The aim is to showcase the remarkable achievements of AI-integrated research, illustrating the significant impact of AI has had across various industries and academia. The approach involves gathering case studies and conducting comprehensive analyses, with a specific emphasis on AI-enhanced research initiatives. The findings underscore the value of AI-based methods and tools in guiding research and industry-related tasks. Through the examination of AI-related case studies, academics and business professionals have been able to enhance productivity and organizational performance by effectively implementing AI. The originality of this study lies in its thorough examination of AI's role in research and industries, featuring numerous case examples from diverse disciplines. The integration of AI in industries has become the foundation for case studies and related content/cases used in academia to educate students. This comprehensive approach offers a fresh perspective on the innovation and significance of AI in research and practical applications. By presenting various scenarios, the chapter will explore the societal and practical implications of successful AI-integrated endeavors, emphasizing AI's ability to transform sectors, improve decision-making, and address complex challenges.
TopIntroduction
Artificial intelligence (AI) research is expanding rapidly, bringing with it revolutionary changes (Adigwe et al. 2024; Yang et al., 2024). AI technologies are highly beneficial in exploring and gaining unprecedented insights into a variety of fields (Crompton & Burke, 2023; Hunt, 2014). One of the most intriguing aspects of AI research is the difficulty in precisely defining the technology. Undoubtedly, the “artificial” nature of AI arises from the way it is created—through human ingenuity and imagination rather than natural (especially biological or evolutionary) processes. Consequently, AI possesses a certain attribute (intelligence) as a result of a specific process (being created, designed, or generated in this manner). The field of study known as artificial intelligence (AI) investigates how to endow computers with the complexity to function intelligently across an expanding array of fields; it is distinct from psychology or sociology. Embracing the entirety of computer science's interests, including programming, logical formalisms, abstraction, and detail, AI prioritizes algorithms over behavioral data, synthesis over analysis, and engineering (doing) over science (knowing) (Kabudi et al., 2021).
The phrase “field of science and engineering concerned with the computational aspects of AI” refers to the comprehension of creating objects that exhibit intelligent behavior, as well as the broader concept of intelligent behavior (Hunt, 2014; Kabudi et al., 2021). Much of the work conducted in the modern era has been inspired by early studies on the mechanisms of the mind, laying the foundation for the evolution of modern logical reasoning (Boden, 2008). Since 1950, when Alan Turing first envisioned the fascinating notion of “thinking machines,” research on AI has proliferated across numerous fields, generating a growing body of prior work (e.g., Kabudi et al., 2021; Kurzweil, 1985; Simmons & Chappell, 1988). Emerging technologies have also transformed the way teaching and learning are conducted in the educational system. With the advancement of AI technology, its educational applications are also expanding (Crompton & Burke, 2023). Offering personalized instruction, dynamic assessments, and meaningful engagement in online, mobile, or hybrid learning settings presents intriguing new opportunities. For instance, in response to teacher shortage, few countries have proposed to employing AI to replace certain roles with robots (Zhang & Aslan, 2021). Despite the expansion of AI in education, AIEd (artificial intelligence in education) innovations are still in the early stages of exploration and discussion. Additionally, there is limited collaboration with educational institutions on relevant interventions, such as AI-enabled adaptive systems (Kabudi et al., 2021).