{"id":36817,"date":"2025-07-07T17:39:28","date_gmt":"2025-07-07T12:09:28","guid":{"rendered":"https:\/\/www.editage.com\/insights\/?p=36817"},"modified":"2025-12-18T11:07:36","modified_gmt":"2025-12-18T05:37:36","slug":"ai-accessibility-and-peer-review-why-plain-language-isnt-always-plain-truth","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/ai-accessibility-and-peer-review-why-plain-language-isnt-always-plain-truth","title":{"rendered":"AI, accessibility, and peer review: Why \u2018plain language\u2019 isn\u2019t always plain truth"},"content":{"rendered":"

This post is based on my research available here: https:\/\/zenodo.org\/records\/15744438<\/a>.<\/em><\/p>\n

Peer reviewer comments are a crucial part of academic publishing\u2014but for many researchers, especially those with cognitive processing disabilities, they can feel more like encrypted puzzles than constructive guidance. With the rise of large language models (LLMs) like GPT-4, there\u2019s growing interest in using AI to simplify this dense feedback. But how helpful is this assistance really?<\/p>\n

In a recent experiment, I tested GPT-4\u2019s ability to simplify peer review comments from academic finance, a field known for its technical rigor and precision. Each comment was rewritten by the model using two prompts\u2014one a general plain-language request, and the other designed with cognitive accessibility in mind (e.g., for users with dyslexia or working memory challenges). My goal was to see whether these AI-generated simplifications could genuinely support inclusion while retaining the essence of what the reviewer was trying to communicate.
\nOn the surface, GPT-4 performed well. It shortened long sentences, smoothed out jargon, and offered gentler phrasing. But dig deeper, and a different story emerges.<\/p>\n

One reviewer had suggested using a difference-in-differences method to improve causal inference in a study of market reactions. GPT-4\u2019s simplified version talked vaguely about \u201chow quickly and how strongly the market reacts.\u201d The method\u2019s role in establishing causality was lost. For a researcher unfamiliar with econometric tools, this could completely distort the reviewer\u2019s intent.<\/p>\n

In another example, the term \u201cendogeneity\u201d was reduced to \u201chidden effects.\u201d While this might seem like a helpful analogy, it misses the real statistical issue\u2014correlation between variables and error terms, often requiring complex modeling adjustments. Here, the simplification didn\u2019t clarify\u2014it misled.<\/p>\n

Perhaps most worryingly, the model often swapped precise technical framing with casual or even incorrect alternatives. A critique about using \u201cstandard methods\u201d was rewritten to imply that the methods themselves caused a lack of novelty. This altered the tone from nuanced to dismissive. Another simplification described \u201cbounded rationality\u201d as \u201climited thinking ability\u201d\u2014a harmful misrepresentation that frames a central theory in behavioral economics as a cognitive defect.<\/p>\n

What\u2019s striking is that even when given a disability-aware prompt, GPT-4 did not consistently preserve meaning. Sometimes the tone improved\u2014more respectful, more accessible\u2014but the technical content still suffered. The simplifications were also inconsistent across runs: the same comment, simplified twice using the same prompt, often yielded different interpretations.<\/p>\n

For researchers navigating peer review, especially those who process information differently, this inconsistency is more than a nuisance\u2014it\u2019s a risk. Misinterpreting feedback can delay publication, derail revisions, or undermine confidence. And for those relying on AI as an assistive tool, trust in the tool\u2019s fidelity is paramount.<\/p>\n

So, what should we take away from this? First, AI can help\u2014but it cannot yet be trusted to simplify peer review comments in technical domains without oversight. Second, editors, peer reviewers, and support platforms like Editage must be mindful of how feedback is phrased. Plain language is a valuable goal, but clarity must be paired with conceptual accuracy. Finally, the future of inclusive publishing requires us to rethink not just how<\/em> we simplify, but for whom<\/em>\u2014and with what guardrails.<\/p>\n

As LLMs continue to evolve, we must ask tough questions about how they\u2019re deployed in scholarly communication. Inclusion is not just about ease of reading. It\u2019s about enabling full participation in the research dialogue. And that means making sure our tools don\u2019t just sound simple\u2014they must speak the truth, too.<\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

This post is based on my research available here: https:\/\/zenodo.org\/records\/15744438. Peer reviewer comments are a crucial part of academic publishing\u2014but for many researchers, especially those with cognitive processing disabilities, they can feel more like encrypted puzzles than constructive guidance. With the rise of large language models (LLMs) like GPT-4, there\u2019s growing interest in using AI […]<\/p>\n","protected":false},"author":225,"featured_media":36357,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2934,2429],"tags":[2743,1078],"new_categories":[],"new_tags":[5763,5830],"series":[],"class_list":["post-36817","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-digital-transformation","category-responding-to-peer-reviewers","tag-ai-for-researchers","tag-peer","new_tags-ai-for-researchers","new_tags-peer-review"],"acf":[],"yoast_head":"\nAI, Accessibility, and Peer Review | Editage Insights<\/title>\n<meta name=\"description\" content=\"With the rise of LLMs, there\u2019s growing interest in using AI to simplify peer review feedback. 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