IJIET 2026 Vol.16(7): 1978-1992
doi: 10.18178/ijiet.2026.16.7.2659
doi: 10.18178/ijiet.2026.16.7.2659
Behavioral Drivers of ChatGPT-assisted TOEIC Reading: Evidence from an Extended UTAUT Model
Yu-Hung Chiang 1, Hsin-Ping Tseng 2, Ching-Hua Lu 3, and Tien-Chi Huang 4,*
1. Department of Asia-Pacific Industrial and Business Management, National University of Kaohsiung, Taiwan
2. Doctoral Program of Intelligent Engineering, National Taichung University of Science and Technology, Taiwan
3. Department of Business Administration, National Taichung University of Science and Technology, Taiwan
4. Department of Information Management, National Taichung University of Science and Technology, Taiwan
Email: yhchiang@nuk.edu.tw (Y.-H. C.); s1f11336002@nutc.edu.tw (H.-P.T.); chlu@nutc.edu.tw (C.-H.L.); tchuang@nutc.edu.tw (T.-C.H.)
*Corresponding author
2. Doctoral Program of Intelligent Engineering, National Taichung University of Science and Technology, Taiwan
3. Department of Business Administration, National Taichung University of Science and Technology, Taiwan
4. Department of Information Management, National Taichung University of Science and Technology, Taiwan
Email: yhchiang@nuk.edu.tw (Y.-H. C.); s1f11336002@nutc.edu.tw (H.-P.T.); chlu@nutc.edu.tw (C.-H.L.); tchuang@nutc.edu.tw (T.-C.H.)
*Corresponding author
Manuscript received November 4, 2025; revised November 24, 2025; accepted March 2, 2026; published July 24, 2026
Abstract—This study examined the effectiveness of Artificial Intelligence (AI)-powered Teaching Assistants (AITAs), developed through ChatGPT fine-tuning to support underperforming technical university students in Taiwan, in improving their Test of English for International Communication (TOEIC) reading comprehension. These students are required to meet the English proficiency graduation requirement of a minimum TOEIC score of 550. However, many struggle to attain even 200 out of 495 points in the reading section. Employing a quasi-experimental design, this study involved 226 undergraduate students from non-English major departments over a 12-week period. The experimental group utilized AITAs-driven materials, whereas the control group relied on conventional resources. Learning outcomes were evaluated through pre- and post-tests, and the extended Unified Theory of Acceptance and Use of Technology (UTAUT) was applied to identify the factors influencing students’ acceptance of AITAs-driven materials. An analysis of covariance revealed that students in the experimental group achieved significantly higher TOEIC reading scores than those in the control group. The extended UTAUT analysis highlighted that social influence, AI self-efficacy, performance and effort expectancy, and facilitating conditions significantly influenced behavioral intention. Task-technology fit moderated the relationship between behavioral intention and usage behavior. These findings provide robust empirical evidence for the efficacy of AI-driven personalized learning in enhancing TOEIC reading comprehension for university students. Furthermore, the research offers a comprehensive framework for integrating AI tools into language education practices. It addresses the specific challenges and unique educational needs of learners and leverages AI technologies to help language learners meet standardized testing requirements.
Keywords—ChatGPT, Artificial Intelligence (AI)-powered Teaching Assistants (AITAs), Test of English for International Communication (TOEIC) reading, extended Unified Theory of Acceptance and Use of Technology (UTAUT), English as a Foreign Language (EFL), vocational education
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—ChatGPT, Artificial Intelligence (AI)-powered Teaching Assistants (AITAs), Test of English for International Communication (TOEIC) reading, extended Unified Theory of Acceptance and Use of Technology (UTAUT), English as a Foreign Language (EFL), vocational education
Cite: Yu-Hung Chiang, Hsin-Ping Tseng, Ching-Hua Lu, and Tien-Chi Huang, "Behavioral Drivers of ChatGPT-assisted TOEIC Reading: Evidence from an Extended UTAUT Model," International Journal of Information and Education Technology, vol. 16, no. 7, pp. 1978-1992, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).