Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research

Computer Science > Artificial Intelligence [Submitted on 2 Jun 2026] Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research Clarisse de Souza, Gabriel Barbosa, Simone Diniz Junqueira Barbosa, Bárbara Betts, Renato Cerqueira, Juliana Jansen Ferreira Large language models are reshaping research practice while quietly eroding researchers epistemic accountability. This commentary introduces PEEL - Protocols for Epistemically Engaged Literacy in AI, a working scaffolding that combines deterministic distant reading via Voyant Tools with LLM interpretation via Claude, grounded in Peircean semiotics and abductive reasoning. Applied to AI-generated condensations of three source texts, PEEL reveals systematic distortions in quantity, term frequency, and epistemic voice that are invisible without non-AI measurement -- and yields three design implications: deterministic instruments must accompany AI tools; fluency is not fidelity; epistemic authority must be designed in, not assumed. Comments: 10 pages, 5 figuras Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY) ACM classes: H.3.3; I.2.0 Cite as: arXiv:2606.04152 [cs.AI]   (or arXiv:2606.04152v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.04152 Focus to learn more Submission history From: Juliana Ferreira J [view email] [v1] Tue, 2 Jun 2026 19:19:52 UTC (1,821 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)