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Title: Impact of Generative AI (ChatGPT, Gemini, Copilot) on Learning Efficiency in the Indian Education System

Abstract: Generative Artificial Intelligence (GenAI) has emerged as one of the most influential technological developments shaping modern learning environments. Tools such as ChatGPT, Google Gemini, and Microsoft Copilot are increasingly being used by Indian students and teachers for explanation, summarization, content generation, and academic support. This study examines how these tools influence learning efficiency in the Indian education system. Using a mixed-method design consisting of a structured student–teacher survey and focused interviews, the study explores changes in understanding, productivity, doubt-clearing, academic confidence, and skill development. Findings reveal that GenAI significantly enhances conceptual clarity, reduces learning time, and supports self-paced learning. However, concerns remain regarding over-dependence, misinformation, ethical use, and unequal access. The paper concludes with recommendations for responsible AI integration in Indian classrooms.

By Shantam Babbar, Rajesh Kumar Raju, Monika Kumari
In Volume: 14,Issue: 2
Title: Components of the Money Supply Process in Bangladesh: An Analysis

Abstract: This study provides a comprehensive assessment of the components and effectiveness of the money supply process in Bangladesh, with a particular focus on its underlying determinants, trends, and policy implications. The primary objective is to evaluate whether the existing money supply mechanism, as implemented by the Bangladesh Bank, is effective in meeting the country’s macroeconomic objectives of price stability, economic growth, and financial stability. The research adopts a mixed-method approach, integrating both descriptive and econometric analyses. Descriptive statistics and trend analysis are used to examine the historical patterns of monetary aggregates namely the monetary base (H), money multiplier (m), narrow money (M1), and broad money (M2) over the past two decades. The results reveal that the money supply process in Bangladesh exhibits both short-run volatility and long-run stability, with the monetary base and money multiplier jointly influencing the expansion of M2. Co-integration tests confirm the existence of long-term equilibrium relationships among monetary aggregates, while ECM results suggest a moderate speed of adjustment toward equilibrium following shocks. However, structural break analysis indicates that global financial crises, domestic policy shifts, and recent pandemic-related disruptions have caused significant short-term deviations. The findings highlight that although the Bangladesh Bank’s monetary policy framework has been largely effective in steering the long-run trajectory of the money supply, challenges remain in managing short-run fluctuations and in aligning monetary expansion with real economic growth. The study concludes with policy recommendations aimed at enhancing the effectiveness of the money supply process, including improving forecasting models, strengthening monetary transmission mechanisms, and enhancing coordination between monetary and fiscal policy.

By Sabiha Akter
In Volume: 14,Issue: 2
Title: Comparative Analysis of Risk Management Strategies for Non-Performing Assets: A Case Study of SBI and ICICI Bank in India

Abstract: This study examines the differential approach to risk management strategies concerning Non-Performing Assets (NPA) within India's two foremost banks – the Indian Public Sector Bank, State Bank of India (SBI) and the Indian Private Sector Bank, ICICI Bank. While comparing the two banks, using a mixed-method approach, the research combines quantitative analysis of trends in financial indicators (Gross and Net NPA ratios, Provision Coverage Ratio and Return on Assets) and a qualitative analysis of credit appraisal and monitoring and recovery frameworks. Data from 2010-2025 were taken from RBI publications, annual reports and credible academic studies, so there was authenticity and reliability of data. Findings show that SBI's recovery centered reforms such as better provisioning (PCR increase from 70.88% to 75%), restructuring under Insolvency and Bankruptcy Code (IBC) and improved post-sanction monitoring have led to a reduction in Gross NPAs by 47% and significant improvement in profitability (ROA increased from 0.48% to 1.1%). On the other hand , ICICI Bank's proactive and technology-driven risk model, with AI-driven early warning systems, digitised credit scoring and stringent underwriting, regularly maintained low NPAs (down from 3.05% to 1.67%) and enhanced profitability (ROA doubling to 2.0%). Correlation study reports we see that there is a very strong inverse relationship between NPAs, provisioning, Net NPA ratio and profitability (r approx –0.9) which means as NPAs and provisioning go up Net NPA ratio and profitability goes down. This is proof that what we put in place for credit assessment, early identification and recovery does in fact directly improve banks’ performance. We found out that what made SBI successful was its recovery and restructurizing which made ICICI’s success was in prevention and technology based monitoring. Also brought to light is the fact that what is key in the Indian banking system is the integration between AI, data analysis and good governance which banks use in risk management and in the end in the maintenance of asset quality in a sustainable way.

By Achintya Kumar Gupta, Narendra Pal Singh
In Volume: 14,Issue: 2
Title: Impact of Generative AI (ChatGPT, Gemini, Copilot) on Learning Efficiency in the Indian Education System

Abstract: Generative Artificial Intelligence (GenAI) has emerged as one of the most influential technological developments shaping modern learning environments. Tools such as ChatGPT, Google Gemini, and Microsoft Copilot are increasingly being used by Indian students and teachers for explanation, summarization, content generation, and academic support. This study examines how these tools influence learning efficiency in the Indian education system. Using a mixed-method design consisting of a structured student–teacher survey and focused interviews, the study explores changes in understanding, productivity, doubt-clearing, academic confidence, and skill development. Findings reveal that GenAI significantly enhances conceptual clarity, reduces learning time, and supports self-paced learning. However, concerns remain regarding over-dependence, misinformation, ethical use, and unequal access. The paper concludes with recommendations for responsible AI integration in Indian classrooms.

By Shantam Babbar, Rajesh Kumar Raju, Monika Kumari
In Volume: 14,Issue: 2
Title: Digital Trade and E-commerce Reshaping Global Trade Architecture in the 21st Century

Abstract: The rapid proliferation of digital technologies has fundamentally transformed the global trade landscape, with e-commerce and digital trade emerging as dominant forces reshaping traditional trade architectures. This paper examines the multifaceted impact of digital trade and e-commerce on global trade structures, analyzing key trends, challenges, and policy implications. Through comprehensive analysis of empirical data and theoretical frameworks, we demonstrate how digital platforms have reduced transaction costs, democratized access to international markets, and created new regulatory challenges. Our findings indicate that digital trade now accounts for a significant portion of global GDP, with cross-border e-commerce growing at unprecedented rates. However, this transformation has also highlighted critical issues including digital divides, data governance concerns, and the need for updated international trade frameworks. This research contributes to understanding how digital trade is reconfiguring global value chains and what policy interventions are necessary to ensure inclusive and sustainable growth in the digital economy.

By Pankaj Yadav
In Volume: 14,Issue: 2
Title: An Analytical Study of the Legal Position and Role of Securities & Exchange Board of India (SEBI)

Abstract: This paper highlights a long journey towards regulatory enhancement within the SEBI ecosystem through the lens of data analysis. The objective is to clear amalgamate existing SEBI systems with those deemed desirable according to the fundamental principles of regulation. It also provides the whole outcome of the research study based on the analysis. It also suggest various policy implications to the researcher and government for an efficient and transparent regulatory environment in the country. In conclusion, it provides a thorough analysis of the enforcement procedures employed by the Securities and Exchange Board of India (SEBI), elucidating their effectiveness, equity, and efficiency. Through rigorous data analysis and empirical inquiry, we have dissected the regulatory landscape, uncovering insights that transcend mere statistics. Our findings add to the current conversation about how well regulations work, how accountable institutions are, and how safe investors are in India's financial markets. They also aim to make SEBI's regulatory system more open, accountable, and trustworthy.

By M.P. Verma, Deepa Singh Yadav
In Volume: 14,Issue: 2

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