Published Online:July 2025
Product Name:The IUP Journal of Management Research
Product Type:Article
Product Code:IJMR010725
DOI:10.71329/IUPJMR/2025.24.3.5-25
Author Name:Naveen Kumar R, Shobha B G and Janani M
Availability:YES
Subject/Domain:Management
Download Format:PDF
Pages:5-25
The study examines the role of GenAI tools like ChatGPT in academic learning, focusing on how undergraduate and postgraduate students in Karnataka utilize the tool for assignment preparation and its broader impact on digital education. Using structured questionnaires and Structural Equation Modeling (SEM), the study evaluates the relationship between student perceptions, ethical considerations, concerns, and academic performance. The findings indicate that Gen AI tools enhances learning outcomes by improving assignment efficiency, offering personalized support, and reducing educational costs through time saving and decreased reliance on external resources. Four key factors—student views, concerns, perceived ethics, and faculty guidance—were identified as being influential in driving effective usage and academic performance. The study highlights the importance of responsible AI integration, emphasizing that ethical awareness and active faculty engagement are critical for maximizing GenAI tools educational potential. Overall, GenAI tools emerge as a cost-effective and transformative learning tool, enriching digital education, while fostering long term academic and professional skill development.
The rapid evolution of artificial intelligence (AI), particularly applications such as ChatGPT, is transforming various aspects of society, including higher education (Ng et al., 2021; Yu, 2023). Generative AI (GenAI) technologies are reshaping learning environment and pedagogical practices at multiple levels (Fullan et al., 2023; Sadeghinejad & Najmaei, 2023; Tajik & Tajik, 2024). ChatGPT, as a sophisticated large language model (LLM), offers functionalities such as text generation, translation, summarization, and informative responses (Bin-Hady et al., 2023; Cotton et al., 2023).