Mapping the Terrain: EFL Teachers’ Acceptance of Generative AI Integration in Lesson Planning

Muhammad Asrori; Endang Setyaningsih

doi:10.35445/alishlah.v17i2.6253

Mapping the Terrain: EFL Teachers’ Acceptance of Generative AI Integration in Lesson Planning

Muhammad Asrori, Endang Setyaningsih

Abstract

Grounded in the Technology Acceptance Model (TAM), this study explores high school EFL teachers’ perceptions of the ease of use (PEU) and usefulness (PU) of Generative AI (Gen-AI) MagicSchool for lesson planning. It further investigates the factors influencing their acceptance or resistance to adopting this technology after undergoing a Gen-AI training session. A mixed-methods approach was employed. Quantitative data were gathered through a TAM-based questionnaire tailored to the features of Gen-AI MagicSchool, while qualitative insights were obtained via focus group interviews. The questionnaire assessed teachers’ perceived usefulness, ease of use, attitudes, and behavioral intentions. Focus groups explored nuanced factors influencing acceptance or hesitation. Thematic analysis revealed generally positive perceptions of Gen-AI. Teachers found the tool easy to use and valuable for lesson planning, indicating an intention to continue exploring its applications. However, concerns emerged about the prompt formulation and the contextual relevance of AI-generated outputs. These concerns highlighted the need for pedagogical alignment and appropriate scaffolding. Findings suggest that while Gen-AI tools have strong potential to support EFL instruction, effective integration depends on enhancing teachers’ content and AI-related competencies. The interplay of PU and PEU was central to shaping behavioral intentions. To foster AI adoption in education, teacher training should include prompt engineering and strategies for AI-human collaboration. Practical exposure to Gen-AI during professional development can strengthen confidence and competence in AI integration.

Keywords

generative AI; lesson plan; technology acceptance model

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References

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DOI: https://doi.org/10.35445/alishlah.v17i2.6253