ArticleFrontiers in psychology2026
AI-assisted task-based language learning and English proficiency: a dual-pathway model of cognitive engagement and self-regulated learning with AI trust as a boundary condition.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Artificial intelligence is increasingly transforming language learning environments, yet the psychological mechanisms through which AI-assisted instructional practices influence language outcomes remain insufficiently understood. Although prior studies have examined AI-supported learning, limited research has explained how AI-assisted task-based learning activates distinct cognitive and behavioral processes that contribute to English proficiency in Chinese higher education contexts. Therefore, this study examines how AI-assisted task-based language learning influences English proficiency among university students in China by integrating cognitive engagement and self-regulated learning as parallel mediating mechanisms, with AI trust functioning as a moderating boundary condition. Methods: Data were collected from 412 undergraduate students enrolled in Chinese universities and analyzed using partial least squares structural equation modeling. Results: The results reveal that AI-assisted task-based learning significantly enhances both cognitive engagement and self-regulated learning, which subsequently improve English proficiency. Notably, self-regulated learning demonstrated substantially stronger direct and mediating effects than cognitive engagement, suggesting that AI-assisted learning environments may operate more effectively through behavioral regulation processes. Furthermore, AI trust strengthens the positive influence of AI-assisted learning on English proficiency. Implications: By integrating instructional design, learner cognitive processes, and trust in artificial intelligence within a unified analytical framework, the study advances a mechanism-based explanation of AI-enabled language learning and highlights how AI technologies can enhance educational effectiveness in higher education language learning environments.
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