Teacher Readiness for Artificial Intelligence Use in School Classrooms: A Critical Theoretical Review and SWOT Analysis of International and Indian Research

Abstract

Artificial Intelligence (AI) is increasingly influencing school education through generative AI, intelligent tutoring systems, automated assessment, adaptive learning, learning analytics, conversational agents, and AI-supported instructional design. However, the successful classroom integration of AI depends less on technological availability than on teachers’ readiness to understand, evaluate, adopt, adapt, and ethically govern AI-supported practices. This theoretical research paper critically examines the emerging literature on school teachers’ readiness, willingness, interest, acceptance, trust, perceived usefulness, self-efficacy, and concerns regarding AI integration in classrooms. The review draws upon international peer-reviewed literature, including studies published in major Scopus-indexed journals, systematic reviews, UNESCO frameworks, and Indian policy and institutional sources. Particular attention is given to the relationship between technological competence, pedagogical knowledge, ethical awareness, perceived usefulness, AI anxiety, institutional support, professional development, and behavioural intention. The review indicates that teachers are generally interested in AI when they perceive clear pedagogical or workload-related benefits; nevertheless, willingness does not automatically translate into meaningful classroom integration. Research consistently identifies inadequate AI knowledge, limited pedagogical competence, insufficient professional development, infrastructure inequalities, privacy and data-security concerns, algorithmic bias, unreliable AI outputs, academic integrity, fear of professional displacement, and lack of institutional guidance as significant barriers. The paper further applies a SWOT framework to existing research, identifying methodological and conceptual strengths as well as weaknesses such as overdependence on self-report surveys, fragmented theoretical models, limited longitudinal research, and insufficient representation of developing countries, particularly India. Opportunities include AI-TPACK-based professional development, school-level AI policies, experiential teacher training, peer learning, curriculum redesign, and culturally responsive AI literacy. Threats include digital inequality, uncritical technological adoption, ethical risks, teacher deskilling, and widening educational disparities. The paper concludes that teacher readiness should be conceptualised as a multidimensional construct involving cognitive, technological, pedagogical, ethical, affective, institutional, and visionary dimensions. Implications are proposed for Indian teacher education, school leadership, policy, professional development, and future research.