Will artificial intelligence drive the advancements in higher education? A tri-phased exploration

Satish Kumar, Purnima Rao, Shubham Singhania, Shubhangi Verma and Myra Kheterpal

Technological Forecasting and Social Change, 2024, vol. 201, issue C

Abstract: This study explores the impact and potential of generative AI, specifically Open AI's ChatGPT, on the transformation of higher education, particularly in the domain of business education. Adopting a qualitative mixed-methods research strategy, this study employs a three-phased methodology consisting of a systematic literature review, semi-structured interviews with academics, and text analysis of opinion posts on professional platforms. This comprehensive approach lends a holistic perspective, integrating insights from both national and global contexts, thereby enhancing the study's methodological rigor and ensuring a nuanced understanding of AI's implications in the educational landscape. ChatGPT, with its personalized and interactive features, enhances pedagogical innovation, academic integrity, and experiential engagements in the teaching-learning-assessment (TLA) process, as revealed by these methodologies' central themes. However, potential obstacles such as plagiarism and diminished development of interpersonal skills were also identified. The findings have significant ramifications for academia and the evolution of AI in education, casting light on the practical benefits and obstacles associated with AI implementation. The study identifies unexplored research avenues to encourage further investigation in this field. This paper contributes to the emerging discourse on AI-enhanced education in business institutions by providing a comprehensive and multifaceted examination of the impact of generative AI on higher education.

Keywords: AI; ChatGPT; SLR; Higher education; Interviews; Survey (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:201:y:2024:i:c:s0040162524000544

DOI: 10.1016/j.techfore.2024.123258

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