Challenges of Integrating Artificial Intelligence as an Interdisciplinary Science into Traditional Classification Structures

Purpose: Artificial intelligence (AI), with its remarkable ability to process vast amounts of data and solve highly complex problems, has emerged as a transformative interdisciplinary science in the modern era. Its applications extend across a wide range of fields, including computer science, philosophy, psychology, and social sciences. These applications have significantly contributed to expanding the frontiers of knowledge and fostering innovation. However, traditional scientific classification systems, which were designed based on rigid and distinct disciplinary boundaries, often struggle to accommodate AI effectively. This failure stems from their inability to integrate AI’s interdisciplinary nature seamlessly. As a result, AI faces numerous challenges when it comes to its incorporation into established academic frameworks. This study aims to explore the barriers that hinder this integration and to propose actionable solutions for overcoming these obstacles. Specifically, it focuses on the conceptual, scientific, organizational, and technical challenges that restrict AI’s effective adoption within academic and research structures.

Method: This study employs an analytical review method, which is a qualitative approach for systematically examining scientific sources. The information sources were extracted from reputable databases. The reviewed articles span the period from 2010 to 2024, as this timeframe encompasses significant advancements in the field of artificial intelligence and the emergence of interdisciplinary challenges. Out of an initial pool of 200 sources, 40 were selected based on criteria such as scientific credibility, thematic relevance, and a focus on interdisciplinarity. To analyze the findings, the selected sources were qualitatively examined, and the identified challenges were categorized into four domains: conceptual, scientific, organizational, and technical. These challenges were then analyzed based on common patterns across various studies, and corrective recommendations were formulated based on previous research findings. This analysis helped clarify the relationship between existing challenges and the structural barriers to integrating artificial intelligence as an interdisciplinary field.

Findings: The study identifies several key challenges to integrating AI into traditional scientific systems. On the conceptual level, disparities in definitions and the absence of a common language between disciplines act as significant barriers to interdisciplinary collaboration. For example, in computer science, AI is largely defined through its algorithms and machine learning systems. In contrast, the humanities approach AI from the perspective of human-machine cognitive and social interactions. These differences complicate effective collaboration and mutual understanding between fields. Scientifically, AI’s methods and theoretical frameworks often fail to align with traditional paradigms in the humanities and social sciences, making integration challenging. Organizationally, universities and research institutions demonstrate resistance to adopting structural and cultural reforms, as they are rooted in rigid departmental systems. Lastly, technical issues such as inadequate hardware, fragmented data systems, and high processing costs pose additional obstacles to implementing AI within academic projects.

Conclusion: Overcoming these challenges requires significant changes across multiple levels. Developing unified conceptual standards, reforming educational and research structures to promote interdisciplinary thinking, and investing in advanced technical infrastructure are critical steps. Moreover, global collaboration among researchers, policymakers, and institutions is essential to create standardized frameworks and support interdisciplinary projects. By addressing these barriers, the integration of AI into traditional scientific structures can be accelerated, fostering innovation and advancing interdisciplinary research.