Can ChatGPT Satisfy All? An Experimental Evidence

DOI

10.2991/978-94-6463-898-1_10How to use a DOI?

Keywords

Generative AI; ChatGPT; User satisfaction; Indifference curve

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.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Preeti Sharma
AU  - Sourav Banerjee
AU  - Anupam Bhattacharya
PY  - 2025
DA  - 2025/11/18
TI  - Can ChatGPT Satisfy All? An Experimental Evidence
BT  - Proceedings of the International Conference on Artificial Intelligence in Management for Business and Industrial Growth (AIMBIG 2025)
PB  - Atlantis Press
SP  - 122
EP  - 131
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-898-1_10
DO  - 10.2991/978-94-6463-898-1_10
ID  - Sharma2025
ER  -