Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
For researchers using AI tools, this work highlights the need for epistemic accountability and deterministic instruments to accompany AI, though it is a conceptual commentary rather than an empirical study.
The paper introduces PEEL, a scaffolding combining deterministic distant reading with LLM interpretation, and demonstrates that it reveals systematic distortions in AI-generated text condensations that are invisible without non-AI measurement.
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.