Speaker
Abstract section 5: References
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Shadiev, R., Hwang, W.-Y., & Huang, Y.-M. (2017). Review of research on mobile language learning in authentic environments. Computer Assisted Language Learning, 30(3-4), 284–303. https://doi.org/10.1080/09588221.2017.1308383
Abstract section 3: Content/method
Participants included 477 university students and 40 English teachers in central Taiwan. Students completed validated TAM and L2MSS questionnaires (Cronbach's α = .796–.913). Ten teachers participated in semi-structured interviews (45–60 min); three interviews with diverse teaching experience (5–20 years) were selected for preliminary thematic analysis. Quantitative data were analyzed using descriptive statistics, Pearson correlations, and hierarchical regression. Interview transcripts were thematically coded to identify instructional orientations, motivational scaffolding practices, and institutional constraints.
Abstract section 4: Outcomes/results
Findings of this study reveal that students demonstrated positive attitudes toward technology-assisted learning and moderate Ideal L2 Self (M = 3.55–3.91). Students with stronger future self-images as English users demonstrated significantly higher technology acceptance. However, teacher interviews exposed a motivational scaffolding gap: while students were motivated by long-term identity goals, teachers prioritized immediate engagement over future-self development, faced institutional constraints (standardized testing), and expressed concerns about effectively integrating AI tools into instruction. These findings suggest current technology integration practices may inadvertently undermine students’ self-directed motivation, highlighting the need for teacher professional development that bridges motivational theory and classroom implementation.
Abstract section 2: Contribution/research questions
This research addresses three questions: (1) How do students perceive technology acceptance in AI-assisted EFL learning? (2) How does students' Ideal L2 Self predict TAM dimensions—perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention? (3) What pedagogical beliefs and institutional barriers shape teachers' technology integration practices? Findings aim to inform professional development strategies that align technology adoption with learners' motivational profiles and address systemic implementation challenges.
Abstract section 1: Relevance
Meta- analyses confirm that AI-supported tools enhance EFL learners' engagement and proficiency across diverse skill domains (Shadiev et al., 2017). However, existing research has predominantly examined student technology acceptance through the Technology Acceptance Model (Granić & Marangunić, 2019), with limited attention to teachers' pedagogical beliefs and classroom implementation realities. Furthermore, the motivational mechanisms linking technology acceptance to learners' future self-guides remain underexplored. Drawing on TAM and the L2 Motivational Self System, this study investigates how students' technology acceptance relates to their Ideal L2 Self and how teachers conceptualize technology integration within institutional constraints.
| Title | Bridging Technology Acceptance, L2 Self, and EFL Practice |
|---|---|
| Teaching Context | College and university education |