This document is a follow-up research note to two previously published Zenodo records. The first record documented a recursive meta-dialogue with Claude concerning historical cyclicality, transformation, self-referential reasoning, and recursive dialogue structures. The second record examined the same Claude dialogue through ChatGPT-based structural analysis, comparing the generative styles, ethical boundaries, and self-understanding of Claude and ChatGPT. The present document extends these prior records into a practical interface proposal for AI response evaluation. Rather than treating AI-generated outputs as self-evidently reliable, it proposes a five-axis meta-evaluation UI intended to help users examine the assumptions, verifiability, uncertainty, overextension, and contextual bias embedded in AI responses. The proposed five axes are integrity, verifiability, deepening rate, premise transparency, and asymmetry correction. These axes are not intended to automatically determine the truth or falsity of AI-generated responses. Rather, they are intended as an auxiliary evaluation layer to support critical human judgment. This document should be understood as a prototype design note, not as a completed evaluation system. Future work should test the framework across multiple models, users, and task domains to evaluate its usefulness for AI literacy, hallucination detection support, and human-centered AI response evaluation. This version additionally includes a methodological addendum titled “Recursive Self-Scoring and Category-Boundary Failure in AI-Generated Reasoning.” The addendum analyzes a further dialogue case in which an AI system repeatedly evaluated its own prior responses while also exposing failures in the application of its own scoring or visualization rules. In particular, the addendum focuses on recursive self-scoring, scoring omission, repeated omission after self-diagnosis, prompt-framing sensitivity, temporal uncertainty, source-density concerns, and category-boundary misrecognition. The case suggests that AI self-evaluation should not be understood as a guarantee of correctness or self-correction. Instead, it may function as a supplementary diagnostic layer that helps human users identify where AI-generated reasoning requires closer scrutiny, external validation, or wider confidence intervals. This additional material is exploratory. It treats the observed scores and self-evaluations as AI-generated meta-evaluative outputs, not as statistically validated metrics. Its purpose is to extend the five-axis meta-evaluation framework with a concrete case study of recursive self-scoring failure, category-boundary failure, and human-guided correction.
Takufumi Sato (Sun,) studied this question.