Universities are being asked to produce more research, of higher integrity and with wider societal reach, while generative artificial intelligence, open science expectations, and reformed evaluation systems change how that work is done and judged. This narrative review synthesises recent literature (2023–2026 preferred) on five trends shaping academic research and development in higher education: the spread of generative AI through research workflows, open science and study registration, responsible research assessment, interdisciplinary research, and university–industry collaboration. Evidence was drawn from systematic reviews, large bibliometric and survey studies, and foundational policy agreements. The review finds that adoption is moving faster than evidence about effects. AI use among researchers has risen sharply while guidance remains thin; open science and registration are plausible remedies for research waste but face disciplinary and incentive barriers; assessment reform is widely endorsed yet left to institutional interpretation; and interdisciplinary researchers still meet career penalties. Organised publication fraud adds urgency to each debate. The paper argues that institutions should align incentives with the behaviours they request, invest in training and infrastructure, and evaluate the reforms themselves.