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<p>Generative AI is changing how we traditionally use, create, and design various things using technology. In the past, AI was usually used for classification and prediction tasks. However, GenAI goes one step further by producing new content like text, images, music, and even 3D designs. This paper explains how different generative models work, including GANs, VAEs, diffusion models, and transformers. It also covers modern tools like ChatGPT and DALL<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\cdot\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>·</mo> </math></EquationSource> </InlineEquation>E that bring these models to life in everyday applications. We look at how GenAI is making a difference in fields such as healthcare, education, cybersecurity, and virtual worlds like the metaverse. At the same time, we point out major challenges, such as bias in results, privacy concerns, and the difficulty in understanding how these models make decisions. Finally, we also discuss on how Generative AI should be used responsibly in practical scenarios. Thus, by encouraging human–AI collaboration, we aim to make sure that Generative AI helps us progress while staying responsible and fair.</p>

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Collaborating with generative AI: a review of models, applications and challenges

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Kashish Verma,
  • Savita Yadav

摘要

Generative AI is changing how we traditionally use, create, and design various things using technology. In the past, AI was usually used for classification and prediction tasks. However, GenAI goes one step further by producing new content like text, images, music, and even 3D designs. This paper explains how different generative models work, including GANs, VAEs, diffusion models, and transformers. It also covers modern tools like ChatGPT and DALL \(\cdot\) · E that bring these models to life in everyday applications. We look at how GenAI is making a difference in fields such as healthcare, education, cybersecurity, and virtual worlds like the metaverse. At the same time, we point out major challenges, such as bias in results, privacy concerns, and the difficulty in understanding how these models make decisions. Finally, we also discuss on how Generative AI should be used responsibly in practical scenarios. Thus, by encouraging human–AI collaboration, we aim to make sure that Generative AI helps us progress while staying responsible and fair.