Abstract:

Sports organizations educate people every day—often without calling it “education.” Coaches refine decision-making, athletes learn self-regulation, analysts develop professional routines, and administrative staff build operational skills under real competitive pressure. Yet the recent adoption of Artificial Intelligence (AI) and learning analytics in sport is still frequently framed as performance surveillance: prediction, control, rankings, and compliance. In that framing, data become a “scoreboard” for people, not a mirror for learning—and the pedagogical value of information is lost.

This paper presents a practical, pedagogy-first model for using AI and learning analytics to strengthen coach, athlete, and staff education inside sports organizations. The core shift is from data-driven (automated judgement) to data-informed (human judgement supported by evidence).

We propose a lightweight implementation blueprint that clubs and federations can adopt without building a research lab:
(a) define learning outcomes and role-based competencies (coaching, athletic, performance support, administration),
(b) capture learning-relevant traces from existing systems (LMS activity, workshop attendance, reflective logs, tagged video, micro-assessments, and—only where appropriate—training-load or match-event data),
(c) convert traces into formative feedback loops, and
(d) embed these loops into everyday workflows (pre-session planning, post-session review, weekly staff meetings, and individualized development plans).

AI is positioned as a pedagogical assistant, not a decision-maker. Example uses include: generating role-specific reflection prompts from session plans and video tags; summarizing learning journals into actionable themes for mentor conversations; recommending short microlearning resources based on observed knowledge gaps; and supporting personalized pathways for coach education and CPD (continuous professional development). Learning analytics are operationalized through simple dashboards designed for learning awareness (What am I practicing? What am I avoiding? What improved this week?), self-regulated learning (goal setting, monitoring, adjustment), and reflective practice (evidence → interpretation → next action). Importantly, all analytics outputs are framed as “signals for dialogue,” not “scores for judgement.”

Methodologically, the paper outlines a design-and-implementation framework aligned with adult learning and workplace learning principles, and provides an evaluation plan suitable for clubs: usability and adoption metrics, perceived learning value, changes in reflective routines, and competency progression through rubric-based assessments and coaching portfolio evidence. Ethical safeguards are treated as design requirements: transparency of data use, proportionality (collect only what supports learning), clear separation between learning feedback and employment decisions, and governance processes that give learners voice and control.

The contribution is a practical roadmap for turning sport’s rich data ecosystem into an educational ecosystem: a learning-oriented culture where AI and analytics amplify coaching expertise, strengthen learner agency, and make professional development measurable in humane, meaningful ways—without reducing people to numbers.

Keywords:

AI in Education, Learning Analytics, Coach Education, Workplace Learning, Sports Organizations.