Projects

Principal Investigator: Ryan K. Boettger, Department of Technical Communication.

This project develops a transparent, faculty-led workflow for creating ethical GenAI-enhanced learning resources in TECM 2700: Technical Writing. Drawing on existing course materials and instructor/student input, our team will build a curated corpus and use it to generate draft textbook chapters and a course-specific chatbot focused on high-impact job-searching and professional branding tasks (resumes, LinkedIn profiles and employment outlook reports). Using design-based research, we will iteratively validate AI drafts with subject-matter experts, then test usability with instructors and students — including scenario-based studies in our eye-tracking lab — to refine accuracy, clarity and bias safeguards. Outcomes include a customized textbook, a supportive chatbot and a replicable model other programs can adapt to responsibly integrate AI into curriculum development.

Principal Investigator: Deborah Cockerham, Department of Learning Technologies.

Principal Investigator: Gayatri Mehta, Department of Electrical Engineering.

Students with disabilities are natural problem solvers who navigate a world built for non-disabled people and often use technology in their everyday lives without realizing the importance of semiconductor chips, the fundamental blocks of the technology. Students with disabilities are significantly less likely to pursue post-secondary education than those with no disability, and employment rates among people with disabilities are significantly lower than those of non-disabled people. In this pilot project, we propose to develop an interactive learning framework driven by the needs of students with disabilities to make semiconductor chip design broadly accessible. Incorporating this unique perspective enhances the STEM field and engineering solutions with broader accessibility. Our interdisciplinary team will introduce fundamental concepts from semiconductor chip design at a level that requires no prior engineering background. We will present real-world, complex problems in relevant and meaningful contexts to connect the importance of engineering solutions for problems related to social science, to spark interest in STEM pathways, and help students realize how their contributions can benefit society. 

Principal Investigator: Vanessa Macias, Department of Biological Sciences.

The formal study of insects, entomology, has long enabled scientists to pursue their fascination with the insect world and find work in public, government and academic sectors.  Modern entomology is transforming due to advancements in molecular biology and AI. However, pedagogy in entomology has not kept pace with advancing technologies, leaving the field without interested students and capable experts.  For this reason, we have designed a research internship and a set of course modules to innovate the learning environment for entomology at UNT to bolster hands-on, research-based exploration of the application of emerging computational technologies to local insect problems. We are integrating a course unit on insect identification using AI and will choose one student to participate in a summer internship to partner with the ongoing West Nile Surveillance efforts to develop a machine-learning platform to identify local mosquitoes. We expect this preliminary implementation to support the establishment of a cohesive entomology track at UNT that will contribute highly capable and competitive graduates to an important field.

Principal Investigator: Ji Hyun Yu, Department of Learning Technologies.

As generative AI becomes a common tool for students, it risks acting as a "crutch" that encourages uncritical cognitive offloading. This interdisciplinary project bridges Learning Sciences and Data Science to transform AI into a true cognitive partner. We are deploying MIRA (Metacognitive Intelligence for Regulated Analytics), a custom AI agent, within a data science course. MIRA doesn't just provide answers; it enforces self-regulated learning through intentional friction points. These include a "Planning Gate," requiring students to explain their logic before receiving code, and a "Veracity Gate," where students must diagnose intentionally flawed AI outputs. Additionally, MIRA features a "Cognitive Mirror", a real-time dashboard reflecting the student's learning behaviors. By capturing detailed interaction data to train a custom BERT classifier, this pilot will deliver a scalable "metacognitive nudge" tool designed to foster productive AI co-regulation and deeper learning in higher education.

Principal Investigator: Mai Zaru, Division of Student Affairs.

First-generation college students arrive to campus with many strengths: resilience, multilingual skills, and tight-knit community ties, but often without the professional networks that open doors to internships, mentors, and career opportunities. Networked Futures Lab is testing a different approach, pairing hands-on network mapping with AI-supported practice, so students can rehearse introductions, informational interviews, and follow-up messages in a low-stakes setting before trying them for real.

Over a 15-month pilot with first-generation undergraduates at UNT, the project asks whether professional networking, often treated as an innate trait some students simply have, can instead be taught explicitly and deliberately. Early design centers not just confidence but a repeatable skill: knowing how to find, build, and sustain the connections that shape careers, regardless of who you know when you start.

Principal Investigator: Regina Kaplan-Rakowski, Department of Learning Technologies.

This project examines how different AI interaction modalities — text-based, voice-based, and embodied chatbots — shape foreign language learners’ cognitive, emotional, and physiological responses during learning interactions. The project integrates wearable biometric sensing, personality measures, engagement and motivation scales, conversation data, and interviews to identify patterns of attention, distraction, arousal, anxiety, and engagement across AI interaction modalities. We capture EEG-based indicators of focus and relaxation, as well as recordings of heart rate and electrodermal activity associated with physiological arousal. The study also examines how individual differences, including personality traits and affective tendencies, relate to responses to different AI interfaces. By combining learning technologies, second-language acquisition, human-AI interaction, and biometric assessment, the project aims to inform the design and educational integration of AI systems that are more responsive to learners’ cognitive and emotional needs.

Principal Investigator: Max Sherard, Department of Learning Sciences.

Over the past decade, education researchers and urban planners have begun collaborating to conceptualize public spaces as informal learning environments — socially and spatially vibrant places where people learn by participating in everyday tasks, such as shopping, socializing, protesting, and working. However, most of these collaborations focus on public places in city, such as parks, community centers, and markets, rather than public routes through the city — the sidewalks, bus routes, and train lines that connect various places.

In this project, we bring together learning sciences, transit planning, and community psychology to conceptualize public transit as an informal learning environment and investigate how transit-dependent people — individuals who rely on paratransit, buses, and trains — reason about and navigate the urban landscape using buses and trains. Using grounded theory methodology, we will survey and interview people who rely on Dallas Area Rapid Transit (DART) to better understand their experiences, reasoning practices for spatial navigation, and sense of community. This study aims to: (a) situate theories of spatial cognition in the daily routines of public transit riders; and (b) compliment quantitative approaches to evaluating bus routes (often used by transit planners) with rich, qualitative data.

Principal Investigator: Ting Xiao, Department of Data Science.