efl-speaking
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- PaperLanguage Teaching Research17 Jul 2026
Not the Artificial Intelligence, but the Sequence: Instructional Design and Oral Communication Strategy Development
Zola Chi-Chin Lai
This study compared oral communication strategy development across three EFL speaking course formats: a structured BOPPPS sequence with integrated ChatGPT use, self-directed AI use, and traditional teacher-led instruction. The BOPPPS-based class showed the strongest strategic gains, while the AI-flex class showed the smallest increases. Findings indicate that instructional design, not AI availability, drives strategic growth.
Original abstract
This study examines how instructional design shapes the development of oral communication strategies in an 18-week English-as-a-foreign-language speaking course, at a time when generative artificial intelligence is often assumed to enhance speaking performance. All three classes worked with the same curriculum and speaking tasks. One class learned through a BOPPPS (Bridge-In, Objective, Pre-Assessment, Participatory Learning, Post-Assessment, and Summary) based sequence in which students prepared ideas, participated in guided speaking activities, and reviewed their performance, with each cycle including a stage where they generated their own content before using ChatGPT to refine it. A second class received regular teacher guidance and completed the same tasks, but their use of ChatGPT was self-directed and not part of a fixed sequence (the artificial intelligence-flex condition). A third class covered the same material through teacher-supported face-to-face interaction without artificial intelligence (the traditional condition). Learners completed the Oral Communication Strategy Inventory at the beginning and end of the semester and provided reflections on how their course format influenced their speaking. The findings show a clear hierarchy in strategic development. The BOPPPS based class demonstrated the strongest gains, especially in negotiation for meaning, fluency regulation, and accuracy monitoring. The traditional class showed moderate improvements, particularly in nonverbal and social-affective strategies. The artificial intelligence-flex class displayed the smallest increases, a pattern reflected in learners’ descriptions of artificial intelligence-driven exchanges involving fewer occasions to plan, clarify, or monitor meaning. Overall, the results indicate that strategic growth is driven primarily by instructional design, with artificial intelligence contributing meaningfully only when situated within a structured pedagogical frame.
- PaperComputer Assisted Language Learning3 Jul 2026
Integrating concept mapping into spherical video-based virtual reality speaking tasks: effects on EFL learners’ speaking performance, self-efficacy, and speaking anxiety
Gwo-Jen Hwang, Chu-Yun Lin, Mei-Rong Alice Chen
Investigates the effects of integrating concept mapping into spherical video-based virtual reality speaking tasks on EFL learners' speaking performance, self-efficacy, and speaking anxiety.
- PaperComputer Assisted Language Learning1 Jun 2026
Vocal and visual design features in multimodal GenAI agents: effects on EFL speaking performance, affect and interaction experience
Chenghao Wang, Xueyun Li, Hui Jin, Bin Zou
This study investigates how vocal and visual design features in multimodal GenAI agents affect EFL learners' speaking performance, affect, and interaction experience.