Can ChatGPT Satisfy All? An Experimental Evidence

Preeti Sharma (), Sourav Banerjee and Anupam Bhattacharya
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Preeti Sharma: University of Engineering and Management
Sourav Banerjee: University of Engineering and Management
Anupam Bhattacharya: Institute of Engineering and Management

A chapter in Proceedings of the International Conference on Artificial Intelligence in Management for Business and Industrial Growth (AIMBIG 2025), 2025, pp 122-131 from Springer

Abstract: Abstract Generative artificial intelligence (AI) is still in its nascent stage. With rapid advancements in terms of technology and use, researchers hold diverse views about its ability to complement natural intelligence. Out of several apprehensions regarding its adaptability, the aspect of self-satisfaction is yet to be empirically studied. In this paper, we demonstrate a set of experiments on two groups of postgraduate students. Each group contained students with and without expertise on ChatGPT. Each student in both the groups are given tasks to develop two technical reports: with and without using generative AI tools respectively. The first report was generic in nature, whereas the second report was more domain-oriented requiring deeper understanding and complex search. The outputs are measured on three aspects: report generation time, self-satisfaction and search utility. Students with AI expertise took significantly more time for domain-specific topic under ChatGPT. Ownership and associated pride were significantly higher in self-generated reports. For experts, AI-generated reports for generic topic showed more enrichment. Ownership and pride is found to be higher in AI-generated domain-reports when compared with that of AI-generated generic reports amongst the expert group. The second group is controlled over incentivised mechanism, and they also underwent a short AI training program. The incentivised group demonstrated significantly higher ownership, pride and enrichment along with marginally lesser standard deviation across all variables. Positive correlation is observed between search time and satisfaction amongst the expert group. Based on the final result, a theoretical framework of ‘Natural-Artificial Indifference-curve’ is proposed for further experiment.

Keywords: Generative AI; ChatGPT; User satisfaction; Indifference curve (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-898-1_10

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