18 UW Tacoma researchers receive SEED-AI grants from AI@UW inaugural funding call

Project team:
Menaka Abraham, Teaching Professor, School of Engineering & Technology, UW Tacoma
Andrea Hill, Teaching Professor, School of Social Work & Criminal Justice, UW Tacoma
Lisa Hoffman, Professor, School of Urban Studies, UW Tacoma
Julia Eaton, Associate Professor, School of Interdisciplinary Arts & Sciences, UW Tacoma
Sean Schmidt, Executive Administrator, Student Planning and Administration, UW Tacoma

Project summary:
Our project performs the urgent task of building AI literacy among UWT’s largely first-generation, transfer, and working student population as they enter an AI-saturated job market and world. Our innovative approach consists of two elements: I) A “bookended” AI literacy learning experience consisting of a broader “this is the AI world” 2-credit seminar for new students, and deeper 2-credit senior capstone in which seniors bring their accumulated disciplinary expertise to examine AI in their professional contexts, futures, and beyond, and II) A host of modifiable “plug and play” assignments and activities for faculty from any discipline to use without significant investment in AI development. Importantly, this approach will enhance AI literacy over the course of the UWT experience by focusing on our student population’s lived experiences. We envision that our campus becomes a model for AI literacy education serving non-technical student populations, aligning with our mission to serve the region.

Project team:
Sarah J. Iribarren, Associate Professor, Biobehavioral Nursing and Health Informatics, School of Nursing
Andrea Hartzler, Professor, Biomedical Informatics and Medical Education, School of Medicine
Michael Leu, Professor, Pediatrics and Biomedical Informatics and Medical Education, School of Medicine
Weichao Yuwen, Associate Professor, School of Nursing & Healthcare Leadership, UW Tacoma 
Jan Flowers, Sr. Research Scientist, Biobehavioral Nursing and Health Informatics, School of Nursing
Emily Schildt, Clinical Informatics Fellow, Biomedical Informatics and Medical Education, School of Medicine
Sikha Pentyala, Postdoctoral Researcher, UW Tacoma School of Engineering and Technology
Kelly Schorling Brewer, Assistant Teaching Professor, Biobehavioral Nursing and Health Informatics, School of Nursing
Jennifer Sprecher, Project Manager, Biobehavioral Nursing and Health Informatics, School of Nursing
Patricia Reid Ponte, Associate Affiliate Clinical Professor, Biobehavioral Nursing and Health Informatics, School of Nursing
Jared Erwin, Lecturer, School of Medicine, Department of Biomedical Informatics and Medical Education

Project summary:
Artificial intelligence (AI) is increasingly embedded in healthcare systems, but most healthcare professionals have received little formal training in its responsible clinical use. This project will develop short, modular learning resources (10–15 minutes each) to prepare healthcare professionals in the UW Clinical Informatics and Patient-Centered Technology (CIPCT) graduate program to understand, evaluate, and ethically apply AI-based tools in clinical settings. The modules will be developed with input from learners, clinical informatics trainees, faculty and clinical leaders to ensure clinical relevance and alignment with real-world practice. An AI-supported tutor will complement the modules by allowing learners to engage in interactive, scenario-based practice focused on ethical decision-making and clinical judgement. Project success will be evaluated through brief knowledge assessments, learner feedback and usability evaluations. While designed for CIPCT, these resources will be readily adaptable for other healthcare programs and clinical partner settings.

Project team:
Rita (Duong) Than, Associate Teaching Professor, Sciences and Mathematics, School of Interdisciplinary Arts & Sciences, UW Tacoma

Project summary:
Introductory mathematics courses often act as critical gatekeepers to student success. This project scales a proven “Targeted Mastery” pedagogical model in TMATH 109—where preliminary research established that engagement with remedial retakes is a statistically significant predictor of final exam performance (p=0.018). By implementing the AI-Driven Bridge System through interactive web-based platforms, we are developing automated tools to generate context-aware adaptive practice, track student performance trajectories and provide Socratic tutoring. This approach removes the logistical barriers to individualized remediation, transforming early mistakes into formative learning paths. The project provides a scalable, evidence-based framework designed to improve retention and pass rates in gateway quantitative reasoning courses.

Project team:
Belinda Y. Louie, Professor, School of Education, UW Tacoma
Karlyn Davis-Welton, Instructor, School of Education, UW Tacoma
Janelle Franco, Instructor, School of Education, UW Tacoma
Jamie Lee, Instructor, School of Education, UW Tacoma
Elin Björling, Research Scientist, Department of Human-Centered Design & Engineering, UW Seattle

Project summary:
Project TELL-AI is transforming how the University of Washington Tacoma prepares teachers for multilingual classrooms. While AI is rapidly changing education, many educators feel hesitant about its ethical use. Our project brings together four faculty members to systematically redesign ten courses in the Washington state English Language Learner endorsement licensing program. Using a structured iterative Design Thinking approach, we will move from identifying teacher needs to testing AI-enhanced lessons in actual classrooms. We aren’t building complex software; instead, we are creating a practical “blueprint” that shows teachers how to use AI as a collaborator for lesson planning and personalized student support. By modernizing this curriculum, we ensure that UWT graduates are ready to help K-12 students in high-need districts overcome language barriers. Ultimately, TELL-AI provides a scalable model for how any university department can move from individual experimentation to a unified, responsible program for the future of learning.

Project team:
Chun Wang, Professor, College of Education
Min Li, Professor, College of Education
Yulia Tsvetkov, Associate Professor, Paul G. Allen School of Computer Science & Engineering, College of Engineering
David Arthur, Assistant Professor, Department of Sciences and Mathematics, UW Tacoma

Project summary:
Each year, UW enrolls thousands of students in introductory courses delivered in large lecture formats. This standardized instructional approach, often implemented without adequate staffing or resources, is associated with failure rates of 30%–50%. Failure in these gateway courses can disrupt academic trajectories, leading students to switch majors or extend time to degree completion. Instructors of these courses often face the daunting task of generating, administering and scoring assessments to meet their teaching goals. This project will develop a “diagnostic assessment copilot” that supports instructors in rapidly creating high-quality assessment tasks aligned to course learning goals. The tool will be tested by faculty members from both Seattle and Tacoma in introductory statistics classes. By lowering barriers to high-quality assessment at scale, this project directly advances UW’s teaching mission and lays the groundwork for longer-term improvements in student success.

Project team:
Lorne Arnold, Assistant Professor, Civil Engineering, School of Engineering & Technology, UW Tacoma
Chris Marriott, Teaching Professor, Computer Science and Systems, School of Engineering & Technology, UW Tacoma
Cassandra Donatelli, Assistant Professor, Mechanical Engineering, School of Engineering & Technology, UW Tacoma

Project summary:
AI tools offer an opportunity to reduce non-essential struggles for students learning to code (like fixing syntax errors), freeing students to focus on deeper learning. But they also pose a challenge to learning because typical AI agents are designed to increase user productivity, not necessarily user understanding. Drawing on Polya’s four-step problem-solving framework (understand, plan, execute, look back) and recent research on cognitive engagement techniques, we will develop an AI teaching assistant that guides students through structured inquiry and problem decomposition rather than providing direct solutions. We will deploy this system across civil/mechanical engineering and computer science courses, comparing learning outcomes to baseline conditions. Our goal is to help students develop genuine problem-solving skills while still benefiting from AI assistance with syntax and implementation details.

Project team:
David Arthur, Assistant Professor, School of Interdisciplinary Arts & Sciences, Department of Sciences and Mathematics, UW Tacoma
Zaher Kmail, Associate Professor, School of Interdisciplinary Arts & Sciences, Department of Sciences and Mathematics, UW Tacoma

Project summary:
Project- and problem-based learning activities have been shown to improve learning outcomes for students. In introductory statistics courses, such activities require students to analyze real-life datasets. To achieve the best outcomes, each student would be provided with a personalized dataset that aligns with their interests and goals while still facilitating progress towards specific learning objectives. In practice, finding real datasets can be difficult and creating high-quality, realistic synthetic datasets can be time-consuming. This project proposes the use of Large Language Models (LLMs) to realize the ideal of personalized datasets for classroom learning. We propose using LLMs to create AI agents that perform the tasks required to write code that can be used to produce realistic, synthetic datasets. This tool will be tested and validated by both instructors of, and students in, an introductory statistics course with the ultimate goal of improving engagement and learning outcomes for students.

Project team:
Christopher R. Beasley, Associate Professor, Department of Social Sciences, School of Interdisciplinary Arts & Sciences, UW Tacoma

Project summary:
Faculty resources for teaching with generative AI typically present isolated use cases without showing how applications connect into a coherent workflow. This project addresses that gap by developing a comprehensive faculty AI workflow guide drawn from three years of iterative development across synchronous and asynchronous courses. The guide will document how generative AI integrates with broader digital tools to create a teaching production pipeline grounded in backward design and constructive alignment. It will include visual workflow diagrams, tested prompt templates, decision frameworks for when AI adds value and strategies for maintaining instructor voice and pedagogical intentionality. Rather than disconnected tips, faculty will gain a framework where AI-assisted outputs build on one another from course design through feedback — capturing the efficiency and coherence that emerge when AI integration becomes a workflow rather than an add-on. A small pilot group of faculty from diverse disciplines will validate transferability before broader dissemination to UW faculty and the public.