Teacher Model-Guided Bayesian Decision Support for Structured Choice Prediction in JEV

Structured choice prediction is central to decision support systems in economics, transportation, and recommendation. Classical discrete choice models (DCM) provide interpretable utilities and well-defined likelihoods, but they often rely on handcrafted attributes that miss semantic context. Recent large language models (LLMs) can provide low-cost preference signals from textual descriptions, yet such signals are noisy, miscalibrated, and difficult to use directly as decision rules. In this paper, we propose TG-BC, a Teacher LLM-Guided Bayesian decision support framework for structured Choice prediction. The framework treats the LLM as an teacher se- mantic scorer and integrates its signal into a Bayesian dis- crete choice model through calibrated utility guidance and lightweight latent steering. Posterior inference then produces calibrated choice probabilities, credible intervals, and action- level uncertainty for downstream policy simulation. We in- stantiate the framework in an urban mobility mode-choice setting, where a decision maker compares interventions such as fare discounts, service-frequency improvements, parking surcharges, and informational nudges. The experimental pro- tocol evaluates prediction, calibration, decision quality, in- terpretability, and robustness against DCM-only, LLM-only, naive-fusion, and deterministic-steering baselines