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AI-Assisted Project Development

Developing Semester-Long Projects with AI

A Backwards Design Approach

For Expert Educators: Leveraging AI While Maintaining Pedagogical Control

The Traditional Project Problem​

📅 Typical Timeline

  • Week 4: Project assigned
  • Week 5-13: radio silence
  • Week 14: Everything due
  • Finals week: Panic grading

😰 Predictable Results

  • Student procrastination
  • No feedback loop
  • All-nighters before deadline
  • Underwhelming submissions

We know better. Why do we keep doing this?

Why Semester-Long Projects Matter​

Pedagogical benefits that homework can't provide:

  • Integration — Apply multiple concepts together, not in isolation
  • Authenticity — Mimic real-world progressive development
  • Motivation — Watch substantial work grow over time
  • Portfolio Value — Meaningful showcase for students
  • Depth — Go beyond surface-level understanding

The Challenge: Keeping students on track without end-of-semester panic

The Core Principle​

Learning-Aligned Milestones

Each milestone should be achievable using
skills students have just learned in lectures

Week 4: Milestone 1 (uses Weeks 1-3 concepts)
Week 7: Milestone 2 (adds Weeks 4-6 concepts)
Week 10: Milestone 3 (adds Weeks 7-9 concepts)
Week 13: Final delivery (adds Weeks 10-12 concepts)

Backwards Design: The AI-Assisted Process​

Traditional Backwards Design​

  1. Define learning objectives
  2. Design assessment
  3. Create scaffolding

Key Innovation: Staged Artifact Development​

Stage 1: Generate concepts
Stage 2: Identify milestones
Stage 3: Expand requirements

Stage 4: Create rubrics
Stage 5: Generate support
Stage 6: Test-solve yourself

Critical: Don't generate everything at once. Build incrementally with review checkpoints.

Stage 1: Generate Project Concepts​

Working in a Course Repository​

Key advantage: AI reads your actual course files

Prompt: "Examine course structure. Generate 3-4 
semester-long project concepts that integrate these 
learning objectives throughout the term. Keep brief: 
2-3 paragraphs per concept."

Your job: Select the most pedagogically sound concept

  • Student engagement
  • Real-world relevance
  • Portfolio value
  • Skill integration
  • Feasibility

Stage 2: Identify Natural Milestones​

Prompt: "I've chosen the [concept] project. Examine 
course structure. Identify 4-5 natural milestone points. 
For each:
- Suggested due week (AFTER required skills taught)
- Available skills from lectures
- High-level deliverable (one sentence)
- Why this is a natural breaking point
- Time estimate (3-6 hours)

Save to assignments/project-overview.md"
⚠️ Critical Review Checkpoint
  • Verify timing relative to exams/breaks
  • Check milestone spacing
  • Ensure progressive difficulty
  • Manually edit for your voice and expectations

git commit -m "Add project overview with milestone timeline"

Stage 3: Expand Each Milestone​

Work on ONE milestone at a time, not all at once

Prompt: "@project-overview.md has our overview. 
Create detailed requirements at 
@project-milestone-1.md for Milestone 1 only.

Include:
- Skills learned by Week 3 (reference lectures)
- Specific deliverables (files, docs, features)
- Acceptance criteria ("done" definition)
- Time estimate (3-5 hours)
- Why this milestone matters pedagogically"

Edit for Specificity​

❌ AI draft:
"Create appropriate documentation"

✅ You edit:
"Create README.md with: title, description (2-3 sentences), 5+ features, tech stack. Use markdown from Lecture 2."

Repeat for remaining milestones: project-milestone-2.md, project-milestone-3.md, etc.

Stage 4: Lecture-Grounded Rubrics​

Prompt: "Create grading rubric for 
@project-milestone-3.md (20 points) that:

- Assesses technical correctness
- Focuses on NEW material since Milestone 2
- Uses code standards from 
  @lecture-notes/l2-best-practices.md
- References specific lectures for each criterion"
Why Reference Lectures Explicitly?
  • Uses same terminology students learned
  • Criteria grounded in what was taught
  • Students can review specific lectures
  • Makes grading feel predictable, not arbitrary
  • TAs grade consistently

Stage 5: Generate Support Materials​

Prompt: "Generate support materials for 
@project-milestone-2.md:

- 'Getting Started' — concrete first steps
- 'Common Pitfalls' — typical errors and fixes
- 'Resources' — links to docs, tutorials, examples

Save to project-milestone-2-support.md"

Additional Support AI Can Generate​

  • FAQ (10 common questions + answers)
  • Troubleshooting Guide (common errors + fixes)
  • Simplified Example (different domain, demonstrates concept)
  • Rubric Explanation (how to earn full credit)

Your job: Manually refine with your specific warnings, encouragement, and resources

Common Pitfalls​

❌ Watch Out For:

Common Pitfalls​

❌ Watch Out For:

1. Front-Loaded Difficulty — First milestone too ambitious
→ Ask AI: "Is milestone-1 achievable after lectures 1-3?"

2. Disconnected Milestones — Students start over each time
→ Ask AI: "Verify each milestone builds on previous deliverables"

3. Skills Gap — Milestone requires skills not yet taught
→ Ask AI: "Verify all required skills taught before Week 12"

4. No Feedback Loop — Grades without actionable guidance
→ Grade within 1 week; use AI feedback templates

5. Unrealistic Time Estimates — "Should take 3 hours" (takes 10)
→ Test-solve yourself. Add 50-100% buffer.

6. Vague Requirements — Students don't know when "done"
→ Ask AI: "Identify vague requirements; suggest measurable alternatives"

Adapting Mid-Semester with AI​

Be responsive. Rigid adherence to original plan can tank student success.

⏱️ Milestones Taking Too Long

"Students struggling with M2 — taking 8 hours vs 5. Suggest ways to reduce scope of M3-M5 while preserving learning objectives."

⚡ Milestones Too Easy

"Students completing quickly. Suggest extension options for M3-M5 that challenge advanced students without changing core requirements."

AI helps you adapt quickly while maintaining pedagogical soundness.

Key Takeaways​

  1. Staged Artifact Development — Build incrementally with review checkpoints
  2. Learning-Aligned Milestones — Each uses skills just taught
  3. Lecture-Grounded Rubrics — Reference specific lectures for predictability
  4. Human Judgment is Essential — AI drafts, you refine and decide
  5. Test-Solve Yourself — If you can't do it in allocated time, neither can students
  6. Be Responsive — Adapt mid-semester based on student progress

Remember: AI handles drafting and structure.
You provide pedagogy, refinement, and final judgment.

The Meta Lesson: AI Philosophy​

What This Process Models​

✅ Effective AI Usage:

  • Only ask for what you can review for accuracy
  • Break large tasks into reviewable chunks
  • Iterate on scope when results unsatisfactory
  • Maintain expertise through direct practice
  • Make conscious decisions about assistance boundaries

❌ Ineffective AI Usage:

  • "Generate complete project" → unreviewed bulk content
  • No editorial checkpoints
  • Loss of pedagogical voice
  • Skill atrophy from over-delegation

This workflow embodies the principles we teach students about AI.

Questions?

Let's discuss:

  • How might this approach adapt to your discipline?
  • What review checkpoints matter most to you?
  • Where do you see risks or limitations?
  • How do you maintain pedagogical control with AI?