From leadership research to district action: Rethinking ethical governance of generative AI in sport pedagogy research: a discipline-sensitive perspective

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: brief-report. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract Generative artificial intelligence (GenAI)—including large language models, image synthesis, and learning analytics tools, is increasingly used in pedagogical research for instructional design, feedback generation, data analysis, and manuscript preparation. However, discipline-specific ethical guidance remains limited, particularly in fields characterized by embodied learning and performance-based pedagogy. In sport sciences, GenAI applications intersect with motor learning, sensorimotor feedback, and learner autonomy, raising distinctive challenges related to academic integrity, transparency, authorship, data protection, and pedagogical validity. In this Perspective, we argue that existing institutional and international AI ethics frameworks, while essential, remain predominantly text-oriented and insufficiently responsive to the embodied, safety-sensitive, and movement-based characteristics of sport pedagogy research. This creates a governance gap in which AI-generated feedback may directly affect motor learning processes, learner safety, and skill acquisition trajectories in ways not adequately captured by generic AI ethics guidance. Drawing on research integrity standards, global AI governance principles, and motor learning theory, we identify key ethical tensions emerging from the integration of generative systems into sport pedagogical research and practice. This article develops a discipline-sensitive ethical framework through a structured, purposive, and theory-guided synthesis of relevant literature. We propose a set of priority orientations to support responsible GenAI use in this context, including strengthened human accountability, transparent disclosure practices, discipline-informed validation of AI-generated feedback, and enhanced protection of learner data. These propositions require empirical validation in real world sport pedagogy contexts. We position pedagogical validity, defined as alignment between AI-supported processes and established motor learning principles, as a novel ethical criterion not yet explicitly articulated in AI education governance. Finally, we outline a research and policy agenda for empirically validating this framework across diverse embodied learning environments and clarify the logic used to identify and synthesize literature across AI governance, research integrity, and motor learning scholarship. This conceptual, theory-guided Perspective is intended to inform researchers, ethics committees, and higher education institutions seeking disciplines-sensitive approaches to the responsible integration of GenAI in embodied learning and performance-based education contexts. Keywords: AI disclosure, generative artificial intelligence, higher education, motor learning, pedagogical validity, research ethics, sport pedagogy

Study methods

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Limitations

This framework is conceptual and has not yet been empirically validated, and several limitations warrant acknowledgment. First, the framework has not been empirically validated through implementation studies examining how researchers, ethics committees, or institutions operationalize pedagogical validity in practice. The feasibility, reliability, and practical utility of the proposed criteria remain empirical questions. Second, the framework grounded in international AI governance standards and motor learning literature reflects predominantly Western research traditions and regulatory contexts. Cross-cultural validation is needed to assess applicability in educational cultures that differ in pedagogical philosophies, institutional structure, and perceptions of learner autonomy. In addition, because this Perspective does not follow a systematic review protocol, the possibility of interpretive selection bias cannot be fully excluded, even though the analysis was anchored in representative and thematically relevant scholarship. Third, the framework focuses primarily on GenAI applications in feedback generation and instructional design. It may therefore require extension to address emerging applications such as AI-supported biomechanical analysis, automated performance prediction, or integration with wearable-sensor ecosystems. Fourth, ongoing technological evolution may require periodic revision of the framework. For this reason, ethical governance should be treated as version-sensitive and revisable, with periodic reassessment whenever AI capabilities, deployment conditions, or data-processing practices materially change. Fifth, the decision pathway (Figure 1) provides structured guidance but cannot eliminate contextual judgment. Its implementation will require adaptation to specific research settings, institutional policies, and regulatory requirements. Finally, the literature base remains limited to the early stage of AI ethics scholarship in sport pedagogy; as empirical research accumulates, the conceptual foundations of the framework may require refinement.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Amayra Tannoubi; Vlad Adrian Geantă; Viorel Petru Ardelean; Edi Setiawan; Fairouz Azaiez. Rethinking ethical governance of generative AI in sport pedagogy research: a discipline-sensitive perspective. Frontiers in Public Health (2026-05-08). https://doi.org/10.3389/fpubh.2026.1812516

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.