This unit synthesizes practical experience with multiple AI-driven coding, learning, and accessibility platforms to build production-ready artifacts (chatbots, assisted codebases, and learning portfolios). Students consolidate debugging, testing, ethical, and deployment practices learned earlier and prepare a documented, portfolio-ready project and responsible-use plan for further study or industry entry.
Learning Objectives
- Analyze the strengths, limitations, and failure modes of multiple AI platforms (e.g., OpenAI, Claude, Codeium, fast.ai) by comparing performance, cost, and safety considerations and producing a tool-selection justification document.
- Apply integrated development workflows to design, implement, test, and deploy a functional chatbot or AI-assisted coding artifact that uses at least one LLM API and incorporates code-assist tools (e.g., IntelliCode, Sourcery) and automated debugging practices.
- Demonstrate proficiency with scaffolded learning and productivity platforms (e.g., Edabit, Microsoft Learn, AI4All Open Learning, Gymnasium) by completing targeted modules, submitting code samples, and documenting measurable improvements in problem-solving or coding efficiency.
- Evaluate ethical, accessibility, and data-privacy implications of AI solutions and produce a mitigation and compliance plan (including accessibility checks using tools like UserWay) to accompany the final portfolio artifact.
Curriculum
- 1 Section
- 15 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 4 — AI Toolchains and Capstone Integration15
- 1.1Pairing Up with AI20 mins
- 1.2Recognizing AI’s Limitations20 mins
- 1.3Developing a Chatbot with OpenAI20 mins
- 1.4Codeium20 mins
- 1.5Edabit20 mins
- 1.6Claude20 mins
- 1.7StatQuest20 mins
- 1.8Microsoft IntelliCode20 mins
- 1.9AI4All Open Learning20 mins
- 1.10Sourcery20 mins
- 1.11Gymnasium20 mins
- 1.12Bugasura20 mins
- 1.13fast.ai20 mins
- 1.14UserWay20 mins
- 1.15Microsoft Learn20 mins
Instructor
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