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.

Speech emotion recognition remains a challenging task, particularly in low-resource language settings. In this work, we explore the development of a system capable of identifying emotional states in Polish speech using training data exclusively from other languages. Our approach relies on a pretrained speech representation model and follows a strict zero-shot training paradigm, enabling cross-lingual knowledge transfer without access to any Polish data. The system was developed in the context of the Polish Speech Emotion Recognition Challenge (PolEval 2025), which required participants to train models solely on multilingual resources and evaluate them on Polish speech in a zero-shot setup. We present a complete solution encompassing model selection, audio preprocessing, and fine-tuning strategy, and discuss the potential of large-scale language models for cross-lingual emotion recognition.