Generative AI in Secondary STEM Classrooms: Teachers’ Conditional Acceptance
Abstract
perceive and engage with generative artificial intelligence (GenAI) in instructional practice and identifies the institutional conditions influencing its responsible integration in public secondary schools. The study aims to understand how teachers regulate the use of GenAI within classroom instruction and professional decision-making.
Methodology: A qualitative exploratory design was employed using a focus group discussion involving thirteen secondary STEM teachers from a public secondary school. Data were collected using a semi-structured discussion guide informed by the Technology Acceptance Model (TAM). Thematic analysis following Braun and Clarke’s six-phase framework was conducted, with NVivo qualitative analysis software supporting coding and data organization.
Main Findings: The findings show that teachers’ engagement with generative artificial intelligence is characterized by partial familiarity, productivity-oriented use, and strong ethical concern. GenAI is primarily used for lesson preparation and instructional planning. Concerns regarding student overreliance, academic integrity, and learning authenticity limit unrestricted use, resulting in selective and regulated integration within classroom practice.
Novelty/Originality of this study: This study contributes qualitative evidence on secondary STEM teachers’ engagement with generative artificial intelligence, a context underrepresented in AI-in-education research. It introduces the concept of conditional acceptance, explaining how teachers selectively adopt GenAI through professional judgment and institutional constraints, extending technology acceptance perspectives beyond binary adoption models.
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