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

Researchers have developed PEEL (Protocols for Epistemically Engaged Literacy in AI), a framework combining deterministic distant reading tools with LLM interpretation to measure and expose systematic distortions in AI-generated text summaries. The framework reveals that large language models introduce undetectable errors in quantity, term frequency, and epistemic voice, challenging the assumption that AI fluency equals fidelity and raising critical questions about researcher accountability in AI-assisted scholarship.

Large language models have rapidly integrated into research workflows, creating an accountability gap where AI-generated outputs appear polished and authoritative while obscuring systematic biases and distortions. This commentary addresses a fundamental problem: researchers cannot reliably detect when LLMs misrepresent source material without external measurement frameworks. PEEL combines Voyant Tools—a deterministic distant reading instrument—with Claude interpretation, grounded in Peircean semiotics to create verifiable benchmarks against which AI outputs can be evaluated.

The research emerges from a broader epistemological crisis in AI-assisted scholarship. As language models become increasingly fluent and persuasive, institutions and individual researchers have adopted them without establishing validation protocols. This mirrors earlier shifts in digital literacy where technological adoption outpaced critical engagement. PEEL responds by treating AI interpretation as a semiotic problem requiring rigorous measurement rather than a black-box convenience tool.

For the research and academic community, PEEL establishes that epistemic authority cannot be assumed through interface design or model capability alone. The framework's demonstration that systematic distortions remain invisible without non-AI measurement directly affects how institutions should evaluate AI tool adoption. Researchers and universities must now consider whether current AI deployment practices meet epistemically accountable standards. This challenges vendors to build measurement and verification capabilities alongside generative systems, shifting expectations from user competence alone to systemic accountability. The implications extend to trust in AI-assisted research outputs across academic publishing, grant evaluation, and institutional credibility.

• →PEEL combines deterministic measurement tools with LLM interpretation to reveal hidden distortions invisible to researchers.
• →AI fluency does not guarantee fidelity—systematic errors in quantity, frequency, and epistemic voice persist undetected without external validation.
• →Epistemic authority in AI systems must be designed as a systemic feature, not assumed through model capability or user familiarity.
• →Researchers currently lack adequate protocols to verify accuracy of AI-generated summaries and condensations in scholarly work.
• →Institutions adopting AI research tools without measurement frameworks risk unknowingly propagating systematic distortions in published research.