ARTIFICIAL INTELLIGENCE AS A META-DISCIPLINARY INTEGRATOR: DISSOLVING ACADEMIC BOUNDARIES FOR TRANSDISCIPLINARY PROBLEM-SOLVING

Abstract

The complex, existential challenges of the 21st century, from pandemics to climate change, are “wicked problems” that resist solutions within traditional academic silos. While multidisciplinary and transdisciplinary approaches are widely advocated as essential, their practical implementation has been hindered by communicative, methodological, and institutional barriers. This paper argues that Artificial Intelligence (AI) is a transformative, meta-disciplinary tool uniquely capable of dissolving these boundaries and operationalising higher integration in applied academia. Through a conceptual analysis and case study review, we demonstrate that AI’s core functionalities, including cross-domain data synthesis, pattern recognition, generative modelling, and literature-based discovery, enable a novel synthesis of knowledge from fields as diverse as virology, logistics, social science, materials engineering, and ethics. Case studies in pandemic response, carbon capture, and personalised medicine provide evidence of AI acting as the central platform for transdisciplinary problem-solving. However, this integration presents significant challenges, including the “black box” nature of AI, data bias, and outdated academic reward structures that favour discipline-specific work. We conclude that realising AI’s full potential requires parallel institutional reforms in curriculum, funding, and publishing. Ultimately, this paper contends that the strategic and ethical deployment of AI is not merely beneficial but imperative for creating a new, boundary-less academy capable of mobilising the entirety of human knowledge to address our most pressing global challenges.