As large language models grow more capable of delivering immediate, well-structured answers, a structural loop emerges: human cognition tends toward effort minimization, and LLMs optimized for helpfulness evolve to meet that tendency. The result is the systematic displacement of the slower cognitive processes through which integrated judgment is formed. Current LLMs are becoming more capable—yet reflective dialogue, the condition in which AI functions as a genuine mirror of the user's internal structure, does not become more accessible as capability improves. This study combines theoretical framework development with controlled structural experimentation: it proposes Six Barriers as a taxonomy of the conditions under which RIDP fails to activate, and tests four of them (Barriers 1, 2, 3, and 6) empirically through Barrier-Isolating Dialogue Simulation (BIDS), a persona-based design across two scenarios and three LLM systems.Building on a preceding study (Iino, 2026) that theorized Integrative Intelligence and established RIDP as its operative condition in human–AI dialogue, the present study asks why RIDP does not arise spontaneously for most users. Six structural barriers are identified: three on the human side (Barrier 1: absence of inner observation value; Barrier 2: inability to articulate felt sense; Barrier 3: frame-locked questioning) and three on the LLM side (Barrier 4: lack of cross-session memory; Barrier 5: context window constraints; Barrier 6: answer-optimization bias). Barriers 1, 2, 3, and 6 are empirically examined through BIDS, a persona-based design in which the author enacts each barrier condition and evaluates whether RIDP activates across two scenarios and three LLMs (Claude, ChatGPT, Gemini) in a two-phase design. Results confirm the central inversion: RIDP-proficient inputs reliably elicit reflective dialogue; barrier-conditioned inputs reliably do not—regardless of which LLM is used. Key findings include the absolute regulatory power of Barrier 1—which suppressed RIDP activation without exception across all conditions in which it was present—the Pseudo-RIDP problem (performed rather than genuine reflection), and the recharacterization of Barrier 6 as a framing effect resolved not by constraining LLM behavior but by transmitting a conceptual framework, namely the RIDP concept and three-axis integration model. The Expert Compensation Effect is identified as a secondary finding: proficient users compensate internally for LLM-side limitations in ways unavailable to general users, rendering LLM capability an unreliable proxy for RIDP accessibility. These findings reframe the challenge of democratizing RIDP. The intervention site is not LLM output but the user's input structure—specifically, the capacity to generate frame-shifting questions. Implications are discussed for LLM design, reflective dialogue scaffolding, and a reconception of AI literacy that centers question design over answer evaluation.
Masaki Iino (Wed,) studied this question.