Artificial intelligence in interdisciplinary higher education: A systematic review on opportunities, challenges and future directions

Abstract

As artificial intelligence (AI) becomes increasingly embedded in higher education, it is simultaneously transforming how interdisciplinary education is conceived, delivered and evaluated. This study presents a systematic review of 59 recent studies to investigate the evolving relationship between AI and interdisciplinary education in higher education. Guided by four research questions, we examined (a) the forms of interdisciplinary education emerging in the AI context; (b) the main research focuses within this intersection; (c) the functional roles AI plays in interdisciplinary settings; and (d) the impact of AI on different educational stakeholders. Findings indicate that AI-supported interdisciplinary education includes the integration of science, technology, engineering and mathematics (STEM) and science, technology, engineering, art and mathematics (STEAM); non-STEM applications; cross-disciplinary curricula; and diverse student backgrounds. The literature search identified three primary research focuses: AI system and tool development, AI-empowered curriculum transformation and AI-based learning analysis and evaluation. Functionally, AI acts as an evaluator, agent, monitor and assistant, reshaping how knowledge is constructed and assessed across disciplines. Finally, this study developed an interdisciplinary human-AI interactive learning model, providing a conceptual reference framework for AI-supported interdisciplinary teaching and outlining five directions for future research. This review offers critical insights for researchers, educators and policymakers seeking to navigate the challenges and opportunities at the intersection of AI and interdisciplinary higher education.

Implications for practice or policy:

• Course instructors can embed AI-supported activities that promote interdisciplinary problem-solving.
• Institutions should establish policies on AI ethics, bias, transparency and student data privacy.
• Educators need sustained training to build practical AI literacy.
• Policymakers should ensure equitable access to AI tools for all learners.
• Programmes should align AI use with sustainability and global citizenship goals.