instructional-design
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- PaperOpenAlex — TESOL research21 Jul 2026
The Role of Artificial Intelligence in Lesson Planning and the Development of English Teaching Methods: Teachers’ Perspectives
Azza Elmadani, Shiraz Al-Ali, Salma Mohammed Osman Elias, Mudathir Yousif Mohamed Ahmed et al.
Artificial Intelligence (AI) is transforming English Language Teaching by aiding lesson planning, instructional design, material development, personalized instruction, assessment, and classroom interaction. Teachers report that AI saves time, offers differentiated instruction options, and provides adaptive materials, while ethical concerns include overdependence, plagiarism, privacy, and teacher qualifications. AI is seen as supplementary rather than a replacement for human educators.
Original abstract
In today's world, Artificial Intelligence (AI) represents an innovative factor in education, especially in English Language Teaching (ELT). The purpose of this research paper is to explore the influence of AI on lesson planning and English teaching methodologies based on teachers' insights. Specifically, it will be considered the impact of AI on instructional design, material development, personalized instruction, assessment, and classroom interaction in the context of English language teaching. For analysis, a qualitative-descriptive and analytical approach will be applied using a review of empirical findings, scientific literature, and personal experience related to integrating AI in ELT. According to results, artificial intelligence contributes to teachers' performance by saving their time, offering options for differentiated instruction, providing adaptive instructional materials, and enabling innovation in the pedagogy field. At the same time, the use of technology is viewed as supplementary to traditional educational activities rather than replacement to human educators. Ethical aspects associated with the integration of AI in the process of education, overdependence on AI-based resources, plagiarism and academic integrity, privacy protection issues, and teacher qualifications should be considered in the case of English teaching.
- 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 Learning6 May 2026
Comparing AI-enhanced instructional designs for EFL postgraduate proficiency: a multisite randomized controlled trial with exploratory ICC outcomes
Zhongmin Li, Cui Gu, Akbar Bahari
This study compared AI-enhanced instructional designs for EFL postgraduate proficiency using a multisite randomized controlled trial and explored intraclass correlation (ICC) outcomes.