Funded at $4,200 toward a high-performance, 32GB GPU workstation, this project brings essential shared-compute infrastructure on campus to provide computer science students with hands-on fine-tuning and experiment design experience for AI workforce readiness.
Modern AI runs on GPUs, but St. Edward's students have had little access to them. This pilot acquires a high-performance workstation with a 32GB AI-capable GPU, hosted on campus and accessed remotely through a job queue, the same shared-compute workflow used in industry and research. Piloted in COSC 4355: Natural Language Processing, students will fine-tune transformer models, run open-weight language models, and plan their own GPU experiments. Beyond NLP, the workstation will serve Introduction to AI, Computer Vision, Machine Learning Algorithms, and undergraduate AI research, and its design allows GPUs to be added or upgraded over time. The project prepares students for post-graduation careers in AI, deepens their understanding of how AI systems actually work, and reduces reliance on recurring cloud-compute costs. As a pilot, it will produce shared materials and usage data to support larger grants expanding compute across departments.
Funded with $800 for AI tokens along with AI license support from SEU Builders Lab, and IET student ambassador support, this project integrates LLM-driven homework feedback and progress summaries to boost student engagement and success in high-DFW gateway math courses.
St. Edward's University is piloting an AI Learning Management System (AI-LMS) in gateway mathematics courses, where DFW rates have averaged over 40 percent. The AI-LMS integrates student homework directly into an LLM, using a mix of deterministic and AI-based grading so that feedback is immediate, consistent, and robust to varied student input. Support is grounded in sound pedagogical principles, including active learning, growth mindset, and managing cognitive load, while guiding students toward understanding rather than simply supplying answers. As students work, the system generates per-assignment summaries of class progress for faculty and keeps an auditable record of all submissions, while faculty retain full authority to override any grade. Our theory of change is that this tool will increase student engagement, provide equitable personalized support, and improve success in gateway courses, while establishing a model for faculty/IET collaboration that could extend across the university.
Funded at $4,420 toward a vendor VR simulation library license, this project establishes a psychologically safe, repeatable virtual reality remediation pathway to help at-risk nursing students master critical clinical competencies and improve retention.
This project will pilot an immersive virtual reality remediation pathway for students who are at risk for failing nursing competency exams. Students will complete targeted virtual scenarios, receive structured feedback and faculty-led debriefing, and have opportunities to demonstrate improvement through reassessment. The model is intended to expand access to consistent, psychologically safe practice while reducing barriers related to laboratory scheduling, faculty availability, supplies, and one-time simulation events. This will provide at-risk students with a targeted resource and aids in remediation, thereby impacting retention in difficult programs, such as Nursing. The project team will evaluate changes in student performance, confidence, usability, remediation completion, course progression, and retention. In addition to informing future use in Nursing, the pilot will produce an implementation guide and evaluation tools that can be adapted for experiential learning in Kinesiology, Counseling, Medical Ethics, and other disciplines. Results will be shared with the campus community to support thoughtful, evidence-informed adoption of immersive learning technologies.
This project explores and evaluates how wearable AI technologies, specifically Meta AI smart glasses, may impact teaching, learning, and assessment at the university level. As AI smart glasses become increasingly popular among students and consumers, faculty must gain a better understanding of their capabilities before these devices become more prevalent in classrooms. The pilot will evaluate features such as voice-based AI assistance, real-time information access, audio/video capture, accessibility support, and communication tools, while also examining concerns related to privacy, distraction, academic integrity, and exam security. Findings will be used to develop practical guidance for faculty, including classroom recommendations, sample syllabus language, and professional development workshops. By proactively studying wearable AI now, the University faculty and staff can thoughtfully prepare for the growing trend of this technology, and will enable them to make informed decisions that balance innovation, student support, and responsible teaching practices.
Munday Library and Instructional Technology in partnership with TLTR propose SEU Labs, a secure "sandbox" for faculty to deploy "vibe-coded" applications (tools built using AI coding assistants like Claude Code or Cursor). Currently, faculty-developed projects—such as a Chatbot Creator designed to bypass Gemini’s 10-file limit for course materials—exist as "shadow IT" on personal accounts. SEU Labs provides a shared space, financial support, and a knowledge-sharing community as a new home for these projects: we help manage development, collaboration, and hosting accounts for the pilot phase while assisting Labs participants with basics including security and FERPA reviews. IET provides infrastructure and helps facilitate conversations, while faculty lead innovative projects. Projects are structured from inception to manage user expectations around stability, maintenance, lifecycle planning, etc.. The purpose of the pilot is to explore what development, support, and long-term hosting requirements look like, including project retirement or migration.
Find out more and get started by sharing your idea here.
This Fall 2026 pilot establishes a highly controlled research cohort across Freshman Seminars and/or other introductory courses to develop and test pedagogical strategies for student AI literacy. Moving away from enterprise tool adoption, this project uses BoodleBox or other official university AI tools (Gemini, Gemini Notebook, or Rumi) as a temporary classroom utility to study how first-year students interact with generative models. Faculty will implement mandatory pre- and post-pilot measures to quantify literacy growth, focusing heavily on assignment designs that utilize prompt-tracking features to teach critical evaluation. The ultimate outcome will be a shared campus repository of assignment rubrics, lesson plans, and a pre/post assessment instrument designed to bridge the digital divide for students lacking premium AI access, providing a blueprint for AI pedagogy across St. Edward's.