2023 - Present
Research Assistant
The University of Alabama, Tuscaloosa, AL, United States
Evidence-based learning innovation across XR, AI, and analytics.
This page keeps the stronger case-study structure from the earlier website while connecting it to the same shared automated workflow.
Research Agenda
Project work organized as case-based evidence.
These studies show how David approaches design, evaluation, and scholarly contribution across immersive learning, AI literacy, and multimodal analytics.
Core Roles
Appointments that shaped the project portfolio.
These roles come directly from the CV and provide context for the design and research work described below.
01/2016 - 01/2022
Research and Instructional Design Assistant
Federal University of Technology Minna, Nigeria
01/2025 – 05/2025
Teaching Assistant
The University of Alabama, Tuscaloosa, AL, United States
10/2016-12/2022
Educator and Classroom Instructor
Model Secondary School, Federal University of Technology, Minna
Selected Case Studies
Three projects that best represent the research story.
The content below preserves the narrative richness of the previous site while using the new shared design system and deploy workflow.
01
Immersive STEM Learning
AI-Enhanced XR Learning for STEM Education: VR Hazard Identification in Civil Engineering
University of Alabama · Civil engineering safety training · Mixed-methods design and analytics
Problem Context
Construction safety instruction often depends on low-immersion demonstrations that make hazard recognition difficult to practice authentically before students enter high-risk settings.
Stakeholders and Context
- Civil engineering students and instructors
- Lab researchers designing VR learning tasks
- Programs preparing students for safety-critical work
Frameworks
- Multimedia learning and immersive cognition principles
- Design-based research for iterative refinement
- Performance-centered assessment for hazard identification
Methods and Tools
- VR simulation design
- Behavioral analytics
- Microgenetic analysis
- Bayesian cognitive diagnostics
Design Contribution
The project uses generative-AI-supported virtual environments and structured hazard-identification tasks to study how learners notice risks, shift strategies, and develop safer decision patterns in authentic contexts.
Outcomes
- Produced publishable work on immersive technology-enhanced learning and safety training.
- Strengthened a research program connecting XR design with measurable performance evidence.
Critical Reflection
This case anchors a broader research direction: immersive environments become more valuable when they are not only engaging, but also analytically transparent enough to reveal how expertise develops over time.
02
AI Literacy Curriculum
AI-WISE: Industry-Informed Artificial Intelligence Literacy Curriculum for Higher Education
University of Alabama · Undergraduate AI literacy design · Cross-disciplinary curriculum development
Problem Context
Students increasingly encounter AI tools in academic and professional settings, yet many programs still lack structured, ethical, and workforce-relevant AI literacy experiences.
Stakeholders and Context
- Undergraduate students across disciplines
- Faculty embedding AI in coursework
- Industry partners shaping competency expectations
Frameworks
- Backward design for outcome-aligned curriculum planning
- Universal Design for Learning for multimodal access
- Culturally responsive pedagogy for inclusive participation
Methods and Tools
- Industry competency mapping
- Co-design workshops
- Pilot curriculum implementation
- Feedback and thematic analysis
Design Contribution
AI-WISE organizes practical AI skills, ethical reasoning, and reflective evaluation into modular learning experiences that faculty can adapt without needing deep technical specialization.
Outcomes
- Created a reusable curriculum model for broader AI literacy integration.
- Supported conference dissemination around practical and equitable AI education design.
Critical Reflection
The strongest lesson here is that AI literacy is not only about tools. It is about helping learners interpret, question, and apply AI systems responsibly in disciplinary and civic contexts.
03
Physiological Learning Analytics
Beyond Self-Report: Using Eye-Tracking and Physiological Data to Evaluate Learning in Computing Environments
NSF ITEST context · Elementary computing experiences · Multimodal evaluation and broadening participation
Problem Context
Traditional evaluation methods often miss moment-to-moment evidence of attention, cognitive load, and engagement, especially with younger learners who may not fully articulate what they experienced.
Stakeholders and Context
- Elementary learners in computing activities
- Researchers collecting multimodal evidence
- Programs focused on equitable STEM pathways
Frameworks
- Mixed-methods evaluation design
- Cognitive load theory
- Learning analytics for multimodal evidence integration
Methods and Tools
- Eye-tracking and pupil-dilation analysis
- Physiological sensing
- Cross-case comparison
- Quantitative and qualitative synthesis
Design Contribution
This work integrates physiological and behavioral data with more familiar assessments to create a richer view of how learners experience computing tasks and where instructional support is needed.
Outcomes
- Contributed to NSF-supported research on computing participation and learner experience.
- Extended evaluation practice beyond self-report toward more responsive evidence models.
Critical Reflection
The project reinforced an important design belief: evaluation should not be an afterthought. It should be built into how we understand learning as it unfolds, especially in novel technology environments.
Next Layer
Research case studies now live inside the automated site.
The case narratives remain curated, while the linked academic record on the profile page continues to regenerate from the Word CV.