When AI Reviews Its Own Research: A Continuing Case Study of Recurring Errors, Evidentiary Limitations, and the Reliability of Artificial Intelligence Self-Auditing

This research paper presents the fourth stage of an ongoing investigation by independent scholar Yeshayahu Ben-Yehudah into recurring artificial intelligence system errors, evidentiary limitations, and the reliability of AI self-auditing. The study examines a previously published AI-assisted manuscript that was intentionally released without human editorial review, followed by an AI-generated assessment of its own work. The investigation evaluates reported errors, unsupported conclusions, methodological limitations, bibliographic deficiencies, and inconsistencies in error classification. Particular attention is given to the distinction between genuine documentary defects, previously disclosed limitations, and inaccuracies introduced during subsequent AI evaluations. The study contributes to an expanding public research record examining AI instruction fidelity, scholarly reliability, evidence integrity, human oversight, and accountability for the design and deployment of artificial intelligence systems. DOI: 10.5281/zenodo.23848146