AI’s Personas and Historical Knowledge: How Models Transform Textbook Questions into Narratives - Werner, Wiktor (Sensus Historiae, 2026)
This article offers a comparative, methodologically transparent pilot study of how different large-language-model configurations shape school-type historical narratives. Using a fixed prompt set (P1–P5) drawn from the textbook Europa. Nasza historia and four conditions - default ChatGPT baseline (C0), a ‘scientist/analytical researcher’ prompted ChatGPT (C1), NotebookLM with retrieval-augmented generation over a shared textbook source (C2), and an agentic system configured on the same knowledge base (C3) - we analyze outputs as discursive artefacts. The study combines four analytic perspectives: substantive accuracy and source anchoring, rhetorical profile and evaluative stance, formal readability and lexical texture, and logical/causal coherence. Quantitative proxies (sentence length, Polish-adapted Fog-type readability, lexical diversity via TTR and MTLD, nominalization and structural markers) are computed on the original Polish outputs without translation. Results reveal systematic trade-offs between anchoring and interpretive depth: retrieval-based and agentic configurations tend to produce a more textbook-like voice and stronger didactic scaffolding, while persona prompting increases meta-argumentation and criteria-driven evaluation. A critical-incident analysis further demonstrates that the appearance of grounding can coexist with hinge-fact failure, where a single erroneous historical anchor remains embedded in an otherwise coherent narrative. The article concludes with implications for history education, suggesting configuration-sensitive uses of LLMs and practical guardrails for classroom deployment.