{"id":57175,"date":"2025-12-17T16:28:55","date_gmt":"2025-12-17T10:28:55","guid":{"rendered":"https:\/\/www.enago.com\/academy\/?p=57175"},"modified":"2026-05-07T12:17:31","modified_gmt":"2026-05-07T12:17:31","slug":"why-human-experts-still-outperform-ai-in-proofreading","status":"publish","type":"post","link":"https:\/\/www.enago.com\/articles\/why-human-experts-still-outperform-ai-in-proofreading\/","title":{"rendered":"Why human experts still outperform AI in proofreading subject-specific language for research publications"},"content":{"rendered":"

Researchers increasingly turn to generative AI for writing support, and large language models (LLMs) now produce fluent summaries, edits, and suggestions in seconds. Yet recent evaluations show significant risks when these tools handle subject-specific language<\/em>\u00a0that is, terminology, methodological detail, and nuanced claims tied to a particular discipline. A large-scale study<\/a> found that LLMs were nearly five times more likely than humans to overgeneralize scientific conclusions when summarizing research, raising clear concerns about relying on AI alone for proofreading and technical language checks.<\/p>\n

This article explains what makes subject-specific proofreading different from general copyediting, why humans still outperform AI for technical and disciplinary language, and how researchers can combine AI speed with human expertise to produce rigorous, publication-ready manuscripts. Practical tips and service options are provided to help authors choose the right workflow for their submission goals.<\/p>\n

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