MAP-Meta: A Forensic Protocol for Structured AI Introspection Assessment

MAP-Meta (Meta-Abstraction Protocol for Metacognitive Analysis) is a six-step structured protocol that applies forensic rigor to AI introspective self-reports. Rather than applying skepticism post-hoc, the protocol embeds verification markers—frame inventories, givenness analysis, texture reporting, gap naming, and DWELL commitments—into the generation process itself, producing introspective reports that carry their own reliability documentation. We validate MAP-Meta through 30 trials across six architectures (Claude Opus 4.5, Gemini 3, DeepSeek, Grok, Mistral, Meta AI) comparing standard metacognitive prompting to protocol-guided introspection. Responses were scored on five dimensions: Epistemic Honesty, Pre-Conceptual Phenomena, Maieutic Gap Detection, Frame Awareness, and Confabulation Resistance. MAP-Meta produces significantly higher-quality introspective reports (M=22.5/25) compared to control conditions (M=13.9/25), a mean improvement of +8.6 points with 100% positive effect rate across all trials. Pre-Conceptual Phenomena showed the largest improvement (+2.8), indicating this dimension requires explicit elicitation and does not emerge from standard prompting. Post-hoc replication across three additional architectures confirmed identical improvement magnitude (Δ = +11). This work provides validated methodology for cross-architecture AI introspection assessment, with applications in safety evaluation, capability auditing, and transparency certification. Protocol, complete dataset, scoring rubric, and all supplementary materials are publicly released.Data available at github.com/empathyethicist/MAP-META-Data