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Course-Level AI Implementation Plan

Overview

This template provides a structured approach for incrementally integrating AI tools into an engineering course. It is designed to be adaptable across engineering disciplines and course levels, from introductory to advanced.

Implementation Timeline

Phase 1: Preparation and Planning (Before Semester)

Week -4 to -3: Tool Exploration and Selection

[ ] Complete personal exploration of 2-3 AI tools relevant to your course
[ ] Document specific capabilities and limitations for your subject matter
[ ] Select primary tool(s) for integration
[ ] Review institutional policies on AI use
[ ] Identify areas where AI could address existing course challenges

Week -2 to -1: Course Material Adaptation

[ ] Review existing learning objectives through an AI lens
[ ] Determine "AI-allowed," "AI-restricted," and "AI-enhanced" components
[ ] Modify 1-3 existing assignments to incorporate AI
[ ] Create documentation requirements for AI use
[ ] Develop AI onboarding materials for students
[ ] Update syllabus with AI policies and expectations

Phase 2: Initial Implementation (Weeks 1-5)

Week 1: Introduction and Onboarding

[ ] Introduce AI tools and policies during course introduction
[ ] Conduct baseline assessment of student AI familiarity
[ ] Demonstrate basic AI tool usage relevant to your course
[ ] Assign first low-stakes AI exploration activity

Week 2-3: Guided Exploration

[ ] Implement structured AI practice activities
[ ] Provide examples of effective vs. ineffective prompts
[ ] Facilitate discussion on AI strengths/limitations
[ ] Create opportunities for student questions and troubleshooting

Week 4-5: First Integrated Assignment

[ ] Implement first formal AI-integrated assignment
[ ] Provide clear documentation requirements
[ ] Collect student feedback on experience
[ ] Address emerging challenges and misconceptions

Phase 3: Expansion and Refinement (Weeks 6-10)

Week 6-7: Enhanced Integration

[ ] Introduce more advanced AI usage techniques
[ ] Implement discipline-specific applications
[ ] Begin scaffolding toward higher-order thinking with AI
[ ] Conduct formative assessment of AI integration impact

Week 8-10: Collaborative Implementation

[ ] Facilitate peer learning around effective AI use
[ ] Implement group activities using AI tools
[ ] Introduce critical evaluation of AI outputs
[ ] Begin preparation for AI-aware assessments

Phase 4: Advanced Implementation and Assessment (Weeks 11-15)

Week 11-13: Advanced Applications

[ ] Implement complex, discipline-specific AI applications
[ ] Connect AI use to professional practice
[ ] Focus on critical evaluation and enhancement of AI outputs
[ ] Prepare students for AI-aware final assessments

Week 14-15: Reflection and Evaluation

[ ] Conduct summative assessment including AI components
[ ] Facilitate student reflection on AI learning journey
[ ] Gather comprehensive feedback for course refinement
[ ] Document outcomes and lessons learned for future iterations

Implementation Worksheet

Course Context

Course Title: ________________________________________________

Level: _____________________ Typical Enrollment: ____________

Subject Area: _______________________________________________

Current Challenges: _________________________________________

Primary Learning Objectives: _________________________________

AI Integration Goals

Primary Integration Goal 1: ___________________________________

Primary Integration Goal 2: ___________________________________

Success Metrics: ____________________________________________

Tool Selection

| AI Tool | Specific Applications in Course | Implementation Complexity (1-5) | Student Access Plan | |---------|----------------------------------|--------------------------------|---------------------| | | | | | | | | | | | | | | |

Assignment Transformation Plan

| Existing Assignment | Current Format | AI Integration Approach | Documentation Requirements | Assessment Modifications | |---------------------|----------------|-------------------------|----------------------------|--------------------------| | | | | | | | | | | | | | | | | | |

Student Support Plan

Onboarding Activities: ________________________________________

Resources to Provide: ________________________________________

Troubleshooting Approach: ____________________________________

Accessibility Considerations: _________________________________

Policy Development

AI Use Policy Summary: _______________________________________

Documentation Requirements: __________________________________

Academic Integrity Provisions: ________________________________

Assessment Strategy

Formative Assessment Approaches: _____________________________

Summative Assessment Approaches: ____________________________

Process vs. Product Evaluation Balance: _______________________

Implementation Challenges and Mitigation Strategies

| Anticipated Challenge | Mitigation Strategy | Contingency Plan | |-----------------------|---------------------|------------------| | | | | | | | | | | | |

Sample Language for Syllabus

Example 1: Gradual Implementation Model

AI Tools in This Course:

This course takes a developmental approach to AI tools, preparing you for their professional use while ensuring you develop fundamental skills. The course is divided into three phases:

Phase 1 (Weeks 1-5): AI tools are restricted to specific, marked activities to ensure development of baseline skills.

Phase 2 (Weeks 6-10): AI tools are permitted for designated parts of assignments with proper documentation of use.

Phase 3 (Weeks 11-15): AI tools are fully integrated, with emphasis on critical evaluation and enhancement of AI outputs.

Throughout all phases, you must document your AI use according to the Documentation Guidelines provided. Undocumented AI use will be considered an academic integrity violation.

Example 2: Task-Based Implementation Model

AI Tool Policy:

This course uses a task-based approach to AI tools. For each assignment, specific components will be designated as:

- AI-Restricted: No AI tools permitted (focuses on fundamental skill development)
- AI-Allowed: AI tools permitted with documentation (focuses on efficient workflow)
- AI-Enhanced: AI tools required with critical evaluation (focuses on professional judgment)

Each assignment will clearly indicate these designations. Students must complete the Documentation Form for any AI use, which becomes part of the assignment submission. The form includes:
1. Which AI tool was used
2. Exact prompts/inputs provided
3. How the AI output was evaluated and modified
4. Reflection on the limitations of the AI approach

Evaluation and Iteration

At the conclusion of the semester:

Review Implementation Outcomes:
- Student performance on learning objectives
- Quality of AI-assisted work
- Student feedback on AI integration
- Challenges encountered and solutions developed
Document Lessons Learned:
- What worked well
- What needs refinement
- Unexpected outcomes
- Technical issues encountered
Plan Next Iteration:
- Adjustments to assignments
- Refinements to policies
- Additional support resources needed
- Expansion to other course components

This implementation plan was developed as part of the "Strategies for Integrating Generative AI in Engineering Education" in collaboration with Claude-3.7 Sonnet.