AI Future of T&L

This week I would like to share a guidance document that was released this month by the U.S. Dept of Education on Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations (May 2023). The report addresses two overarching questions:

1. What is our vision of a desirable and achievable educational system that leverages automation to advance learning while protecting and centering human agency?

2. How and on what timeline will we be ready with necessary guidelines and guardrails and convincing evidence of positive impacts, so that constituents can ethically and equitably implement this vision widely?

Key sections of this document that may be of interest to educators include:

• Reasons to Address AI in Education Now

• Toward Policies for AI in Education

• Human in the Loop AI Learning

• AI Enables Adaptivity in Learning

• Intelligent Tutoring Systems: An Example of AI Models

• Always Center Educators in Instructional Loops (so they can best support students)

  • Teachers make moment-to-moment decisions as they do the immediate work.

  • Teachers prepare for, plan, and reflect on teaching, which includes PD.

  • Teachers participate in decisions about the design of AI-enabled tech, participate in selecting and shape the evaluation.

• Using AI to Improve Teaching Jobs

• Preparing and Supporting Teachers in Planning and Reflecting

• AI Formative Assessment: Enhance Feedback Loops (measure what matters)

  • Enabling Enhanced Question Types

  • Measurement of Complex Competencies

  • Providing Real-Time Feedback

  • Increasing Accessibility

• Re-thinking Teacher Professional Development

• Recommendation #1: Emphasize Humans in the Loop

• Recommendation #2: Align AI Models to a Shared Vision for Education

• Recommendation #3: Design Using Modern Learning Principles

• Recommendation #4: Inform and Involve Educators

The report addressed how AI models must be expanded to address how we learn, from:

1. individual cognition to include social and other aspects of learning (social learning supports learning to reason, explain, and justify; and develop self-regulated learning);

2. neurotypical to neurodiverse learners (benefit from different learning paths and from forms of display and input that fit their strengths - these are not learning styles);

3. fixed tasks to active, open, and creative tasks (students to learn to invent and create innovative approaches.); and

4. correct answers to additional goals (students to learn how to self-regulate when they experience difficulties; able to persist in working on a difficult problem).

References

U.S. Department of Education, Office of Educational Technology, Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations, Washington, DC, 2023.