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.

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.

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