Workshops and Panel

The primary goal of the session is to give attendees an opportunity to learn about the affordances and challenges of AI in engineering and computing education from the viewpoints of AI and education leaders in a diversity of contexts. The panel will provide a forum for attendees to ask about how AI is regulated and/or integrated in the various educational realms represented. Attendees will leave the session with a more comprehensive understanding of the role of AI in engineering education and the ways AI in education can be approached and with connections to other faculty and university leadership interested in innovating or studying in the space of AI-based education.
While many recent articles can be found about AI in engineering and computing education, written work allows for only static, one-directional sharing of information and doesn’t provide an opportunity for experts different backgrounds and professional roles to engage in discussion. A panel session allows for the real-time discussion of topics of interest with discussion directions guided by the interests of attendees. The panel structure will emphasize frequent interactions between panelists, moderators, and participants. The interactive panel session will align with conference topics around AI and Machine Learning for Instruction, Learning, and Assessing Student Outcomes.

Engineering education will not meet its talent and innovation goals without intentional structures that broaden participation and develop leadership at every stage. This panel reframes STEM Frontiers through four practical strands that programs can adopt immediately: 
1. Mentoring & Sponsorship at Scale. How to design near‑peer, peer, faculty–student, and industry–academic models that are structured (clear roles, mentor compacts, meeting cadence), incentivized (recognition, workload credit), and sustainable (mentor training, rotation, avoiding overload). We will compare models that demonstrably improve persistence, confidence, and readiness for advanced roles. 

2. Leadership Pathways. Building transparent pipelines from classroom to lab to committee and project leadership: micro‑roles, rotating responsibilities, and apprenticeship approaches that cultivate communication, project management, ethical reasoning, and community stewardship. 

3. Inclusive Program & Course Design. Pragmatic adjustments that reduce friction for heterogeneous learners—teamwork norms, assessment options, hybrid access to labs/resources, bridging programs, and communities of practice that support belonging and progression. 

4. Community & Continuity. From one‑off activities to living ecosystems: mentoring circles, student–alumni bridges, and light governance so initiatives outlive any one champion. We share how WIE and EdSoc chapters can act as connective tissue across institutions. 

Panelists will ground discussion in cases spanning national and international STEM initiatives, center‑level mentoring ecosystems, research groups, and motivational design (challenges, projects, etc.). We will surface both successes and failures, then converge on reusable artifacts and good practices

The primary goal of this workshop is to demonstrate the practical implementation of Agentic AI in the virtual course lifecycle—especially its role in automating course promotion, lead generation, and participant engagement. By equipping participants with actionable strategies and toolkits, the workshop supports DEMOcon’s mission to advance innovative, scalable digital education practices. Attendees will explore how intelligent agents can streamline institutional operations while maintaining educational quality and responsiveness

The primary goal of this workshop is to introduce educators to the possibilities of innovative online laboratory implementations through the integration of IoT and AI technologies. Participants will explore the concept of online laboratory management systems OLMS, learn about relevant standards in the field, and gain hands-on experience by designing and implementing an activity that combines hardware components with machine learning classification algorithms.