Readiness and Challenges among School Teachers for Integrating the Indian Knowledge System (IKS) through Artificial Intelligence: A Study of Jharkhand An Empirical Study

Abstract

The National Education Policy (NEP) 2020 envisions the integration of the Indian Knowledge System (IKS) across school and higher education curricula, and simultaneously promotes the use of Artificial Intelligence (AI) as a pedagogical and administrative tool. The confluence of these two mandates places school teachers at the centre of a complex professional transition that requires them to be conceptually rooted in indigenous knowledge traditions while being technologically competent enough to employ AI-based tools for content delivery, translation, contextualisation and assessment. This study examined the readiness and challenges of school teachers in Jharkhand for integrating IKS through AI-enabled pedagogy. A descriptive survey design was employed on a stratified random sample of 320 school teachers drawn from eight districts of Jharkhand (Ranchi, Dhanbad, East Singhbhum, West Singhbhum, Hazaribagh, Deoghar, Gumla and Palamu). Data were collected using a self-constructed and validated tool, the Teachers’ Readiness and Challenges for IKS-AI Integration Scale (TRC-IKS-AI), comprising four readiness dimensions (cognitive, technological, pedagogical and attitudinal) and four challenge dimensions (infrastructural, training-related, curricular/content-related and attitudinal-resistance). Results indicated that the overall readiness of teachers was moderate (M = 3.26, SD = 0.30 on a 5-point scale), with attitudinal readiness being the strongest dimension (M = 3.66) and technological readiness the weakest (M = 2.90). Teachers reported a high overall level of challenge (M = 3.58), with training-related gaps (M = 4.05) and infrastructural deficits (M = 4.00) emerging as the most severe barriers. Independent samples t-tests revealed statistically significant differences in readiness by locale, with urban teachers (M = 3.33) scoring higher than rural teachers (M = 3.21, p < .001), and in challenges by locale, with rural teachers reporting significantly higher challenges (M = 3.64) than urban teachers (M = 3.49, p < .001). One-way ANOVA showed a significant effect of academic qualification on readiness (F = 4.10, p = .017), with doctorate/M.Phil. holders reporting the highest readiness (M = 3.42). Gender, school management type and teaching experience did not yield statistically significant differences. The study concludes that while Jharkhand’s school teachers hold favourable attitudes toward IKS-AI integration, structural and capacity-building gaps — particularly digital infrastructure and in-service training — constrain actual readiness, especially in rural and tribal-dominated districts. Policy-level interventions in teacher training, infrastructure development, and locally contextualised AI-IKS content are recommended.