Abstract Building performance models (BPMs) such as building energy simulation models have been widely used in building design. Conventional BPMs may not be able to effectively address human- building interactions in new buildings still under design. The lack of such capability often contributes to the existence of building performance gaps, i.e., differences between predicted performance during design and actual performance of buildings. To improve the prediction accuracy of conventional BPMs, a computational framework is developed. It combines an existing BPM with context-aware design-specific data involving human-building interactions in new designs, using a machine learning approach. Immersive virtual environment (IVE) is used to capture data describing design-specific human-building interactions; and an artificial neural network (ANN) is used to combine data obtained from an existing BPM and an IVE to produce an augmented BPM. Additionally, the framework has the capability to rank influence of factors impacting human-building interactions using a feature ranking technique, which can help the design of future IVE experiments for better data collection. The framework is tested using an application of a single occupancy office. An IVE of the office is created to simulate key artificial light use events during design. The Hunt model is selected as an existing BPM. The actual use of artificial lighting in the office is observed for one month using sensors to validate the effectiveness of the framework. The results of the application have shown the potential of the framework in improving the prediction accuracy of the Hunt model evaluated against data obtained from the actual office. The results verify the important role of context-aware design-specific data in improving the prediction of human-building interactions during design. In addition, the feature ranking technique is effective in identifying influencing factors impacting human-building interactions. Limitations of this study and future work are also discussed.