Teach learners to validate what they build

What would it take for students without programming backgrounds to examine a published AI model rather than merely use its predictions? PRAISE describes a workflow for selecting, installing, reproducing and validating scientific models, with explicit routes back when something fails. Three chemistry undergraduates and their tutor applied it to an enzyme-interaction model over four months. They changed computing environments, expanded data coverage and examined the evaluation pipeline across four iterations. They ultimately reproduced and extended the model. The educational value proposed here is disciplined checking through a real technical project. This is one supported implementation case, not a controlled demonstration of learning gains or universal accessibility.