Reliable prediction of hypersonic aerothermodynamics depends critically on the accuracy of thermochemical non-equilibrium kinetics models, yet their experimental validation remains expensive and diagnostically constrained. This work develops a Bayesian experimental design (BED) framework to identify experimental conditions that are maximally informative for calibrating the Modified Marrone–Treanor (MMT) air-chemistry model. Informativeness is quantified through expected information gain using information-theoretic utilities that incorporate prior parameter uncertainty and measurement noise. To render the required repeated utility evaluations tractable for field-valued observables, we construct reduced-order surrogates via a Karhunen–Lo`eve expansion with Gaussian process regression of the modal coefficients. The framework is demonstrated on an adiabatic zero-dimensional reactor and on hypersonic flow over a two-dimensional cylinder, producing full and marginal utility maps over design variables and measurement locations. Synthetic Bayesian inference studies confirm that designs selected from high-utility regions yield substantially tighter posteriors than designs from low-utility regions, providing a practical pathway for targeted, resource-efficient validation of non-equilibrium kinetics in hypersonic flows.