Unleashing the Power of Generative Artificial Intelligence: Exploring its Boundless Potential and Overcoming Challenges in Academic Environments
Abstract
Integrating generative artificial intelligence tools can allow one to manage and develop novel business methods efficiently. With the introduction of self-learning generative artificial intelligence (GAI) tools, the corporate world is trying to figure out the applications and their business implications. Major technology companies like Microsoft, Google, Facebook, etc., have shown enormous interest in investing in ventures developing generative AI tools/models. These tools/models can be experimented with, and their business use is being explored. The study aims to explore the potential uses of generative AI (GAI) in academics through its current capabilities and applications. The study also highlights the potential challenges and concerns arising from using generative AI in academics. The study has used descriptive and quantitative methods to answer the research questions. The study has used a questionnaire based on the Likert scale to measure the significance of indirect variables, viz. perceived ease of use (PEOU) and perceived trust (PT) on direct variable adoption of generative artificial intelligence (AGAI) in academics. The sample size consists of 100 students and 100 university professors. The study Mentions that perceived ease of use (PEOU) positively influences GAI (AGAI) adoption. Also, perceived trust (PT) has a predictive ability of AGIA when controlling for PEOU. The use of generative AI in a few years will become imperative in most human lives, which makes it mandatory to explore the possible ways of its adoption in the mainstream rather than avoiding or restricting its usage in academics.
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Traditional plagiarism detection tools, such as Turnitin, are ineffective in identifying AI-generated content, often yielding false positives or negatives.
“Studies have revealed that traditional plagiarism detection tools, such as Turnitin, are ineffective in identifying AI-gen -erated content, often yielding false positives or negatives [ 31, 60, 85, 89, 90 ].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [90]
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Ncube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31 · see citing works
“This method allows educators to track students’ learning progress and interaction with AI throughout the assessment process, making it more difficult [ 31, 60, 85, 89, 90 ].”
- Authors
- Fegade, A., Raut, R., Deshpande, A., Mittal, A., Kaul, N., & Khanna, V.
- Affiliations
- Sri Balaji University, Pune (IN)
- Indira School of Business Studies,PGDM,Pune,India
- Chitkara University (IN)
- Symbiosis International University (IN)
- Sri Sukhmani Institute of Engineering & Technology,MBA Department,Mohali,India
- Venue
- 2023 6th International Conference on Contemporary Computing and Informatics (IC3I)
- Publisher
- IEEE
- Published
- 2023-09-14
- Pages
- 1243-1249
- OpenAlex
- W4391250126
- Cited by
- 18
- References
- 12
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 3
- FWCI
- 4.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Contemporary Computing and Informatics (IC3I) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, AI adoption in academia, perceived ease of use, perceived trust, technology acceptance, university students, university faculty
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