The three-level model of learning provides a useful entry point for understanding artificial intelligence and its potential impact on human activities. When AI entered social practices at the level of operations, it augmented and complemented activities, thus increasing the efficiency and effectiveness of the way things were done. Likewise, at the level of acts, AI has replaced, substituted, and automated acts that were previously done by humans. Thus, the integration of AI into the realm of research has ushered in a transformative era of data-driven discovery and innovation. AI in research not only spans diverse fields, from enhancing data analysis to aiding decision-making processes, but more importantly, it has become a dynamic field with significant potential and challenges. The purpose of this chapter is to explore its challenges and future directions.
Top1. Introduction
In the ever-evolving landscape of research, Artificial Intelligence (AI) has emerged as a potent and disruptive force, reshaping the methodologies and outcomes across diverse fields (Van Roy, et al., 2021). The rapid advancements in AI technologies have had a profound impact on research in all areas of human society including the economy, politics, science, and education (Flasiński, 2016). Furthermore, with the potential to automate many tasks that currently require human intervention, AI has attracted considerable interest in research from a variety of fields. However, harnessing this technology effectively, ethically, and equitably remains a challenge (Zlateva, et al., 2024). With the rapid integration of AI into various aspects of research, its infiltration into mainstream education seems imminent (Topol, 2019; Civaner, et al., 2022). This intersection has sparked intense discussions and conjectures about the future of AI in research and education, revolving around its potential uses and limitations. As a result, the integration of such a transformative technology into existing educational practices demands an informed, considerate approach. It necessitates not only an understanding of the capabilities and limitations of AI but also a forward-thinking blueprint for researchers and educators (Savage, 2022). This chapter aims to offer a comprehensive overview of the potential opportunities and challenges that AI presents for research and general education.
The current excitement about AI in research has continued to lead to a technology push, where AI is viewed as a solution to a wide variety of problems. It is probably fair to say that the potential and challenges of AI in research are still not adequately understood. AI can be understood as a general-purpose technology, and it can be applied in research in many ways, such as: