Unpacking AI-supported Chinese as a foreign language learning: How beginner-level learners' cognitive and motivational factors predict speaking proficiency

This study examines the cognitive and motivational factors that influence Mandarin speaking proficiency among learners in an artificial intelligence (AI)-supported, project-based Chinese as a Foreign Language (CFL) learning environment. It specifically investigates how learners' psychological engagement with AI tools relates to language performance. A mixed-methods explanatory design was adopted. Quantitative data were collected from 120 beginner-level CFL university students in Southeast Asia and analyzed using correlation and multiple regression techniques. Speaking proficiency was assessed through both script writing and oral project presentations. To enrich interpretation, qualitative data were obtained from interviews, learner-generated artifacts, and instructor observations with a purposive subsample of 30 learners. Results indicated that technical proficiency, perceived usefulness, and positive attitudes toward AI were significant positive predictors of speaking performance. In contrast, high levels of trust in AI were negatively associated with outcomes, suggesting diminished learner autonomy. A strong correlation emerged between AI usage and script writing scores, while a weaker but significant relationship was observed for oral presentation. Notably, written and oral performance were not significantly correlated, highlighting distinct developmental trajectories in these modalities. Qualitative results revealed that learners who engaged critically and strategically with AI achieved better outcomes than those who relied on it passively. This study contributes to understanding the psychological mechanisms underlying AI-assisted language learning. It highlights the importance of fostering mindful and autonomous engagement with technology to support speaking proficiency, and underscores the need for skill-specific instructional strategies in AI-integrated educational settings.

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Publication Details

Journal
Acta Psychologica
Published
2025-10-01
DOI
https://doi.org/10.1016/j.actpsy.2025.105703
Citations
3
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
7.44
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article

Unpacking AI-supported Chinese as a foreign language learning: How beginner-level learners' cognitive and motivational factors predict speaking proficiency

Goh Ying Yingsoon, 杰 张, Xiaosheng Zhou
3 citations
Acta Psychologica
Intelligent Tutoring Systems and Adaptive Learning
7.44
article

Unpacking AI-supported Chinese as a foreign language learning: How beginner-level learners' cognitive and motivational factors predict speaking proficiency

Goh Ying Yingsoon, 杰 张, Xiaosheng Zhou
article en
3 citations

Abstract

This study examines the cognitive and motivational factors that influence Mandarin speaking proficiency among learners in an artificial intelligence (AI)-supported, project-based Chinese as a Foreign Language (CFL) learning environment. It specifically investigates how learners' psychological engagement with AI tools relates to language performance. A mixed-methods explanatory design was adopted. Quantitative data were collected from 120 beginner-level CFL university students in Southeast Asia and analyzed using correlation and multiple regression techniques. Speaking proficiency was assessed through both script writing and oral project presentations. To enrich interpretation, qualitative data were obtained from interviews, learner-generated artifacts, and instructor observations with a purposive subsample of 30 learners. Results indicated that technical proficiency, perceived usefulness, and positive attitudes toward AI were significant positive predictors of speaking performance. In contrast, high levels of trust in AI were negatively associated with outcomes, suggesting diminished learner autonomy. A strong correlation emerged between AI usage and script writing scores, while a weaker but significant relationship was observed for oral presentation. Notably, written and oral performance were not significantly correlated, highlighting distinct developmental trajectories in these modalities. Qualitative results revealed that learners who engaged critically and strategically with AI achieved better outcomes than those who relied on it passively. This study contributes to understanding the psychological mechanisms underlying AI-assisted language learning. It highlights the importance of fostering mindful and autonomous engagement with technology to support speaking proficiency, and underscores the need for skill-specific instructional strategies in AI-integrated educational settings.

Acta PsychologicaVol. 260
Wenzhou University (CN), Universiti Malaysia Terengganu (MY), Xi'an International Studies University (CN), Xi’an International University (CN)
Openalex Percentile: Top 3%
Intelligent Tutoring Systems and Adaptive Learning
7.44
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