Fostering Innovation in Classrooms: AI Competence Self-Efficacy Among Elementary School Teachers in Nagaland

Rashmi, Pongmei Konyak

Artificial intelligence, teacher competency, self-efficacy, AI knowledge, AI pedagogy, AI assessment, AI ethics, human-centred education, professional engagement.

As Artificial Intelligence (AI) continues to reshape the educational landscape globally, it is imperative to assess teachers’ readiness and confidence in integrating AI into classroom practices, particularly in regions like Nagaland, India, where digital initiatives are still evolving. This study investigates the status of Teacher Artificial Intelligence Competence Self-Efficacy (TAICS) among primary school teachers in Nagaland across six key dimensions: AI Knowledge, AI Pedagogy, AI Assessment, AI Ethics, Human-Centred Education, and Professional Engagement. A descriptive research design was employed, using a structured self-efficacy scale administered to 252 primary teachers. The results reveal that the majority of teachers rated their overall AI competence at an average level, with relatively stronger self-efficacy in the areas of Human-Centred Education and Professional Engagement. However, notable deficiencies were observed in AI Pedagogy, Assessment, and Ethics, suggesting a gap between conceptual awareness and practical application. Despite statewide advancements such as the introduction of AI tools like Livi AI and partnerships with digital learning platforms the study identifies a lack of empirical research assessing teacher competence in this domain. This research, therefore, fills a critical gap by offering baseline data and actionable insights for policymakers and educators. Recommendations are provided for context-specific teacher training, ethical sensitization, infrastructure development, and the promotion of inclusive, AI-integrated teaching practices in Nagaland’s primary education system.

https://dx.doi.org/10.31873/IJEAS.12.06.02