Article’s

Advancements in artificial intelligence (AI) have stimulated the development of AI tools for education, offering potential to transform teaching and learning processes. However, teachers’ acceptance remains a critical barrier to successful integration, and little is known about how their underlying pedagogical beliefs shape perceptions of different AI implementations. This study examines how secondary school teachers’ pedagogical orientations influence their acceptance of AI, specifically comparing collaborative “co-pilot” tools that augment teacher decision-making with autonomous systems that operate independently. A quantitative, cross-sectional survey design was employed with 500 teachers in Hyderabad, Pakistan. The questionnaire measured constructivist and instructivist pedagogical beliefs using adapted scales and assessed AI acceptance through an extended Technology Acceptance Model applied to two distinct operational scenarios. Results indicate that teachers demonstrate a clear and significant preference for collaborative AI over autonomous systems, with higher perceived usefulness and behavioral intention for co-pilot implementations. Constructivist pedagogical beliefs strongly and positively predict acceptance of collaborative AI tools, while instructivist beliefs positively predict acceptance of autonomous systems. Technology use frequently emerges as a consistent positive predictor across all acceptance models, and perceived ease of use significantly influences behavioral intentions. The regression models explain 8-15% of variance in AI acceptance, indicating meaningful but partial explanatory power of pedagogical beliefs. These findings highlight that teachers’ educational philosophies serve as crucial filters through which they evaluate technological innovations, with constructivist-oriented teachers favoring augmentation tools and instructivist-oriented teachers showing greater openness to automation. The study contributes to extending technology acceptance frameworks by demonstrating pedagogical beliefs as significant external variables and provides practical guidance for developing human-centered AI designs that align with teachers’ diverse educational approaches and values