REU Site: Human-Technology Interfaces: Artificial Intelligence Applications for Digital Product Design
- Institution
- Morgan State University, MD
- Principal investigator
- Kayenda T Johnson
- Funding
- $372K
- Duration
- 2026–2030
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Abstract
This Research Experiences for Undergraduates (REU) Site at Morgan State University provides a 10-week, summer immersive research program focused on exploring how artificial intelligence (AI) can enhance human-centered digital product design. As AI becomes increasingly embedded in everyday technologies, there is a growing need to understand how humans and intelligent systems can effectively collaborate to create user-friendly, accessible, and efficient digital experiences. This project addresses that need by engaging undergraduate students in hands-on research at the intersection of AI and human-computer interaction, while strengthening participation in STEM by building a nationwide cohort of students from varied academic and institutional backgrounds. By developing technical, research, and professional skills, the program contributes to the preparation of a skilled STEM workforce and advances the national interest in innovation and technological competitiveness. The REU program trains students to conduct interdisciplinary research at the intersection of AI, human-computer interaction (HCI), and human-centered design (HCD). The project focuses on four integrated research themes: (1) AI tools for qualitative data analysis, (2) AI tools for conceptual design, (3) AI-enhanced usability testing, and (4) transparency and trust in AI-supported design. Participants will use mixed methods such as surveys, thematic coding, usability evaluation, and analysis of interaction data to examine human-AI collaboration in design workflows. Projects include evaluating AI-assisted qualitative analysis, generating and assessing AI-based personas and interface designs, identifying bias and gaps in AI-generated outputs, analyzing user interaction patterns with AI-supported insights, and examining how explainability influences trust in AI-generated recommendations. Through structured mentoring and progressively independent research, students will produce research reports, presentations, and dissemination materials that contribute to advancing knowledge on effective, responsible, and inclusive human-AI collaboration in digital product design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Kayenda T JohnsonPI
- Vishnu Kumar
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