Objectives 

• Provide faculty with foundational knowledge of AI concepts and capabilities.
• Demonstrate practical use cases relevant to academic settings, such as grading assistance, content generation, and predictive analytics.
• Foster responsible and ethical use of AI in education.
• Enable faculty to identify opportunities for AI integration within their disciplines.

Expected Outcomes

• Increased faculty proficiency in applying AI tools to improve efficiency and innovation.
• Development of discipline-specific strategies for AI adoption.
• Creation of a collaborative environment for sharing best practices and continuous improvement.
• Creation of a scholarly capstone product for dissemination

Participant Recognition and Benefits

These benefits are designed to acknowledge participants’ commitment to professional growth and to support continued engagement in academic innovation and faculty development.

• Access to a premium Copilot license.
• Certificate of completion.
• Digital badge and commemorative pin.
• Eligibility to serve as a speaker in future faculty development series and recognition on the CEE website.
• Potential credit toward scholarly activity points, in accordance with institutional guidelines

Location

Active Learning Practice (ALP) lab
E4-223

Schedule

Enrollment is limited to eight faculty.

Questions

Nehad El-Sawi, PhD
Assistant Vice President, Academic Innovation & Enhancement
[email protected] 

Sessions

Foundations of AI: From Principles to Practice 

During this interactive session, faculty will develop a shared understanding of AI terminology, DMU core principles, and considerations guiding AI use in education. Through guided practice using DMU-approved tools, faculty will explore opportunities for responsible, student-centered integration.

Leads: 

• Erfan Mojaddam, EdM, Chief Information Officer
• Carlyn Cox, MA, Director for Educational Technology

AI for Assessment: Writing High-Quality Questions, Quizzes, and Quiz Bots 

It focuses on designing high-quality courses and board-style questions, building effective D2L quizzes, and using Microsoft Copilot to create tutoring bots from existing lecture materials. The session emphasizes alignment with learning objectives, the inclusion of feedback, and leveraging D2L to collect statistics on AI-generated items.

Lead:

• Martin Schmidt, PhD, Professor of Biochemistry and Nutrition

Designing Engaging Content in D2L with Lumi Pro  

This session demonstrates how to move beyond static slide decks by turning PowerPoint content into engaging D2L learning modules with Lumi Pro. Faculty will leave with strategies for enhancing student learning through interactive, well-structured course content.

Lead: 

• Carlyn Cox, MA, Director for Educational Technology

Using AI to Enhance Active Learning Experiences

This session explores how AI can enhance the quality of students' active learning activities. Drawing from a real intervention in which AI was used as a motivational interviewing patient, participants will learn to identify active learning activities in their own courses where AI can raise the ceiling on student experience. Faculty will leave with practical strategies for designing AI-powered interactions that deepen engagement. 

Leads:

• Brian Pinney, PhD, Assistant Director, Center for Educational Enhancement-Academic Support
• Kari Smith, DPT, Professor of Physical Therapy

Using AI for thematic analysis

This session will introduce practical ways for faculty to use AI with written student data. Topics include using AI to identify patterns in student comments while retaining faculty judgment, exploring course evaluations for specific areas of interest, examining reflective writing for trends related to course concepts, and using AI as a first pass for rubric-based grading. The focus will be on realistic, faculty-controlled uses of AI that save time and support instructional improvement.

Leads:

• Martin Schmidt, PhD, Professor of Biochemistry and Nutrition
• Brian Pinney, PhD, Assistant Director, Center for Educational Enhancement-Academic Support

AI and Clinical Case Scenarios for Health Professions Education 

This hands-on session equips faculty with practical strategies for using AI to create high-quality clinical case scenarios for didactic teaching, including lectures, labs, and simulated patient experiences. Participants learn effective prompting to align cases with learning objectives and assessments, and how to critically evaluate AI-generated content for clinical accuracy. By the end of the session, participants will have a complete, ready-to-use clinical case scenario for their course.

Lead:

• David Plutschack, OTD, Associate Professor of Occupational Therapy

Guiding, Designing, and Mentoring: A Faculty Roadmap for GenAI in Healthcare Education 

This session provides a concise, practical framework for faculty to integrate generative AI into healthcare education. Participants will examine a five-step approach for guiding students in responsible AI use, explore curriculum-aligned Socratic AI tutoring designed by faculty or through faculty–student collaboration, and consider how class time can be restructured after AI-supported learning to deepen application, clinical reasoning, and discussion.

Lead:

• Neil Mehta, MBBS, MS, Jones Day Endowed Chair in Medical Education, Professor of Medicine, Associate Dean for Curricular Affairs, Cleveland Clinic Lerner College of Medicine at Case Western University

Capstone 

Apply AI tools to redesign one component of your course, share, and reflect with the team.

Leads: 

• All participants and session leads.

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