Meeting 2: Prompt Engineering and Integration Brainstorming
Discussion Outline
- Pedagogical Use Cases of AI
- Prompt Engineering Strategies for Learning
- Exploring Generative AI Providers for Canvas Integration
- Group Reflection & Discussion
Case Study: Not All Chatbots Teach
- Recent study: Not All Chatbots Teach
- Compared a structured, outcome-aligned chatbot vs. a generic LLM wrapper
- Key finding: Structured AI tools significantly improved student performance and clarity in software testing tasks
Implications for Teaching
- Structure and scaffolding matter
- Alignment with learning outcomes is crucial
- Promotes metacognition and reduces surface-level AI use
Reflect & Discuss
Questions:
- Where have you seen AI help or hinder student learning?
- What risks or opportunities do you see in using AI inside your own course context?
Prompt Engineering for Learning
- Prompts = Pedagogical design space
- AI output quality hinges on the prompts we design
Using Prompts as Guardrails
- Instructor-written prompts can:
- Set boundaries (academic integrity, tone, focus)
- Scaffold learning (step-by-step guidance, Socratic questioning)
- Encourage metacognition (reflect on process)
Prompt Examples
- “Ask the student questions to deepen their understanding of X.”
- “You are a peer reviewer. Give friendly but rigorous feedback on their code.”
- “Use the Feynman Technique to help the student clarify their concept.”
Reflect & Discuss
Questions:
- What kind of prompts might support your students’ learning?
- How can prompt templates become reusable teaching assets?
Exploring Generative AI Providers
- Let’s compare the top options for integration and customization:
Major Providers
Provider | Notes |
---|---|
OpenAI | GPT-4o, API access, supports embeddings, used widely |
Gemini | Google’s LLM, integrated into Google Workspace, strong data privacy controls |
Anthropic Claude | Emphasizes safety and guardrails |
Cohere, Mistral, HuggingFace | Research/community focused, fine-tuning support |
Building Custom Chatbots for Canvas
🛠️ Options include:
- OpenAI Functions + Flask/FastAPI backend
- Google Cloud Vertex AI with Gemini + Dialogflow
- Custom wrappers using LangChain or Semantic Kernel
- Embed into Canvas via LTI tools, iframes, or static tools
Reflect & Discuss
Questions:
- What role could a chatbot play in your course?
- Would it be formative, summative, exploratory?
- What barriers do you foresee?
Wrap-up
Today’s Themes:
- Pedagogical framing is essential for AI tools
- Prompting is teaching
- Integration is possible — and powerful — in Canvas
Next Steps
- Share a prompt or idea in our collaborative doc
- Try one AI tool this week — reflect on use
- Prepare for Next Week: Deeper Prompt Engineering and use case design