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<p>Generative Artificial Intelligence (GenAI) has revolutionized multiple industries by improving efficiency and promoting innovation in fields such as business, education, healthcare, and cybersecurity. This study evaluates the transformative impact of GenAI on advancing the United Nations' Sustainable Development Goals (SDGs) by analyzing research trends and applications. In this research, authors utilized Latent Dirichlet Allocation (LDA), a Natural Language Processing (NLP) technique. The work delineates emergent themes and sector-specific progressions in GenAI research. The dataset consists of 2162 research articles published from 2020 to 2025, obtained from the Scopus database. The abstracts of these publications provide the foundation for analysis, facilitating a thorough examination of literature. The authors provided 10 topics, which are recent trends that future researchers can explore. The results indicate GenAI's capability in processing automation, creative enhancement, and innovation promotion while addressing ethical concerns such as prejudice, privacy, and social effects. The study indicates potential directions for ethical technology adoption by analyzing trends in GenAI applications. This work highlights the essential requirement for interdisciplinary methods and ethical frameworks to optimize GenAI's contributions to innovation while assuring alignment with sustainable and equitable development goals.</p>

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Generative artificial intelligence for sustainable development: predictive trend analysis in key sectors using natural language processing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月4日

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  • Chetan Sharma,
  • Shamneesh Sharma,
  • Vivek Bhardwaj,
  • Balwinder Kaur Dhaliwal

摘要

Generative Artificial Intelligence (GenAI) has revolutionized multiple industries by improving efficiency and promoting innovation in fields such as business, education, healthcare, and cybersecurity. This study evaluates the transformative impact of GenAI on advancing the United Nations' Sustainable Development Goals (SDGs) by analyzing research trends and applications. In this research, authors utilized Latent Dirichlet Allocation (LDA), a Natural Language Processing (NLP) technique. The work delineates emergent themes and sector-specific progressions in GenAI research. The dataset consists of 2162 research articles published from 2020 to 2025, obtained from the Scopus database. The abstracts of these publications provide the foundation for analysis, facilitating a thorough examination of literature. The authors provided 10 topics, which are recent trends that future researchers can explore. The results indicate GenAI's capability in processing automation, creative enhancement, and innovation promotion while addressing ethical concerns such as prejudice, privacy, and social effects. The study indicates potential directions for ethical technology adoption by analyzing trends in GenAI applications. This work highlights the essential requirement for interdisciplinary methods and ethical frameworks to optimize GenAI's contributions to innovation while assuring alignment with sustainable and equitable development goals.