ChatGPT for Intelligent Human–AI Interaction: Opportunities and Limitations
DOI:
https://doi.org/10.37547/ibast/Volume06Issue08-03Keywords:
ChatGPT, Human–AI Interaction, Conversational Artificial Intelligence, Large Language ModelsAbstract
The rapid advancement of large language models (LLMs) has significantly transformed the landscape of intelligent human–AI interaction. Among these developments, ChatGPT has emerged as a widely adopted conversational artificial intelligence system capable of supporting knowledge acquisition, content generation, programming assistance, educational tutoring, healthcare communication, and scientific writing. Its ability to understand natural language, generate context-aware responses, and perform diverse reasoning tasks has positioned it as a representative model of next-generation human-centered AI. Despite these advantages, concerns regarding factual reliability, hallucination, transparency, ethical responsibility, privacy, and evaluation consistency continue to challenge its widespread adoption. ChatGPT for Intelligent Human–AI Interaction: Opportunities and Limitations provides a comprehensive review of the technological foundations, capabilities, applications, and constraints associated with ChatGPT, emphasizing its role in enabling intelligent human–AI collaboration (Aczel & Wagenmakers, 2023). The study adopts a qualitative review methodology based exclusively on twelve selected scholarly references related to chatbot technology, large language models, transparency, reasoning evaluation, academic writing, healthcare applications, multilingual performance, and AI limitations. Comparative synthesis reveals that ChatGPT demonstrates remarkable versatility across multiple domains while simultaneously exhibiting persistent challenges related to hallucination, explainability, domain-specific accuracy, and responsible deployment. The review further identifies research gaps involving transparency frameworks, evaluation methodologies, and human-centered governance mechanisms. The findings suggest that future conversational AI systems should integrate explainable reasoning, improved factual verification, multimodal intelligence, and ethical safeguards to maximize trustworthy human–AI interaction. ChatGPT for Intelligent Human–AI Interaction: Opportunities and Limitations therefore contributes a structured academic synthesis that supports researchers, practitioners, educators, and policymakers in understanding both the opportunities and limitations associated with modern conversational AI technologies (Aczel & Wagenmakers, 2023).
Downloads
References
1. Aczel, B., & Wagenmakers, E.-J. (2023). Transparency guidance for ChatGPT usage in scientific writing. PsyArXiv Preprint.
2. Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In I. Maglogiannis, L. Iliadis & E. Pimenidis (Eds.), Artificial intelligence applications and innovations (pp. 373–383). Springer International Publishing.
3. Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15(2), e35179.
4. AL-Qadri, A. H., & Ahmed, S. A. (2023). Assessing the ChatGPT accuracy through principles of statistics exam: A performance and implications. arXiv Preprint.
5. Antaki, F., Touma, S., Milad, D., El-Khoury, J., & Duval, R. (2023). Evaluating the performance of ChatGPT in ophthalmology: An analysis of its successes and shortcomings. medRxiv Preprint.
6. Aydın, Ö., & Karaarslan, E. (2022). OpenAI ChatGPT generated literature review: Digital twin in healthcare. In Ö. Aydın (Ed.), Emerging computer technologies 2 (pp. 22–31). İzmir Akademi Dernegi.
7. Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., :::, & Fung, P. (2023). A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. arXiv Preprint: 2302.04023.
8. Basic, Z., Banovac, A., Kruzic, I., & Jerkovic, I. (2023). Better by you, better than me, ChatGPT3 as writing assistance in students essays. arXiv Preprint: 2302.04536.
9. Bordt, S., & von Luxburg, U. (2023). ChatGPT participates in a computer science exam. arXiv Preprint: 2303.09461.
10. Borji, A. (2023). A categorical archive of ChatGPT failures. arXiv Preprint: 2302.03494.
11. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., :::, & Amodei, D. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan & H. Lin (Eds.), Advances in Neural Information Processing Systems (pp. 1877–1901). NIPS Foundation.
12. Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., :::, & Raffel, C. (2021). Extracting training data from large language models. In 30th USENIX Security Symposium, 2633–2650.
13. K. Ramamurthy, R. K. Konduru and N. Amanmadov, "EvoGraphCoder: An Evolutionary Graph-Reasoning Framework for Self-Adaptive Software Engineering," in IEEE Access, vol. 14, pp. 63063-63076, 2026, doi: 10.1109/ACCESS.2026.3686019.
14. Geo Philip, Paulson, Integrated Intelligent Building Energy Management: A Multi-LayerFramework for Renewable Energy, HVAC Optimization, and SmartElectrical Network Coordination. Available at SSRN: https://ssrn.com/abstract=6993209 or http://dx.doi.org/10.2139/ssrn.6993209
15. M. R. Marri, S. B. Kurada, S. Gupta, S. Dey, S. Kumar and R. Chauhan, "Graph-Based Deep Learning Model for Identifying Cyber Threats in Cloud Platforms," 2025 International Conference on Computational Intelligence, Security, and Artificial Intelligence (IntelliSecAI), Al-Khobar, Saudi Arabia, 2025, pp. 1-7, doi: 10.1109/IntelliSecAI66368.2025.11472831.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dr. Antoine Morel, Dr. Camille Laurent
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). Under this license:
- Share: Copy and redistribute the material in any medium or format
- Adapt: Remix, transform, and build upon the material for any purpose, including commercially
Attribution required: You must give appropriate credit, provide a link to the license, and indicate if changes were made.
License URL: https://creativecommons.org/licenses/by/4.0/
Authors retain copyright of their work while granting the journal first publication rights.
