Generative AI and Chatbot Assistance: A Comprehensive Review

By: Pinaki Sahu, International Centre for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,


Generative artificial intelligence, or AI, has emerged as a revolutionary technology that proves adaptable. Generation models are used in Chatbot Help, one of the most popular areas, to improve natural language generation and translation. This research article provides a comprehensive summary of the current state of Generative AI and its applications with the help of chatbots. We explore basic concepts, implementation strategies, challenges, and possible directions for future research and development of these dynamic relationships.


Advances in Generative AI have transformed natural language processing by allowing machines to understand and act human-like Chatbot support is a type of conversational AI that interacts with humans through reproductive patterns to understand their environment meet and understand their language. This article explores the relationship between chatbot support and generative AI, its benefits, drawbacks, and potential future applications.

Generative AI: Foundations and Techniques

We give an overview of the main ideas behind Generative AI in this section. General pre-trained transformers, or GPT, are examples of general generative models and their training techniques. By analysing cognitive processes, transfer learning, and fine-tuning mechanisms, generational models describe how language understanding is acquired and competency is generated[1].

Chatbot Assistance: Evolution and Applications:

The transition from rule-based to AI-powered conversational agents is explored in the history of chatbots. We explore Chatbot Assistance’s varied uses in a variety of sectors, such as customer support, medical, education, and more. The incorporation of generative AI into chatbot frameworks has apparent advantages, as evidenced by case studies that showcase effective implementations[2].

Fig.1.Integration of Generative AI and Chatbot Assistance[4]

Challenges in Generative AI for Chatbot Assistance:

While generative AI has shown remarkable progress, challenges persist in deploying effective chatbot assistance systems. Ethical concerns, bias in language generation, and the potential for misinformation are explored. We discuss ongoing research initiatives and proposed solutions to mitigate these challenges, emphasizing the importance of responsible AI development[3].

Future Directions and Innovations

The article anticipates future developments and breakthroughs in generative AI for chatbot support. We talked about new developments including enhanced contextual comprehension, multi-modal capabilities, and domain-specific knowledge integration. In addition, we have to find whether self-supervised learning and reinforcement learning could enhance chatbot performance[4].


This study concludes with a thorough summary of the complementary roles that chatbot assistance and generative AI play in one another. We emphasise the revolutionary effects of generative models on the formation and interpretation of natural language, while also recognizing the obstacles that demand further study and advancement. The use of generative AI to chatbot support has enormous potential to transform human-machine interactions in a variety of fields as technology develops.


  1. van der Zant, T., Kouw, M., & Schomaker, L. (2013). Generative artificial intelligence (pp. 107-120). Springer Berlin Heidelberg.
  2. A. Bhharathee, S. Vemuri, B. Bhavana and K. Nishitha, “AI-Powered Student Assistance Chatbot,” 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2023, pp. 487-492, doi: 10.1109/IDCIoT56793.2023.10053439.
  3. Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304.
  4. Dwivedi, Y. K., Pandey, N., Currie, W., & Micu, A. (2023). Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: practices, challenges and research agenda. International Journal of Contemporary Hospitality Management.
  5. Poonia, V., Goyal, M. K., Gupta, B. B., Gupta, A. K., Jha, S., & Das, J. (2021). Drought occurrence in different river basins of India and blockchain technology based framework for disaster management. Journal of Cleaner Production312, 127737.
  6. Gupta, B. B., & Sheng, Q. Z. (Eds.). (2019). Machine learning for computer and cyber security: principle, algorithms, and practices. CRC Press.
  7. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-43.
  8. Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-24.

Cite As

Sahu P. (2024) Generative AI and Chatbot Assistance: A Comprehensive Review, Insights2Techinfo, pp.1

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