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Session Description

Free ticketed event
Generative AI (genAI) is rapidly reshaping higher education, yet many institutions lack
structured approaches to help faculty and staff explore, adopt, and implement these
tools responsibly. Faculty developers are often tasked with leading campus
conversations but may not have a roadmap for building effective programming.
Additionally, genAI tools and capabilities are advancing at a rapid pace, exacerbating
the challenge of keeping faculty and staff engaged in responsible adoption and effective
implementation.
This workshop will introduce a practical framework for creating, sequencing, and scaling
generative AI workshops for faculty and staff. Using the Swanson School of
Engineering’s 7-part genAI Workshop Series (e.g., Generative AI for the Absolute
Beginner, Task Automation for Staff and Faculty, Authentic Assessment Strategies in a
GenAI World, etc.) as a case study, participants will examine how to scaffold workshops
from introductory to advanced levels, tailor them to distinct audiences, and integrate
guardrails such as ethics, accessibility, and academic integrity.
While the primary output is a workshop plan, the process itself is also designed to
deepen participants’ own genAI literacy. By critically analyzing, deconstructing, and
sequencing workshops on topics from basic use to advanced strategies, participants will
build and refine their own comprehensive understanding of responsible genAI
integration and be better equipped to lead these conversations on their own campuses.
By the end of the session, participants will leave with a draft workshop design and a
planned sequence to launch GenAI programming at their own institutions.

Speakers

1. Dr. April Dukes

   University of Pittsburgh

   Dr. April Dukes is the Faculty and Future Faculty Program Director for the Engineering Educational Research Center (EERC) and the Institutional Co-leader for Pitt-CIRTL at the University of Pittsburgh. Dr. Dukes' current research and professional development efforts focus on two critical areas shaping the future of higher education. She serves as Senior Staff on a major National Science Foundation (NSF) project to create and validate a Personalized Learning Model (PLM) for STEM graduate education. This work is dedicated to increasing inclusivity by replacing "one-size-fits-all" training with flexible, student-centered pathways tailored to individual backgrounds and career goals. Additionally, she leads research into effective strategies for implementing the ethical and responsible use of generative AI in teaching, learning, and faculty professional development.

2. Dr. Mary E. Besterfield-Sacre

   University of Pittsburgh

   Dr. Mary Besterfield-Sacre is the Senior Associate Dean for Academic Affairs and
   Nickolas A. DeCecco Professor in Industrial Engineering at the University of Pittsburgh Swanson School of Engineering. She is also the founding Director of the Engineering Education Research Center (EERC). She has been highly active in ASEE and is co-chair of the Council of Deans' Undergraduate Experience Committee. She has been awarded the WEPAN Betty Vetter Award for Research and the ASEE Sharon Keillor Award for Women in Engineering Education for her engineering education research. She also serves on the advisory board for the AEE Journal and is a Fellow of the ASEE. Her principal research is in engineering education assessment, funded by NSF, the Department of Education, Sloan, EIF, and VentureWell. Regarding assessment, Dr. Sacre conducts primarily quantitative analyses and statistical modeling, and this work is well published in the engineering education literature.

3. Dr. Robert J Kerestes

   University of Pittsburgh

   Robert Kerestes, PhD, is an Associate Professor of Electrical and Computer
   Engineering and the Undergraduate Program Director for Electrical Engineering at the University of Pittsburgh’s Swanson School of Engineering. A native of Pittsburgh’s Mount Washington neighborhood, Bob earned his B.S. (2010), M.S. (2012), and PhD (2014) from the University of Pittsburgh, with a concentration in electric power systems. Bob’s areas of interest are in engineering education, generative artificial intelligence applications in education, and power systems modeling and analysis.