Quantum Artificial Intelligence for Engineering Education
A hands-on workshop bringing together engineering and computing educators and researchers to explore quantum AI, trustworthy AI, post-quantum technologies, and their integration into engineering education.
About the Workshop
NSF SponsoredThis NSF-sponsored workshop will bring together up to 25 educators and researchers from engineering, quantum computing, and trustworthy AI. The workshop is designed to help participants understand how quantum AI technologies can be applied to engineering problems and incorporated into engineering and computing education.
Arlington Campus, VA
Co-Located with ACM TRUST 2027
Why This Workshop?
Quantum AI EducationRapid advances in quantum technologies are creating new opportunities and new security challenges for engineering systems. At the same time, many engineering programs still have limited quantum-related course content. This workshop will help educators connect emerging quantum AI concepts with practical engineering education.
Who Should Attend?
- Engineering and computing educators and researchers interested in quantum AI, trustworthy AI, or post-quantum cryptography.
- Faculty interested in introducing quantum technologies into engineering and computing courses.
- Participants from EPSCoR-jurisdiction institutions are strongly encouraged.
What You Will Do
- Attend keynotes and panels with quantum and trustworthy AI experts.
- Participate in hands-on labs on post-quantum cryptography and quantum-classical machine learning.
- Join two pre-workshop webinars and a post-workshop virtual gathering.
- Participate in pre- and post-workshop surveys.
What You Will Gain
Hands-on OutcomesReady-to-Teach Course Module
Co-design a quantum AI course module for a course you plan to teach and refine it through structured peer feedback.
Continued Faculty Learning Community
Leave with open-source materials and continue learning and exchanging ideas through a 12-month faculty community.
Featured Speakers
Invited ExpertsAssociate Chair for Research
University of Florida
Director of Education, Quantum Science and Engineering Center
George Mason University
Tentative Agenda
March 8–9, 2027The workshop combines pre-workshop preparation, in-person technical sessions and course design, and post-workshop follow-up activities.
| Time | Description & Activities |
|---|---|
| Pre-Workshop | |
| Before the Workshop | Pre-workshop survey; suggested pre-reading based on survey results; and initial design of a quantum AI module for a course participants plan to teach in 2027. |
| Day 1 — March 8, 2027 | |
| Arrival | Participant arrival and check-in. |
| 6:00–8:00 PM | ACM TRUST 2027 Banquet. |
| Day 2 — March 9, 2027 | |
| 8:00–9:00 AM | Breakfast. |
| 9:00–9:15 AM | Welcome and Opening Remarks: Workshop Co-Chairs. |
| 9:15–10:00 AM | Invited Keynote Speaker: Dr. Jessica Rosenberg, George Mason University. |
| 10:00–10:15 AM | Morning Break & Networking, including structured networking and speed mentoring. |
| 10:30 AM–12:15 PM | Parallel technical sessions on quantum learning for electrical engineering and computing, and for mechanical, civil, and materials engineering, followed by team reports. |
| 12:15–1:30 PM | Luncheon and guided exploration of quantum learning platforms and tools. |
| 1:30–2:30 PM | Moderated Panel: Quantum Machine Learning for Engineering Education. |
| 2:30–2:45 PM | Afternoon Break. |
| 2:45–4:00 PM | Co-design quantum AI modules for participants' courses and share team outputs. |
| 4:00–4:30 PM | Wrap-up and structured peer feedback: "What I will use / What I will share / What I need next." |
| Post-Workshop | |
| Following the Workshop | Implement the module in participants' courses, participate in virtual follow-up meetings, and complete post-workshop surveys. |