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Research Article
📘 Vol. 3 Issue 2 (2026): Current Issues

ISSN: 2277-405X

The Future of Generative AI: ChatGPT, Emerging Applications, and Human–AI Collaboration

Minakshi Tambe

Paper ID: IJATRD-2026-00035

DOI :

DOI: https://doi.org/10.67750/ijatrd.v3.i2.35

Keywords:

Keywords:

Generative AI ChatGPT

Abstract:

Abstract

 

Generative Artificial Intelligence (Generative AI) has revolutionized the way humans create, analyze, and interact with digital information. Among the most widely adopted generative AI tools, ChatGPT has demonstrated significant potential across education, healthcare, business, research, software development, and customer support by generating human-like responses and assisting in complex decision-making tasks. Despite these advantages, the rapid adoption of ChatGPT introduces several challenges, including hallucinated outputs, algorithmic bias, privacy concerns, ethical dilemmas, misinformation, and intellectual property issues. This paper reviews the applications, challenges, and opportunities of Generative AI with a particular focus on ChatGPT and explores the emerging concept of AI–human collaboration. The study is based on an extensive review of recent scholarly literature and highlights how collaborative intelligence can enhance productivity, creativity, and decision-making while maintaining human oversight. The findings suggest that responsible AI governance and ethical implementation are essential for maximizing the benefits of Generative AI across diverse domains.

How to Cite

Tambe, M. (2026, September 29).
The Future of Generative AI: ChatGPT, Emerging Applications, and Human–AI Collaboration.
https://ijatrd.org/en/article/2026-00035

References:

References

1. OpenAI. (2023). GPT-4 Technical Report. arXiv. https://arxiv.org/abs/2303.08774

2. Dwivedi, Y. K., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI. International Journal of Information Management, 71, 102642.

3. Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.

4. Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154.

5. Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30.

6. Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. Stanford University.

7. Bubeck, S., et al. (2023). Sparks of Artificial General Intelligence: Early Experiments with GPT-4. Microsoft Research.

8. Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877–1901.

9. Wei, J., et al. (2022). Emergent Abilities of Large Language Models. Transactions on Machine Learning Research.

10. Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694.

11. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

12. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

13. UNESCO. (2023). Guidance for Generative AI in Education and Research.

14. OECD. (2024). OECD Framework for Generative Artificial Intelligence.

15. World Economic Forum. (2024). The Future of Jobs Report 2024.

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Published

2026-09-29

Issue

Vol. 3 No. 2 (2026): Current Issues

Section

Articles

How to Cite

The Future of Generative AI: ChatGPT, Emerging Applications, and Human–AI Collaboration. (2026). International Journal for Advancements in Technical Research & Development, 3(2), 1-6. https://doi.org/10.67750/ijatrd.v3.i2.35
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