Generative Artificial Intelligence


  • Carlos Rios-Campos
  • Jessica Del Consuelo Luzuriaga Viteri
  • Elixer Alexandra Palma Batalla
  • Juan Francisco Castro Castro
  • Jorge Bautista Núñez
  • Edilbrando Vega Calderón
  • Francisco Javier Gómez Nicacio
  • Melissa Yaneth Pretell Tello



generative, artificial intelligence


The general objective of the research is to determine the advances related to Generative Artificial Intelligence. Methodology, in this research, 47 documents have been selected, carried out in the period 2014 - 2023; including: scientific articles, review articles and information from websites of recognized organizations. Results, Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. Conclusions, the general objective of the research is to determine the advances related to Generative Artificial Intelligence. Artificial intelligence has evolved from predictive to generative. Key Techniques: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive Models. Countries are establishing standards for the ethical use of AI, while respecting human rights. Currently, AI has many applications in human activity, but the ethical use of AI is necessary. Various countries are establishing regulations in this regard. Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. The specific objectives of the research are to identify the applications and the software of Generative Artificial Intelligence. Applications: Generating realistic images, creating natural language text, composing music. Generative artificial intelligence (AI) tools, such as Bard, ChatGPT, and GitHub CoPilot.


Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.

Australian Parliament House (2023). Inquiry into the use of generative artificial intelligence in the Australian education system. Retrieved from

Baidoo-Anu, David and Owusu Ansah, Leticia (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Available at SSRN: or

Barrero, A. & Rosero, A. (2018). Estado del Arte sobre Concepciones de la Diversidad en el Contexto Escolar Infantil. Revista Latinoamericana de Educación Inclusiva, 2018, 12(1), 39-55

Bell, G., Burgess, J., Thomas, J., and Sadiq, S. (2023). Rapid Response Information Report: Generative AI - language models (LLMs) and multimodal foundation models (MFMs). Australian Council of Learned Academies

Bengio, Y., Courville, A., & Vincent, P. (2019). Representation learning: A review and new perspectives. Journal of Machine Learning Research, 12(2019), 1-53.

Brock, A., Donahue, J., & Simonyan, K. (2019). Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096.

Browne, R. (2023). EU lawmakers pass landmark artificial intelligence regulation. Retrieved from

Chiara Longoni, Andrey Fradkin, Luca Cian, and Gordon Pennycook (2022). News from Generative Artificial Intelligence Is Believed Less. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). Association for Computing Machinery, New York, NY, USA, 97–106.

Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Ethics Inf Technol 20, 1–3.

Dohmke, T. (2023). GitHub Copilot X: The AI-powered developer experience. Retrieved from

Dong, H., Hsiao, W., Yang, L., & Yang, Y. (2018). MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).

Dubow, B. (2023). Russia’s New Underpowered Weapon – Artificial Intelligence. Retrieved from

Dwivedi, Y.K., Pandey, N., Currie, W. and 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, Vol. ahead-of-print No. ahead-of-print.

Ebert, C. and Louridas, P. (2023). Generative AI for Software Practitioners, in IEEE Software, vol. 40, no. 4, pp. 30-38. doi: 10.1109/MS.2023.3265877

European Parliament (2023). EU AI Act: first regulation on artificial intelligence. Retrieved from

Eysenbach, G. (2023). The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers. JMIR Med Educ 2023;9:e46885. DOI: 10.2196/46885

Feingold, S. (2023). What is artificial intelligence—and what is it not?.

Gartner (2023). What is generative AI?. Retrieved from

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (pp. 2672-2680).

Gragnaniello, D., Marra, F. & Verdoliva, L. (2022). Detection of AI-Generated Synthetic Faces. In: Rathgeb, C., Tolosana, R., Vera-Rodriguez, R., Busch, C. (eds) Handbook of Digital Face Manipulation and Detection. Advances in Computer Vision and Pattern Recognition. Springer, Cham.

Haase, J., & Hanel, P.H. (2023). Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity. ArXiv, abs/2303.12003

Huang, X., Liu, H., Ma, S., & Lee, G. (2018). Multimodal unsupervised image-to-image translation. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 179-196).

Hughes RT, Zhu L and Bednarz T (2021). Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends. Front. Artif. Intell. 4:604234. doi: 10.3389/frai.2021.604234

IBM (2023). What is artificial intelligence (AI)?. Retrieved from

Jo, E., & Gebru, T. (2021). Lessons from archives: Strategies for collecting sociocultural data in machine learning. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT).

Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4401-4410).

Kingma, D. P., & Welling, M. (2013). Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114.

Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K. and Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: analysis of ChatGPT. Central European Management Journal, Vol. 31 No. 1, pp. 3-13.

Mondal, S., Das, S., & Vrana, V. G. (2023). How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life. Technologies, 11(2), 44. MDPI AG. Retrieved from

Murugesan, S. and Cherukuri, A. K. (2023). The Rise of Generative Artificial Intelligence and Its Impact on Education: The Promises and Perils. in Computer, vol. 56, no. 5, pp. 116-121. doi: 10.1109/MC.2023.3253292

Noy, S. & Zhang, W. (2023). Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. Available at SSRN: or

NVIDIA Corporation (2023). What is Generative AI?. Retrieved from

Odena, A., Olah, C., & Shlens, J. (2020). Conditional image synthesis with auxiliary classifier GANs.

Pan, C. (2023). China sets out new rules for generative AI, with Beijing emphasising healthy content and adherence to ‘socialist values’. Retrieved from

Pavlik, J. V. (2023). Collaborating With ChatGPT: Considering the Implications of Generative Artificial Intelligence for Journalism and Media Education. Journalism & Mass Communication Educator, 78(1), 84–93.

Pichai, S. (2023). An important next step on our AI journey. Retrieved from

Prasad, K. (2023). Achieving a sustainable future for AI. Retrieved from

Queensland Brain Institute (2023). History of Artificial Intelligence. Retrieved from

Reuters (2023). US to launch working group on generative AI, address its risks. Retrieved from

Svendsen, A. and Garvey, B. (2023). Prompt-engineering testing ChatGPT4 and Bard for assessing Generative-AI efficacy to support decision-making. Available at SSRN: or

Sylvan, E. & Guio, A. (2023). Generative AI: What should governments in Latin America do?. Retrieved from

The Japan Times (2023). NEC develops Japanese-language generative AI. Retrieved from

UNESCO (2023). Artificial Intelligence. Retrieved from

Yilmaz, B. & Korn, R. (2022). Synthetic demand data generation for individual electricity consumers: Generative Adversarial Networks (GANs). Energy and AI, Volume 9. 100161. ISSN 2666-5468.

Yue Liu, Zhengwei Yang, Zhenyao Yu, Zitu Liu, Dahui Liu, Hailong Lin, Mingqing Li, Shuchang Ma, Maxim Avdeev, Siqi Shi (2023). Generative artificial intelligence and its applications in materials science: Current situation and future perspectives, Journal of Materiomics. ISSN 2352-8478.

Zhihan, Lv (2023). Generative artificial intelligence in the metaverse era. Cognitive Robotics, Volume 3, Pages 208-217. ISSN 2667-2413.




How to Cite

Rios-Campos, C., Viteri, J. D. C. L., Batalla, E. A. P., Castro, J. F. C., Núñez, J. B., Calderón, E. V., Nicacio, F. J. G., & Tello, M. Y. P. (2023). Generative Artificial Intelligence. South Florida Journal of Development, 4(6), 2305–2320.

Most read articles by the same author(s)

1 2 3 > >>