International Journal of Science, Engineering and TechnologyAuthors: Jyoti, Akshita Tiwari, Divyansh Kajla, Aman Tomar
Abstract: Artificial Intelligence (AI) plays a major role in educational transformation in India, according to the National Education Policy 2020 (NEP 2020). Nonetheless, the accelerated rush to implement computing thinking and AI since Grade 3 poses a threat to increasing the rural-urban digital divide because of poor infrastructure, language differences, and inequities in rural government schools. Although most urban schools are enjoying the use of cloud-based and generative AI, many rural schools are still without reliable electricity, consistent internet connections, adequate hardware, and well-trained teachers. The paper suggests a Localized AI Implementation Framework to overcome the policy gaps in last-mile in rural Indian classrooms. The research follows a three-stage process via a Sequential Exploratory Mixed-Methods design (QUAL-quan) based on the introduction of socio-technological adoption theories and the national data used in the study, including ASER and UDISE+. Phase I will entail qualitative interviews with teachers, administrators, and stakeholders in the community in linguistically diverse districts to discover contextual barriers. Phase II measures these insights using big data surveys using UTAUT measures of technology acceptance. Phase III tests an offline-first Edge AI model on systems based on Raspberry Pi to test the infrastructural resilience, vernacular AI performance, teacher workload reduction, and student learning outcomes. It has three pillars, namely: (1) Infrastructure Resilience, through edge computing that makes it possible to use AI without constant internet connectivity; (2) Localized Multilingual Pedagogy, in which AI automates administrative work without reducing pedagogical authority of teachers; and (3) Human-Centric Teacher Empowerment, by which AI automates administrative work and does not decrease the pedagogical authority of teachers. The study hypothesizes that culturally resonant, offline AI systems will lead to better STEM learning, use fewer resources than cloud-dependent models, and that teacher time resources can be used to mentor students. It is anticipated to result in higher levels of digital equity, teacher efficiency, and quantifiable student engagement and performance in the rural setting. On the whole, the framework proposes decentralized and context-sensitive AI ecosystems that can match the ambitions of NEP 2020 with the context of underserved classrooms in rural areas. It provides policy-consistent and scalable ways of ensuring fair adoption of AI across the country.
DOI: http://doi.org/10.5281/zenodo.21701251
