Work You Can Explain
Practical AI, computing, and learning for students
term
Structure projects, define completion, test assumptions, diagnose failures, and preserve evidence of your work.
Use this path after the programming and Git foundations to make project work more deliberate, testable, and explainable.
Use the ordered guides as a recommended route. Begin with the first guide for a systematic introduction, or enter where your current task begins and follow prerequisite links as needed.
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Organize source, tests, documentation, scripts, and examples in a small project skeleton that makes expected work easy to find.
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Write observable success criteria for scope, behavior, tests, documentation, and delivery before beginning a project.
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Debug with a reproducible cycle of observation, reduction, hypothesis, experiment, and verification.
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Design a small meaningful test set covering normal behavior, important boundaries, and invalid inputs.
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Give a classmate, instructor, maintainer, or AI enough safe evidence to reproduce and investigate a failure.
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Present a few projects through their problems, decisions, implementation, tests, evidence, credits, and honest reflection.
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Use demonstrated problems, outcome tests, time limits, and stop rules to keep tool maintenance from replacing student work.
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Archive course work, test recovery, review tools, update setup documentation, preserve repeated work, and select portfolio evidence.
