DialogueQ: A Quantum-Inspired Inference Engine for Adaptive Educational Dialogue

We present DialogueQ, a conversational inference engine that applies the mathematical formalism of quantum mechanics to a problem every AI dialogue system faces: what to do when you are not sure of the answer. Standard large language models respond fluently regardless of their actual knowledge — a form of calibrated overconfidence that is especially damaging in educational settings. DialogueQ takes a different approach: it maintains a superposition of candidate responses across five processing layers and collapses to a definitive answer only when confidence is sufficient. When it is not, the system enters Socratic mode, converting uncertainty into pedagogically productive dialogue rather than papering it over with plausible-sounding text. A hybrid fallback to external LLM inference handles queries that genuinely exceed the system's knowledge domain. Preliminary interaction logs show that quantum-inspired confidence modulation activates Socratic mode in 20–30% of queries where a retrieval baseline would answer directly — and incorrectly. DialogueQ is deployed as the inference engine of the Quantum Intelligence platform by Eloisa Technologies.