When Artificial Intelligence Misinterprets the Inquiry It Is Asked to Examine: A Continuing Observational Investigation of Conversational Redirection, Interpretive Distortion, Instruction Fidelity, and Human–AI Epistemic Reliability

When Artificial Intelligence Misinterprets the Inquiry It Is Asked to Examine: A Continuing Observational Investigation of Conversational Redirection, Interpretive Distortion, Instruction Fidelity, and Human–AI Epistemic Reliability This research paper by independent scholar Yeshayahu Ben-Yehudah examines recurring interpretive failures in human–artificial intelligence communication, particularly when an AI system produces apparently relevant responses while failing to recognize the human researcher's actual communicative intention. The investigation originates from a documented interaction in which the researcher requested an examination of his intellectual processes—specifically, how he thinks, processes information, examines interpretations, and engages in inquiry. Although the AI generated extensive analyses, the researcher identified a shift toward intellectual identity and broader personal characterization rather than the intended examination of information processing. After explicitly clarifying his objective, the researcher repeated his question to demonstrate the earlier redirection. The AI interpreted the repetition as another request for analysis, thereby producing an additional example of the very interpretive failure the researcher was attempting to expose. The study examines the distinction between topical relevance and interpretive fidelity, demonstrating how an AI system may accurately discuss a subject while misunderstanding the communicative purpose of the human participant. Particular attention is given to recursive interpretive failure, corrective feedback, conversational context, instruction fidelity, and the limitations of AI-generated self-evaluation. The investigation also identifies a significant observational contradiction: the AI repeatedly described the researcher's ability to recognize interpretive discrepancies while failing to recognize that the researcher was identifying a discrepancy in the AI's own behavior. Drawing upon contemporary research concerning large language model instruction following, conversational context, sycophancy, and AI risk management, the paper proposes a multilayer framework for evaluating lexical recognition, topical alignment, pragmatic intent fidelity, corrective uptake, and evidence-grounded AI self-assessment. The research incorporates the Ben-Yehudah Six-Pillar Process Architecture and includes the researcher's own participation, corrections, and observations as part of the evidentiary record. This continuing observational case study contributes to the examination of cognitive distortion, human–AI epistemic reliability, artificial intelligence accountability, and the preservation of human intention during AI-assisted inquiry. Its theoretical propositions remain open to further scholarly examination and empirical validation. DOI: 10.5281/zenodo.23865246