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This study aims to understand the development trends and research structure of articles on artificial intelligence (AI) and information processing in the past 10 years. In particular, this study analyzed 13,294 papers published from 2012 to 2021 in the Web of Science, used the bibliometric analysis method to visualize the data of the papers, and drew a scientific knowledge map. By exploring the development of mainstream journals, author and country rankings, keyword evolution, and research field rankings in the past 10 years, this study uncovered key trends affecting AI progress and information processing that provide insights and serve as an important reference for future AI research and information processing. The results revealed a gradual increase in publications over the past decade, with explosive growth after 2020. The most prolific researchers in this field were Xu, Z.S.; Pedrycz, W.; Herrera-Viedma, E.; the major contributing countries were China, the USA, and Spain. In the AI and information processing research, keywords including "Deep learning", "Machine learning", and "Feature extraction" are components that play a crucial role. Additionally, the most representative research areas were "Engineering", "Operations Research and Management Science", and "Automation Control Systems". Overall, this study used bibliometric analysis to provide an overview of the latest trends in artificial intelligence and information processing. Although AI and information processing have been applied to various research areas, many other sub-topics can be further applied. Based on the findings, this study presented research insights and proposed suggestions for future research directions on AI and information processing. [ABSTRACT FROM AUTHOR]

The emerging generative artificial intelligence (AI) chatbots, such as Chat Generative Pre-Trained Transformer (ChatGPT), have recently taken different disciplines by surprise. Very few scholarly papers show the collaborative effort by researchers on the impact of generative AI in higher education (HE) and its implication on HE disciplines and activities. This paper uses citation network analysis by deep searching the different research articles using Scopus databases. A bibliographic analysis tool is used to reveal patterns of application of generative AI chatbots in HE, collaboration of generative AI research, and generative AI in HE among different countries of the world. Furthermore, it investigates the thematic topics that generative AI in HE researchers are studying. Research papers from Scopus databases were sampled. The sampling timeframe is from January 2022 to September 2023. This paper presents the preliminary findings of the work that was conducted for a doctoral thesis in computer science and information technology. The preliminary findings show that Spain, the United States of America (USA), and Australia are countries that collaborate more often on the development of generative AI chatbots. In the study, generative AI chatbots were most likely to cover plagiarism and essay inclusion, but very few covered research for postgraduate enterprises. The study concludes that Chatbots are an important part of teaching technology that should be embraced by higher education. [ABSTRACT FROM AUTHOR]