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Exploring the data analytical capabilities of generative AI tools ChatGPT and Google Bard (Gemini): a comparative analysis of GenAI tools with excel and python

Nikhat Afshan et al.

International Journal of Management and Decision Making2025https://doi.org/10.1504/ijmdm.2026.150375article
AJG 1
Weight
0.50

What the paper says

ChatGPT and Google Bard (now renamed as Google Gemini), the latest iterations of real-time generative artificial intelligence (GenAI), are being extensively used across discipline including education. There has been growing interest amongst academics to integrate GenAI into teaching to create personalised learning experience for students. Though the recent versions of GenAI have been designed to tackle complex natural language and data analytical problems, concerns have been raised regarding the reliability of the outputs generated by these GenAI tools. This paper tries to explore the data analytical capabilities of ChatGPT 4.0 and Bard and understand its suitability to teach data analytics courses. The study conducts and compares three statistical analyses viz. regression analysis, classification analysis and linear programming problem using GenAI tools (ChatGPT 4 and Google Bard) and traditional software (Python and Excel). The study discusses the nuances of using these tools effectively by students, educators, and practitioners.

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https://doi.org/https://doi.org/10.1504/ijmdm.2026.150375

Or copy a formatted citation

@article{nikhat2025,
  title        = {{Exploring the data analytical capabilities of generative AI tools ChatGPT and Google Bard (Gemini): a comparative analysis of GenAI tools with excel and python}},
  author       = {Nikhat Afshan et al.},
  journal      = {International Journal of Management and Decision Making},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijmdm.2026.150375},
}

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Exploring the data analytical capabilities of generative AI tools ChatGPT and Google Bard (Gemini): a comparative analysis of GenAI tools with excel and python

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.