This chapter explores the transformative impact of artificial intelligence (AI) on various research domains. The chapter highlights the leading role of AI research platforms in the development of AI in research practices. In addition, the chapter describes the fundamental concepts of AI powered tools such as generative artificial intelligence and large language models. The narrative extends to AI integration in modern research practices, spotlighting its use in hypothesis formulation, generation of research questions, and data analysis. AI's contributions to data analysis in many fields are explored, particularly in automating processes and generating insights. The discussion further explores several AI applications in various research domains such as RPA, Semantic Scholar, and DeepChem. However, potential bias and transparency are main challenges of AI-powered applications. Therefore, it is imperative to champion explainable AI and human-in-the-loop approaches to improve confidence. The chapter recommends further interdisciplinary collaboration between researchers and AI developers.
TopIntroduction And Background
Artificial Intelligence (AI) is a transformative technology in various domains such as education, healthcare, and society. There is a significant progress has been noticed in development of AI in relation to its methods and AI powered tools (Nalepa & Stefanowski, 2020). Recently, AI research trend increased and provided insights into the impact of AI on various domains including economy and society from different perspectives which generated a solution for many challenges faced by governmental organizations, and companies (Han et al., 2020).
AI Applications are dominant in a multitude of fields, changing and revolutionizing the way we do things (Wirtz et al., 2018). In the medical field, AI is being leveraged for applications such as diagnosis, surgery, and combating diseases like COVID-19 (Shahnaz & Mukesh, 2022). AI competitions and platforms like Kaggle and Grand Challenge are driving AI-based research in medical imaging, highlighting its competitive and collaborative nature (Luitse et al., 2023). In addition, AI plays a significant role in supply chain management, social computing, and cloud computing, indicating its pervasive impact among diverse areas (Žigienė et al., 2020; Wang, 2021; Zheng & Wen, 2021). Furthermore, the deployment of AI is becoming crucial in finance and retail sector, which dominated by its broad spectrum of functionality (Wirtz et al., 2018; Rana et al., 2021), mainly with its ability to improve the Human-Computer Interaction (HCI) and user experience (Yang et al., 2020).
AI plays a prominent role in transforming teaching and learning by personalized and adaptive learning. Moreover, AI tools pioneering in its ability to structure the scientific literature review as well as an emerging technology for creating an adaptive computational system (Hinojo-Lucena et al., 2019). Machine learning algorithms which is part of AI have been identified as powerful tools for developing battery research cycle (Wu et al., 2023).
However, several challenges raised up while Implementing AI in research practices which need to be addressed for successful integration. These challenges highlighted and reported by several authors such as Wirtz et al., 2018; Gama et al., 2022; Lamberti et al., 2019; Petersson et al., 2022; Zhou et al., 2020; Borkowski, 2022; Bérubé et al., 2021; Nilsen et al., 2022; Ji et al., 2020; Huang, 2019. AI safety, system/data quality and integration, financial feasibility, specialization and expertise, staff skills, data structure, budgets, patient-centeredness, usability, ethical principles, material descriptors, data-related challenges, data quality, data sharing mechanisms, integrated AI systems, and patient acceptance are challenges identified recently which indicating the necessity of considering it while moving forward to AI Integration in research practices. Additionally, issues such as uncertainty and data dependency in AI are considered one of the unique challenges in AI implementation (Weber et al., 2022).
This chapter aim to provide a comprehensive discussion in AI deployment in research. Starting with understating the AI’s system and its fundamental concepts. Then, moves into understanding AI in modern research practices, and delve into some AI’s applications in various research areas. Then, the chapter address the challenges associated with applying AI in research, as well as highlighting the ethical consideration in using AI powered tool in research. Finally, the chapter concluded by anticipating the future trend of AI in research. Collectively, the chapter provides a comprehensive and updated impact of AI utilization in various fields of scientific research.