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PreprintCC BY 4.0Zenodo

Generative AI Survey: Architectures, Applications, Challenges, and Future Directions

  • Raksha Mali1
  1. 1R.C.Patel college of Engineering and Polytechnic
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Abstract

This survey paper provides an overview of Generative Artificial Intelligence (GenAI), including its fundamental concepts, major architectures, applications, challenges, and future research directions. It discusses important generative approaches such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), transformer-based models, and diffusion models.

The paper explores applications of GenAI in healthcare, education, finance, software development, scientific research, and cybersecurity. It also examines challenges related to data privacy, bias, hallucinations, interpretability, copyright, computational costs, and responsible AI governance.

Finally, the survey highlights emerging research directions in multimodal AI, model efficiency, human-AI collaboration, and AI safety. It aims to provide students, researchers, and practitioners with a structured understanding of Generative AI and its opportunities for future research.

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Cite

BibTeXRIS
BibTeX
@misc{mali2026generative,
  title = {{Generative AI Survey: Architectures, Applications, Challenges, and Future Directions}},
  author = {Raksha Mali},
  howpublished = {Zenodo},
  year = {2026},
  month = oct,
  doi = {10.5281/zenodo.23526831},
  url = {https://zenodo.org/records/23526831},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Generative AI Survey: Architectures, Applications, Challenges, and Future Directions
AU  - Raksha Mali
PY  - 2026
DA  - 2026/10/21
PB  - Zenodo
DO  - 10.5281/zenodo.23526831
UR  - https://zenodo.org/records/23526831
AB  - This survey paper provides an overview of Generative Artificial Intelligence (GenAI), including its fundamental concepts, major architectures, applications, challenges, and future research directions. It discusses important generative approaches such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), transformer-based models, and diffusion models.  The paper explores applications of GenAI in healthcare, education, finance, software development, scientific research, and cybersecurity. It also examines challenges related to data privacy, bias, hallucinations, interpretability, copyright, computational costs, and responsible AI governance.  Finally, the survey highlights emerging research directions in multimodal AI, model efficiency, human-AI collaboration, and AI safety. It aims to provide students, researchers, and practitioners with a structured understanding of Generative AI and its opportunities for future research.
ER  -