NOLEGEIN- Journal of Information Technology & Management

Scientists’ Perspectives on Artificial Intelligence (AI) in Knowledge Resource Centre Services of the Council of Scientific and Industrial Research (CSIR) Laboratories in Uttar Pradesh, India

M. P. Singh, Ritik Bajpai — Sat, 04 Apr 2026 00:00:00 +0000

This study examines scientists’ perceptions regarding the adoption and use of Artificial Intelligence (AI) tools within the Knowledge Resource Centres (KRCs) of CSIR laboratories located in Uttar Pradesh, India. A structured survey methodology was employed to gather data from scientists working at CSIR-CDRI, CSIR-CIMAP, CSIR-IITR, and CSIR-NBRI. The findings indicate that most respondents are familiar with AI-based applications and actively utilize them for various research-related activities, including literature searching, plagiarism detection, language refinement, reference management, and data organization. Despite this widespread awareness and usage, the level of satisfaction with AI- enabled services varies across laboratories. Key challenges identified include insufficient hands-on training, restricted access to licensed AI tools, technical limitations, and inconsistent institutional support. Some respondents also highlighted concerns about data privacy and the reliability of AI-
generated outputs. Nevertheless, the overall perception toward AI integration in KRC services remains largely positive. Scientists believe that AI has significant potential to enhance research productivity, streamline information retrieval, and modernize library services. The study concludes that for effective implementation, KRCs must invest in continuous capacity-building programs, ensure equitable access to advanced AI tools, and strengthen technical and administrative support systems. Such measures will enable optimal utilization of AI technologies, thereby improve scientific research outcomes and advancing knowledge management practices within CSIR institutions.

A COMPARATIVE STUDY OF AI-DRIVEN AND TRADITIONAL APPROACHES TO FINANCIAL COVENANT AND COMPLIANCE MONITORING AT MUFIN GREEN FINANCE

Richa Mehta, Aditi Jain — Wed, 03 Jun 2026 00:00:00 +0000

This study looks at how AI-driven financial covenant and compliance monitoring stacks up against traditional methods used by Mufin Green Finance, a leading non-banking financial company in India’s green finance sector. Traditionally, covenant checks depend on manual reviews, periodic audits, and rule-based systems. These processes can be slow, require a lot of labor, and are often delayed by human error. With the rise of machine learning, natural language processing, and predictive analytics, financial institutions can now analyze borrower data in real time. They can identify anomalies early and predict risks rather than just reacting to them later. The research compares both methods based on accuracy, efficiency, scalability, and cost. It also examines practical issues like data quality, model interpretation, and regulatory acceptance. The study is part of a broader change in India’s financial services industry, where NBFCs like Mufin Green Finance are essential for improving access to credit and supporting sustainable development through electric mobility and clean energy financing. By looking at Mufin’s operations, strategic goals, and competitive environment, the project shows how AI- enabled monitoring can improve risk management, support compliance, and strengthen portfolio resilience. The findings add to the ongoing conversation about the role of AI in creating sustainable, transparent, and future-ready financial systems, particularly in emerging markets where green finance is growing quickly.

Evolving Paradigms of Research Data Management (RDM): A Conceptual Framework for University Libraries in Rajasthan

Manish Sharma, Praveen Babel — Thu, 02 Apr 2026 00:00:00 +0000

The challenge of managing vast amounts of research data is relatively new to Indian universities and is often tied to the issues of open science and data-intensive scholarly research. The present study focuses on the shifts relating to the Research Data Management (RDM) process within the context of higher education systems of the university in the Indian state of Rajasthan. I propose the Integrated RDM Readiness and Stewardship Framework (IRRS-Framework) to fully address these gaps. The IRRS-Framework is a multi-faceted optimistic model of RDM governance within policy, global perspectives, and cross-national infrastructure which includes institutional and ethical RDM stewardship, as well as the paradigms of human governance and capacity. The stewards of IRRS- Framework maintain RDM policy interoperability, and governance and ethics RDM policy alignment on the diverse resource universities of Rajasthan. RDM is often perceived as a technological problem, however, I strongly argue that success stems from holistic shifts within the institute. This paper argues that infrastructure development, capacity building, and governance and ethical accountability pathways emerging from India’s regional university framework and data policy can help drive systemic institutional shifts. RDM ecosystems shaped by institutional policy and governance in Rajasthan’s universities are locally grounded, ethically responsive, and globally aligned.

What Does Knowledge Systems Mean: Answers from Literature

Supriya Raithatha — Wed, 03 Jun 2026 00:00:00 +0000

The Indian Knowledge System (IKS) constitutes a long-standing and evolving tradition of knowledge production that harmonizes empirical reasoning with philosophical and metaphysical inquiry. Rooted in ancient Indian intellectual practices, IKS presents a comprehensive worldview that integrates Jnan Vignan (systematic knowledge and science) with Jeevan Darshan (a holistic philosophy of life). This paper explores the conceptual foundations and historical development of IKS, tracing its progression from early Vedic literature to its formal recognition and integration within the National Education Policy (NEP) 2020. The study critically examines the epistemological basis of IKS, with particular emphasis on the theory of Pramanas as diverse and valid means of knowledge acquisition. It further analyses the structural organization of IKS through the framework of the 14 Vidyas and 64 Kalas, highlighting their interdisciplinary nature. By comparing IKS with dominant Western knowledge paradigms and other indigenous systems, the paper underscores its distinctive non-dualistic approach, wherein the knower and the known are viewed as interdependent. Empirical relevance and scientific credibility are demonstrated through selected case studies, including advanced metallurgical practices, such as Wootz steel, mathematical innovations of the Kerala School, and principles of sustainable design, embodied in Vastu Shastra. Finally, the paper discusses contemporary approaches to assessing the relevance and continuity of IKS, introduces the notion of cognitive justice, and argues for the system’s potential contribution to addressing modern challenges related to sustainability, ethical development, and mental well-being.

Innovating Insurance Lending in Indian Fintech: A Study of Bimapay’s EMI Model, Business Strategy, and Role in Alternative Credit for Insurance Inclusion

Richa Mehta, Rimjhim Kapoor — Wed, 03 Jun 2026 00:00:00 +0000

This study examines EMI-based insurance lending within India’s fintech ecosystem, focusing on Bimapay Finsure Pvt. Ltd. and its role in improving insurance accessibility through technology-enabled premium financing. Using primary data collected from 41 employees across finance, operations, and technology teams, the research evaluates employee understanding of the EMI model, perceptions of business model sustainability, and attitudes toward AI-driven reconciliation. Statistical analyses – including descriptive analysis, correlation, and regression – reveal that employees generally have positive perceptions of efficiency, accuracy, and the usefulness of AI tools. Correlation results show strong positive relationships between system effectiveness, accuracy, and employee trust in AI (r = .579, p < .001), while regression findings confirm that key variables such as perceived accuracy (VAR00005) significantly predict operational efficiency (p < 0.05). However, certain challenges negatively influence outcomes, such as delays due to manual processes and mismatch-related issues (VAR00007), which showed a significant negative effect (p < 0.05). Descriptive analysis indicates consistent agreement among employees regarding the need for automation and centralized dashboards. Overall, the findings highlight that although Bimapay has adopted several digital tools, gaps remain in reconciliation automation, interdepartmental coordination, and real-time data visibility. The study concludes that enhanced API integration, AI-supported anomaly detection, and unified dashboards are crucial to

Innovative Teaching–Learning Practices in Computer Science

Nitin D. Chudasama — Thu, 12 Mar 2026 00:00:00 +0000

Advances in digital technologies, rapid innovation in the information technology industry, changing learner expectations, and the growing emphasis on higher-order thinking skills have significantly transformed the goals and practices of Computer Science education. Traditional lecture-centric teaching approaches, where students primarily act as passive recipients of information, are increasingly inadequate for preparing learners to address real-world, ill-structured, and complex technological problems. In today’s dynamic digital environment, Computer Science graduates are expected not only to possess sound theoretical knowledge and programming proficiency but also to demonstrate the ability to design, analyze, implement, and maintain complex systems, work effectively in collaborative teams, communicate ideas clearly, and continuously adapt to evolving technologies. These evolving expectations have created a strong need for innovative teaching–learning practices that shift the focus from one-way knowledge transmission to student-centered, interactive, and experiential learning environments. Such practices encourage learners to actively engage with content, apply concepts to practical scenarios, reflect on their learning processes, and develop problem-solving and critical-thinking skills. Active learning, project-based learning, flipped classrooms, and technology-
enabled instruction play a crucial role in fostering deeper understanding and sustained engagement in Computer Science courses . In this context, the present paper explores innovative teaching–learning practices in the Computer Science stream, with a particular focus on pedagogical models, technology integration, active learning strategies, assessment innovations, and emerging educational trends. By examining these approaches, the paper aims to highlight how innovative practices can enrich teaching, enhance student motivation and participation, and ultimately improve learning outcomes, thereby aligning Computer Science education with the demands of the modern digital economy

Ethical and Policy Gaps for Responsible AI in Indian Healthcare: Review Toward Viksit Bharat

Santosh Jain, Pinkle Vanavat — Tue, 28 Apr 2026 00:00:00 +0000

The accelerated adoption of Artificial Intelligence (AI) within the Indian healthcare sector – particularly in medical imaging, clinical decision support, and predictive diagnostics – has emerged as a critical enabler of the national vision of Viksit Bharat (Developed India). AI-driven technologies offer substantial potential to improve diagnostic accuracy, optimize healthcare delivery, and address systemic challenges such as workforce shortages and regional disparities. This review critically examines existing academic literature, government reports, and publicly available policy documents to assess the ethical, regulatory, and governance frameworks shaping Health AI implementation in India. While the country leverages robust Digital Public Infrastructure (DPI) to support large-scale digital health initiatives, the analysis reveals notable policy and regulatory deficiencies. Foremost among these is the absence of a comprehensive, standalone national Health AI policy that clearly defines accountability, liability, and governance mechanisms. Significant gaps persist in mandating independent audits to identify and mitigate algorithmic bias, particularly in high-stakes diagnostic applications. Furthermore, existing policies inadequately address data protection, transparency, and informed consent within DPI-enabled AI systems, raising concerns regarding patient privacy and trust. The study underscores the urgent need for a coherent policy framework that integrates ethical safeguards, legal clarity, and technical oversight. Addressing these shortcomings is essential to ensure that AI adoption in Indian healthcare remains reliable, equitable, and socially responsible, while effectively contributing to national development objectives and public health outcomes.