Beyond Instrumental Use: A TAM-Based Study of Al-AssistedLanguage Learning Among Chinese University Students

With the rapid advancement of artificial intelligence (AI), particularly in natural language processing and adaptive learning technologies, AI tools are becoming increasingly prominent in foreign language education. However, their pedagogical effectiveness and impact on learning outcomes remain underexamined. Anchored in the Technology Acceptance Model (TAM), this study surveyed 365 Chinese university students to assess their adoption, usage patterns, and perceptions of AI in language learning. The findings reveal that: (1) students primarily use AI for structured tasks such as writing assistance and grammar training, with less emphasis on speaking and listening applications; (2) perceived usefulness, reflected in improved learning efficiency and language knowledge acquisition, aligns closely with students' cognitive and behavioral engagement; and (3) perceived ease of use and teacher guidance jointly shape learning effectiveness, underscoring the critical role of pedagogy in leveraging AI tools. Based on these insights, the study proposes a three-dimensional framework that emphasizes the development of multimodal functionalities to foster intercultural competence, redefines the teacher's role as a strategic facilitator, and advocates for intelligent, learner-centered ecosystems. These recommendations contribute to advancing the integration of AI in foreign language education and provide practical guidance for its pedagogical optimization.

Authors

Publication Details

Journal
Journal of Education and Training Studies
Published
2025-08-04
DOI
https://doi.org/10.11114/jets.v14i1.8034
Primary Topic
Arabic Language Education Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond Instrumental Use: A TAM-Based Study of Al-AssistedLanguage Learning Among Chinese University Students

J P Qian, Ting Zhang, Ying Zhang, N. Xu et al.
Journal of Education and Training Studies
Arabic Language Education Studies
article

Beyond Instrumental Use: A TAM-Based Study of Al-AssistedLanguage Learning Among Chinese University Students

J P Qian, Ting Zhang, Ying Zhang, N. Xu, Yan Zou, Xiaofei Yu
article en

Abstract

With the rapid advancement of artificial intelligence (AI), particularly in natural language processing and adaptive learning technologies, AI tools are becoming increasingly prominent in foreign language education. However, their pedagogical effectiveness and impact on learning outcomes remain underexamined. Anchored in the Technology Acceptance Model (TAM), this study surveyed 365 Chinese university students to assess their adoption, usage patterns, and perceptions of AI in language learning. The findings reveal that: (1) students primarily use AI for structured tasks such as writing assistance and grammar training, with less emphasis on speaking and listening applications; (2) perceived usefulness, reflected in improved learning efficiency and language knowledge acquisition, aligns closely with students' cognitive and behavioral engagement; and (3) perceived ease of use and teacher guidance jointly shape learning effectiveness, underscoring the critical role of pedagogy in leveraging AI tools. Based on these insights, the study proposes a three-dimensional framework that emphasizes the development of multimodal functionalities to foster intercultural competence, redefines the teacher's role as a strategic facilitator, and advocates for intelligent, learner-centered ecosystems. These recommendations contribute to advancing the integration of AI in foreign language education and provide practical guidance for its pedagogical optimization.

Journal of Education and Training StudiesVol. 14(1)
Nanjing Normal University
Openalex Percentile: Top 69%
Arabic Language Education Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.