- 2. Guiding Principles
- 2.1. Be humble
- 2.2. Be bold
- 2.3. Put humanity front and center
- 2.4. Lean into learning
- 2.5. Teach with intentionality
- 2.6. No one size fits all
- 2.7. Augmentation not automation
- 2.8. Think beyond the classroom and the campus
- 3. Recommendations
- 3.1. Adapt educational processes for an AI-aware world
- 3.1.1. Revisit course goals
- 3.1.2. Ensure durable learning through new course policies, structures, and forms of assessment
- 3.1.3. Emphasize experiential and project-based learning
- 3.1.4. Build structured in-person social learning into subjects
- 3.1.5. Preserve and expand out-of-class research and career experiences
- 3.1.6. Reconsider grades and incentives
- 3.1.7. Expand in-person spaces for labs and in-person evaluation
- 3.1.8. Provide AI use policies, with justification
- 3.1.9. Exercise caution with AI detectors and online exam platforms
- 3.1.10. Support responsible experimentation in the curriculum
- 3.2. Center people, community, and the residential experience
- 3.2.1. Define and communicate the value of residential education
- 3.2.2. Strengthen social connection and personal wellbeing
- 3.2.3. Encourage instructor disclosure around their own AI use
- 3.2.4. Teach effective, responsible, and ethical use of AI
- 3.2.5. Recognize and mitigate negative impacts of AI
- 3.2.6. Acknowledge AI use in theses and other research work
- 3.3. Build processes, teams, and tools for continuous reflection, iteration, and improvement
- 3.3.1. Establish an ongoing AI and education committee
- 3.3.2. Create school/college- or department-level AI Leads
- 3.3.3. Fund AI Fellows and an AI Implementation Team
- 3.3.4. Create an AI Pilot Fund
- 3.3.5. Provide ongoing training and instructor support
- 3.3.6. Develop metrics
- 3.3.7. Ensure equitable technology access
- 3.3.8. Protect sensitive data and preserve model choice
- 3.3.9. Establish privacy, logging, and auditing policies
- 3.3.10. Monitor AI costs and environmental impact
- 4. Conclusion