Multidisciplinary Higher Education Teaching and Learning

This open dataset provides research materials for analysing multidisciplinary higher education teaching and learning between 2015 and 2025. It supports the revised manuscript "Teaching Beyond Boundaries: Reflections on AI, Multidisciplinarity, and Institutional Change in Higher Education" and also preserves broader background files from earlier phases of the research project.

The dataset contains three core analytical layers. Dataset S1 is a pre-COVID baseline corpus of coded publications from 2015-2020. Dataset S2 is a post-2020 comparison corpus covering 2020-2025. Dataset S3 is a focused AI-entry corpus examining how artificial intelligence enters the multidisciplinary higher education problem space through student support and engagement, curriculum and boundary opening, integrity and governance, and assessment and feedback capacity.

The dataset also includes search documentation, coding files, calculation workbooks, a codebook, an index file and figure outputs. These materials support transparency, reuse and reproducibility. Additional background files are retained as part of the wider research record and may support future analyses on multidisciplinary higher education, post-COVID teaching and learning, digital transformation, curriculum development, student engagement and AI in higher education.

The dataset analyses scholarly discourse and research problem-framing. It should be used to inspect how the literature frames, weights and connects barriers to multidisciplinary higher education, rather than as a direct measure of institutional transformation.