Explainable AI for Multiobjective Design Decisions in University Teaching Buildings

University teaching buildings require design choices that balance energy demand, occupied thermal comfort, useful daylight, and outdoor openness. This study connects these aims to an interpretable shortlist for a teaching floor in Hengyang, China. Four explainable prediction models use a 1000-record simulation archive containing 978 distinct designs and achieve coefficients of determination of 0.935–0.985 for previously unseen design groups. Direct simulations distinguish energy-oriented, thermal-service, and view-oriented alternatives. Compared with the baseline envelope at identical occupied setpoints, a design alternative with manufacturer-linked glazing reduces the ideal-load energy index by 7.62% and discomfort hours by 52.12%, while maintaining useful daylight near 78% and outdoor openness near 9%. Controlled comparisons separate fabric and operating contributions, and catalog substitutions make specification-dependent energy trade-offs explicit. The preferred design depends on the brief: tightening the comfort requirement changes the eligible set and its equal-preference winner. Empirical-error margins guide which predictions merit checking, and direct simulations determine final performance. The workflow gives design teams interpretable environmental alternatives and a reproducible way to update their choice when comfort requirements, priorities, or glazing specifications change.