AI-assisted and Big-Unit teaching enhance speed-skating performance through psychological mechanisms in adolescents: evidence from a three-arm intervention study

TL;DR

AI-assisted and Big-Unit teaching methods improve speed-skating performance in adolescents by boosting psychological factors like motivation and self-efficacy.

Contribution

This study is the first to compare AI-assisted and Big-Unit teaching models and identify psychological mechanisms in adolescent winter sports.

Findings

AI-assisted and Big-Unit teaching improved 500-m speed-skating performance significantly more than conventional methods.

Innovative teaching enhanced psychological outcomes like motivation, self-efficacy, and resilience in adolescents.

Learning motivation and self-efficacy mediated the relationship between teaching methods and performance gains.

Abstract

Innovative instructional approaches are increasingly advocated in physical education to enhance both motor skill development and psychological adaptation. However, few studies have directly compared micro-level (AI-assisted) and macro-level (Big-Unit) teaching models, or examined the psychological mechanisms underlying performance improvements in adolescent winter-sport environments.

A three-arm, quasi-experimental longitudinal study was conducted with 129 first-year middle school students (AI-assisted: n = 42; Big-Unit: n = 43; Conventional: n = 44). Participants completed an 8-week speed-skating intervention consisting of 24 on-ice lessons. Learning motivation, self-efficacy, psychological resilience, and related psychological constructs were assessed at baseline (T1), mid-intervention (T2), and post-intervention (T3). Skating performance was evaluated using electronic 500-m timing. Linear mixed-effects models, ANCOVA, and structural equation modeling were applied to assess Group × Time interactions and mediation pathways.

Both AI-assisted and Big-Unit teaching produced significantly larger improvements in 500-m performance than conventional instruction (AI: −5.59 s; Big-Unit: −7.60 s; Conventional: −1.80 s; all p < 0.001). All 13 psychological outcomes showed strong Group × Time interactions favoring the innovative groups [all χ2(4) > 137.28, q < 0.001]. ANCOVA confirmed substantial adjusted Group effects for changes in learning motivation, self-efficacy, psychological resilience, and anxiety/stress (partial η2 = 0.650–0.927). Mediation analyses identified a statistical suppression pattern, in which increases in learning motivation and self-efficacy served as significant indirect pathways linking innovative instruction to performance gains. However, the direct technical impact remained the dominant driver.

AI-assisted and Big-Unit teaching substantially enhance both technical performance and psychological functioning in adolescent speed skating. Statistical mediation models support learning motivation as a plausible mechanism linking teaching mode to performance, with self-efficacy providing additional support. These findings highlight the complementary potential of technology-enhanced and mastery-oriented pedagogies to modernize physical education through both direct technical renovation and indirect psychological adaptation.

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