A Theoretical Framework on the Influence of Teachers’ Adaptation and Confidence Improvement in Artificial Intelligence-assisted Learning Environment on Teaching Efficacy

Abstract

This study develops a theoretical framework to analyze the intricate relationship between teachers’ adaptation, confidence enhancement, and teaching efficacy within the context of artificial intelligence-assisted learning environments. By integrating theories from multiple disciplines such as technology acceptance and diffusion theory, self-efficacy theory, and teacher professional growth theory, the research employs literature review, theoretical deduction, and conceptual model construction to establish a comprehensive understanding. This framework offers novel insights into the behavioral and psychological mechanisms underlying teachers’ responses to technological integration, significantly contributing to the field of behavioral sciences by providing a basis for predicting and influencing teacher behaviors in AI-driven educational reforms. Theoretical propositions suggest that individual differences among teachers, particularly those related to teaching experience and subject areas, play a significant role in their adaptation to AI-assisted teaching. The framework further postulates that personalized applications of intelligent teaching systems and AI-assisted teaching evaluation are crucial for enhancing teaching efficacy. It is also theorized that teacher training models based on AI and intelligent recommendation systems can profoundly promote professional growth and confidence, while robust school management support is conceptually identified as a strong guarantee for teachers to effectively apply AI technology in teaching. The study offers theoretical support for educational practice and proposes deepening theoretical research, promoting the practical transformation of outcomes, and strengthening interdisciplinary and intercultural studies, laying a foundation for future empirical investigations.