Artificial Intelligence in Research and Academic Publication

Abstract

The rapid adoption of large language models is accelerating scientific output faster than expert attention can scale, placing growing pressure on the scholarly communication system. This perspective examines how artificial intelligence (AI) is reshaping research and scholarly communication, from knowledge discovery to scientific evaluation and production. Evidence to date reveals a central tension: AI can make scholarly work more efficient and evidence-aware, yet its limitations can also undermine trust in the resulting scholarship. We argue that the strongest near-term model is human–AI collaboration, in which AI assists with analysis while humans remain accountable for scholarly judgment. More fundamentally, AI offers an opportunity to reorganize scholarly exchange around transparent research processes and ongoing evaluation. This shift could broaden participation and allow contributions to be judged by traceable evidence rather than venue prestige or polished presentation. With governance that ensures transparency and preserves human responsibility, AI can become essential infrastructure for a more scalable and evidence-centered scientific ecosystem.