Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: The Paradox of AI Empowerment in Primary School Physical Education: Why Technology May Hinder, Not Help, Teaching Efficiency

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): Primary school. Verify the participant population and task before transferring the findings.

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

This study investigates why artificial intelligence (AI) may hinder rather than enhance teaching efficiency in primary school physical education (PE). Guided by socio-technical systems theory, we conducted focus group interviews with 13 PE teachers (6 from Nanjing and 7 from Chongqing, China) who had at least three years of teaching experience and two years of AI implementation experience. Participants were purposefully selected through a two-stage sampling strategy: initial screening via open-ended questionnaires to identify teachers reporting negative experiences with AI integration, followed by snowball sampling to recruit additional participants with similar perspectives. Data collection employed a dual-facilitator approach using semi-structured interviews, with one moderator guiding discussions while another observed non-verbal cues. Qualitative content analysis revealed key barriers across four dimensions: technological (interface complexity, infrastructure limitations), employee (professional identity conflicts, interpersonal tensions), task-related (real-time monitoring challenges, reduced pedagogical flexibility), and organizational (inadequate support systems, unclear implementation policies). These findings suggest that successful AI integration in PE requires a holistic approach addressing both technological and human factors, rather than focusing solely on technological advancement. The study contributes to understanding how socio-technical interactions uniquely manifest in physically active learning environments.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Primary 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.

Study methods · source passage

This study employed a phenomenological research design to examine the complex interplay between artificial intelligence (AI) technologies and physical education (PE) practices. The phenomenological approach was selected as the methodological framework for its capacity to illuminate participants’ lived experiences and shared meanings regarding AI implementation challenges (Moustakas, 1994; Van Manen, 2016). While quantitative methodologies predominantly rely on numerical data and predefined variables, phenomenological inquiry enables a nuanced understanding of educators’ subjective experiences and perceptions (Day, 1991; Alberola-Mulet et al., 2021; Wohlfart et al., 2021). Contemporary research on AI integration in PE settings has largely emphasized technological functionality, often reducing it to a mechanical intervention while overlooking the nuanced experiential dimensions of frontline educators. Our phenomenological framework facilitated exploration beyond surface-level observations to examine the underlying structures of meaning in teachers’ encounters with AI technology. To gather rich phenomenological data, we utilized focus group interviews as the primary data collection method. This approach proved particularly efficacious in capturing multiple perspectives simultaneously while fostering dynamic discussions that revealed shared experiences and collective meaning-making processes. The methodological choice of focus groups has demonstrated considerable utility in educational research contexts (Wilson, 1997), specifically in investigating teachers’ engagement with digital technologies and their perspectives on pedagogical integration (Carlock & Perry, 2008; Erişti et al., 2012). The synergistic nature of focus group interactions enabled participants to articulate and reflect upon their experiences while facilitating the emergence of deeper insights into the complexities of AI implementation in PE settings (Kruger & Casey, 2015).

Limitations and uncertainty · source passage

The data illuminated a nuanced pattern of professional marginalization that extends beyond simple exclusion from decision-making processes. One participant candidly expressed: “Nobody asked us what tools would actually help in our PE classes. They just bought these expensive systems and now expect us to make them work somehow” (NJ005). This observation gains particular salience through another educator’s experience: “We’ve been teaching PE for years; we know what works with our students. But suddenly, all that experience seems to count for nothing” (CQ006).

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.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

A 90-day district pilot

  1. 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.
  2. Days 16–30 · Design. Pilot one teacher-led unit with short guided turns. The tutor asks for an explanation and offers a graduated hint before any worked solution. Use concrete examples and an equivalent task without AI. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. 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.
  4. Days 61–90 · Decide. Use an age-appropriate independent post-task and a delayed parallel task. Score explanation quality and transfer, not the number of agent messages or completion rate alone. 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

Haoran Zha; Wenye Li; Weihao Wang; Jian Xiao. The Paradox of AI Empowerment in Primary School Physical Education: Why Technology May Hinder, Not Help, Teaching Efficiency. Behavioral Sciences (2025-02-19). https://doi.org/10.3390/bs15020240

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.