{"id":56914,"date":"2025-11-21T20:09:08","date_gmt":"2025-11-21T14:09:08","guid":{"rendered":"https:\/\/www.enago.com\/academy\/?p=56914"},"modified":"2026-05-08T09:15:18","modified_gmt":"2026-05-08T09:15:18","slug":"ai-hallucinations-research-citations","status":"publish","type":"post","link":"https:\/\/www.enago.com\/articles\/ai-hallucinations-research-citations\/","title":{"rendered":"Understanding Citation Ethics: Why You Should Never Rely Solely on AI for Literature Discovery"},"content":{"rendered":"

Recent evaluations of generative AI show a worrying pattern: many AI systems produce plausible-looking but incorrect or entirely fabricated bibliographic references. In one multi-model study<\/a> of academic bibliographic retrieval, only 26.5% of generated references were entirely correct, while nearly 40% were erroneous or fabricated.<\/p>\n

For researchers, students, and institutional authors, this matters because literature discovery and accurate citation underpin reproducibility, peer review<\/a>, and scholarly trust. This article explains what goes wrong when you rely solely on AI for literature discovery, why those failures occur, and most importantly practical, implementable workflows and checks you can use to preserve research integrity.<\/p>\n

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