Discover strategies for designing assignments that effectively integrate AI tools while maintaining academic integrity. These guidelines help create meaningful learning experiences that support student growth and understanding.
Learning Outcome Alignment
Key Considerations:
- Specific skill assessment
- Knowledge demonstration
- Course objective alignment
- Meaningful evaluation
Clear Instructions
Essential Components:
- AI tool usage guidelines
- Specific requirements
- Original work expectations
- Assessment criteria
AI Tool Integration
- Brainstorming support
- Draft development
- Research assistance
- Critical thinking focus
Attribution Methods
- Citation requirements
- AI tool documentation
- Process transparency
- Source attribution
In-Class Activities
- Guided tool usage
- Reflection exercises
- Hands-on practice
- Group discussions
Authentic Assessment
Assignment Types:
- Real-world projects
- Case study analysis
- Interactive presentations
- Applied learning tasks
Feedback and Revision
Process Elements:
- Draft submission
- Peer review
- Instructor feedback
- Revision guidance
Ethics Integration
Discussion Topics:
- Responsible AI use
- Academic integrity
- Ethical implications
- Technology impact
Transparency and Updates
Maintenance Strategies:
- Regular policy reviews
- Technology updates
- Clear communication
- Student feedback integration
