AI literacy in india through the lens of national policy: a four-dimensional evaluation

Subhodeep Mukhopadhyay — Wed, 07 Jan 2026 00:00:00 +0000

As India seeks to become a global leader in Artificial Intelligence (AI), its national AI policy plays a crucial role in promoting AI literacy. However, a gap exists in the literature regarding analyses of AI policy through the lens of established AI literacy models. This study qualitatively evaluates India's national AI policy document, India AI 2023: Expert Group Report – First Edition, using a four-aspect AI literacy framework. A thematic analysis examines how the policy addresses the dimensions of understanding, application, evaluation, and ethics. Results show that the policy is in line with the four aspects of the theoretical model. The study also identifies three additional sociocultural themes, inclusion, equity, and AI for social good. Gaps that could potentially hinder the policy's ability to fully promote inclusive AI literacy, and solutions, are also discussed. The study highlights the strengths and action ability of India’s national AI policy, and it's relevance to fostering AI literacy in India.

A comparative study of cloud-native vs. edge computing architectures for real-time data processing

Noor Alwan Malk — Wed, 04 Feb 2026 00:00:00 +0000

The fast adoption of Internet of Things (IoT) devices, autonomous systems and latency-sensitive applications has increased the need to have effective real-time data processing architectures. The paper will provide a detailed comparative analysis of cloud-native and edge computing systems in processing real-time data with a systematic literature review (SLR) and empirical benchmarking experiments. On the basis of a PRISMA-directed review of 87 papers (22 of which have been ultimately included) found after screening, we evaluate latency, throughput, energy consumption, scalability, fault tolerance, and security profiles of both paradigms. The experimental findings show that edge computing has a mean latency of 8.3 ms compared to cloud-native deployment of 142.7 ms, and the cloud-native architecture has higher availability at 99.95% and is scaled 3.8× times horizontally. It is suggested to use a hybrid framework, combining edge inference with cloud orchestration that is 94.2% times faster and has the same cloud-grade reliability. The ANOVA, regression modelling, and multi-criteria decision analysis (MCDA) data analysis shows that the choice of the optimal architecture is determined by application specific latency tolerance (α), data locality requirements and the budget constraint in the infrastructure. These results are applicable to the system architects operating in such sectors as smart healthcare, industrial IoT, autonomous vehicles, and smart grid management.

Journal of Artificial Intelligence,Machine Learning and Neural Network

AI literacy in india through the lens of national policy: a four-dimensional evaluation

A comparative study of cloud-native vs. edge computing architectures for real-time data processing