•A situational awareness Bayesian network is developed based on expert knowledge.•It enables exploring biophysical pathways starting with the expert knowledge.•It allows physicians to conduct their familiar “what if” counterfactual inference.•It outperforms other credible models for the joint prediction of treatment outcomes.•It has the potential to be a key component of personalized adaptive radiotherapy. A situational awareness Bayesian network (SA-BN) approach is developed to improve physicians’ trust in the prediction of radiation outcomes and evaluate its performance for personalized adaptive radiotherapy (pART). 118 non-small-cell lung cancer patients with their biophysical features were employed for discovery (n = 68) and validation (n = 50) of radiation outcomes prediction modeling. Patients’ important characteristics identified by radiation experts to predict individual’s tumor local control (LC) or radiation pneumonitis with grade ≥ 2 (RP2) were incorporated as expert knowledge (EK). Besides generating an EK-based naïve BN (EK-NBN), an SA-BN was developed by incorporating the EK features into pure data-driven BN (PD-BN) methods to improve the credibility of LC or / and RP2 prediction. After using area under the free-response receiver operating characteristics curve (AU-FROC) to assess the joint prediction of these outcomes, their prediction performances were compared with a regression approach based on the expert yielded estimates (EYE) penalty and its variants. In addition to improving the credibility of radiation outcomes prediction, the SA-BN approach outperformed the EYE penalty and its variants in terms of the joint prediction of LC and RP2. The value of AU-FROC improves from 0.70 (95% CI: 0.54–0.76) using EK-NBN, to 0.75 (0.65–0.82) using a variant of EYE penalty, to 0.83 (0.75–0.93) using PD-BN and 0.83 (0.77–0.90) using SA-BN; with similar trends in the validation cohort. The SA-BN approach can provide an accurate and credible human–machine interface to gain physicians’ trust in clinical decision-making, which has the potential to be an important component of pART.