 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
Methods A cross-sectional survey was conducted with 1,084 full-time PE undergraduates (age 19–28 years).
Study methods · source passage
The study sample consisted of 1,084 undergraduate students enrolled in physical education programs across various academic years. Among them, 468 were female and 616 were male, aged between 19 and 28 years (M = 21.03, SD = 1.89). All participants were full-time students with access to digital learning tools and had used at least one educational platform in the past six months. Recruitment occurred during compulsory coursework sessions, where faculty members presented an invitation to participate in the study. Participation was entirely voluntary, and all individuals provided informed consent prior to data collection. Participants retained the right to withdraw at any stage. Data were collected electronically, and only fully completed responses were retained for analysis. Demographic variables were recorded to characterize the sample, and no personally identifiable data were collected at any stage.
Limitations and uncertainty · source passage
The study did not measure instructional effectiveness, academic performance, skill acquisition, or learning outcomes. Therefore, the findings should not be interpreted as evidence for the effectiveness of specific pedagogical strategies or AI integration practices in physical education. The reported relationships should therefore be understood as associations observed at one point in time, rather than evidence that AI use changed students' VARK preference patterns or technology acceptance beliefs. In addition, the study did not objectively verify platform-specific activity, usage duration, prompt histories, interaction quality, or the extent to which AI outputs were integrated into PE-specific learning tasks. Future longitudinal studies should combine repeated VARK or learning-preference measurements with objective AI-use indicators, such as platform logs, AI-use diaries, time-on-task data, or teacher-verified AI-supported assignments, to examine whether these patterns change over time.
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.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
- Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
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. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. 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. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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
Stefan Alecu; Gheorghe Adrian Onea. Associations between generative AI use frequency, technology acceptance, attitudes toward AI, and reported learning preference patterns among students in physical education classes. Frontiers in Sports and Active Living (2026-06-15). https://doi.org/10.3389/fspor.2026.1806871
Companion prepared 2026-11-01. 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.