From Metrics to Meaning: Rule-Grounded LLM Explanations for Data Literacy in the Case of Youth Football
Year: 2026
Type: conference-paper
Abstract: Young athletes, parents, and coaches are increasingly exposed to training metrics from wearable technology, yet such metrics are difficult to interpret without contextual explanation.We present a rule-grounded data-to-text framework for supporting data literacy in youth football through concise, stakeholder-specific summaries of training sessions.A rule layer maps duration-normalised indicators to structured facts about session profile, internal intensity, speed exposure, and movement dynamics, which are then verbalised by a large language model for coaches, parents, or players.We compare direct generation from raw metrics, generation from rule-derived facts, and an augmented rule-grounded configuration, ENRICHED, that supplements validated facts with raw metrics and explicit threshold definitions.In this setting, selected open-weight models are additionally adapted using LoRA.The framework is developed using 122 anonymised player-session records from a U15 environment and evaluated on a held-out subset of ten sessions with stakeholder-oriented reference summaries.The results indicate that rule grounding improves reliability and audience adaptation compared with direct generation from raw metrics, particularly by reducing unsupported or overly strong interpretations.A school-based expert evaluation with physical education teachers further suggests that player-facing explanations in the evaluated ENRICHED setting can remain accurate, comprehensible, and practically useful.We position the framework as an interpretable data-literacy support interface for youth sport analytics.
Authors: Tomasz Piłka, Tomasz Kuczyński, Mateusz Czajka
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166882317