From foundational concepts and ethical considerations to hands-on strategies for prompting, evaluating outputs, curriculum design, clinical education, and scholarly work, this series will equip participants with practical competencies for responsible and effective AI use. Led by experienced educators and thought leaders in medical education, each monthly session offers actionable insights and tools to help you integrate AI into your teaching, assessment, and professional development. Whether you're just getting started or looking to deepen your expertise, this series offers a supportive and structured path toward AI literacy and leadership.
Session 1: Foundations of Skill-Building with Artificial Intelligence
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Session 2: Prompting for Educators: Effective Communication with AI
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Session 3: Evaluating AI Outputs: Ensuring Accuracy and Relevance
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Session 4: AI for Efficiency and Automation
Thursday, August 21
1:00 - 2:00pm ET
Ready to reclaim your time? Discover how AI can take the busywork off your plate. In this practical session, you’ll explore tools that streamline administrative and daily professional tasks—whether you're in education, research, or healthcare operations. Learn how to automate scheduling, summarize documents, organize data, and evaluate the ethical implications of AI-driven efficiency. Join us to unlock the power of AI for smarter, faster, and more efficient workflows in medical education.
Objectives:
- Identify AI tools that can streamline administrative and teaching tasks in medical education.
- Automate repetitive processes using AI for scheduling, document summarization, and data organization.
- Evaluate the effectiveness and ethical implications of AI-driven automation in education.
Presenters
Tonya Anderson, MAEd
Adjunct Professor and Curriculum Development Consultant
Wayne State University
Larry Hurtubise, MA, PhD
Curriculum and Instruction Consultant
Ohio State University
Ashley Reed, PhD
Assistant Director of Curriculum Services
University of Michigan Medical School
Catherine (Cat) McDermott, MD, MEd
Clinical Assistant Professor of Internal Medicine and Clinical Assistant Professor of Pediatrics
University of Michigan Medical School
Session 5: Using AI with Data and Scholarship
Thursday, September 18
1:00 - 2:00pm ET
Objectives:
- Explore AI tools for analyzing and interpreting educational and research data.
- Use AI to streamline literature reviews, data visualization, and academic writing.
- Recognize ethical considerations when using AI for research and scholarly work.
- Practice transparency and attribution for AI contributions in their work.
Session 6: Developing AI-Enhanced Curricula
Thursday, October 16
1:00 - 2:00pm ET
Objectives:
- Use AI tools to design curriculum components, including syllabi and learning objectives.
- Integrate AI-generated content into curriculum mapping.
- Evaluate the effectiveness of AI in curriculum development.
- Evaluate the impact of bias and automation on educational equity and fairness.
Session 7: Personalizing Learning with AI
Thursday, November 20
1:00 - 2:00pm ET
Objectives:
- Develop personalized learning activities using AI tools.
- Explore AI applications for virtual simulations and intelligent tutoring.
- Assess the effectiveness of AI-driven personalized learning solutions.
Session 8: AI for Assessment and Evaluation in Medical Education
Thursday, December 18
1:00 - 2:00pm ET
Objectives:
- Use AI to create assessments aligned with learning objectives.
- Analyze assessment results using AI tools to inform instruction.
- Explore AI applications for evaluating narrative data such as SOAP notes.
Session 9: Preparing for AI Integration in Clinical Education
Thursday, January 15
1:00 - 2:00pm ET
Objectives:
- Develop curriculum components on AI’s role in clinical settings.
- Evaluate the ethical and practical considerations of using AI in patient care.
- Create educational resources to explain AI applications to students.
Session 10: Continuous Professional Development in AI: Building a Lifelong Learning Plan
Thursday, February 19
1:00 - 2:00pm ET
Objectives:
- Assess personal AI knowledge gaps and develop a learning plan.
- Explore and share AI-related resources for ongoing professional growth.
- Reflect on AI use in medical education and identify areas for further experimentation.