Actionable companion · Feedback & scaffolding

Learning with agency: Artificial intelligence in education: effects on motor learning, motivation, and student engagement from an educational psychology perspective

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:

Abstract The rapid expansion of artificial intelligence (AI) in education has renewed debates about its pedagogical value, particularly in relation to learner-centered approaches and motivational processes. While a growing body of research has examined AI-supported learning in cognitively oriented subjects, empirical evidence remains scarce in practice-based disciplines such as physical education (PE), especially within secondary school contexts in developing countries. This study investigated the effects of integrating AI-supported instructional tools into PE lessons on students' motor learning, intrinsic motivation, and engagement in Tunisian secondary schools. A quasi-experimental pre-test/post-test design involving two intact classes was employed because individual random assignment was not feasible in the school setting (N = 56; age range 15–17 years, M = 16.4, SD = 0.9), including an experimental group receiving AI-supported instruction and a control group following traditional teaching methods over an 8-week intervention period. Motor learning was assessed using the TGMD-3, intrinsic motivation through the Intrinsic Motivation Inventory, and engagement via a multidimensional student engagement scale. Results revealed statistically significant and educationally meaningful improvements in motor skill acquisition, intrinsic motivation, and engagement among students exposed to AI-supported instruction compared with their peers in the control group. Effect sizes indicated strong practical relevance, suggesting that AI-supported visual feedback based on predefined movement indicators was interpreted and contextualized by the teacher to support motor skill acquisition. These findings suggest that AI-supported instruction may enhance motor learning, intrinsic motivation, and student engagement when integrated into teacher-mediated physical education. Given the quasi-experimental design involving intact classes, these findings should be interpreted as evidence of association rather than definitive causal effects. These findings contribute context-sensitive empirical evidence to the field of educational psychology by highlighting the potential of human-centered AI integration in secondary school physical education within a developing country context. The study underscores the importance of pedagogical mediation, learner autonomy, and ethical awareness in the design and implementation of AI-supported learning environments. Keywords: artificial intelligence, educational psychology, intrinsic motivation, motor learning, secondary education, student engagement

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

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

A quasi-experimental pre-test/post-test design involving two intact classes was employed because individual random assignment was not feasible in the school setting (N = 56; age range 15–17 years, M = 16.4, SD = 0.9), including an experimental group receiving AI-supported instruction and a control group following traditional teaching methods over an 8-week intervention period.

Study methods · source passage

The study was conducted in a public secondary school located in an urban area of Tunisia during the second academic term. The participating school was selected through convenience sampling based on administrative authorization and willingness to participate in the study. Two existing classes were included because reorganizing students into randomly assigned groups was not feasible within the school's organizational structure. Physical education lessons were delivered in accordance with the national curriculum, with two sessions per week for each class. This instructional context reflects typical conditions in Tunisian public secondary schools, characterized by relatively large class sizes and limited material resources (Darwez et al., 2020; Saaidia et al., 2026).

Limitations and uncertainty · source passage

Despite the robustness of the quasi-experimental design and the use of validated instruments, several methodological limitations warrant consideration when interpreting the findings. First, the absence of random assignment limits the strength of causal inferences that can be drawn from the results. Although baseline equivalence between groups was established and standard controls were implemented, unmeasured contextual variables such as classroom climate, peer dynamics, or prior exposure to digital tools may have influenced learning outcomes (Shadish, 2002).

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.
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. 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

Amin Daly; Sofiene Mnedla; Mohamed Souhaiel Chelly. Artificial intelligence in education: effects on motor learning, motivation, and student engagement from an educational psychology perspective. Frontiers in Psychology (2026-08-28). https://doi.org/10.3389/fpsyg.2026.1816494

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.