Research Projects

Evidence-based learning innovation across XR, AI, and analytics.

Case studies showing how research design, instructional design, and evaluation work together.

A learner exploring an immersive AI-powered virtual reality interface
Immersive Learning · AI · XR

Studying how immersive technologies and AI-supported instruction reshape how learners experience knowledge.

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.

Immersive Learning SystemsAI-Enhanced EducationLearning AnalyticsEquity and Participation
David Awoyemi at his desk, with screens showing a Learning Analytics Dashboard and AI in Education research
Core Roles

Appointments that shaped the project portfolio.

These roles provide context for the design and research work described below.

2023 - Present

Research Assistant
The University of Alabama, Tuscaloosa, AL, United States

01/2016 - 01/2022

Research and Instructional Design Assistant
Federal University of Technology Minna, Nigeria

10/2016-12/2022

Educator and Classroom Instructor
Model Secondary School, Federal University of Technology, Minna

1/2015 - 1/2016

Mathematics Educator
Abarikpo Community Secondary school, Ahoada, Rivers Solved administrative problems for the school which resulted in over 100% improvement in the results of the students. Engaged and sustained students in an effective...

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

XR LearningVirtual RealityEngineering EducationSafety TrainingLearning 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

AI LiteracyCurriculum DesignHigher EducationWorkforce ReadinessEquity

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

Eye-TrackingPhysiological ComputingElementary STEMEvaluationMixed Methods

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.

04
Arts-Integrated GenAI Literacy

Arts-Integrated GenAI Literacy Development Program for Pre-Service Teachers

University of Alabama · Music and theater modules on Blackboard LMS · Mixed-methods online study

Generative AITeacher EducationArts IntegrationAI LiteracyOnline Learning Design

Problem Context

Pre-service teachers are entering classrooms where generative AI is reshaping how learners create, evaluate, and share knowledge, yet few have a structured opportunity to develop GenAI literacy through their own creative practice before they teach.

Stakeholders and Context

  • Pre-service teachers across content areas
  • Teacher educators preparing future classroom innovators
  • Arts and STEM faculty designing integrated learning experiences

Frameworks

  • Arts-integration pedagogy for transferable creative competencies
  • GenAI literacy framework spanning prompting, evaluation, and ethics
  • Backward design for outcome-aligned online professional development

Methods and Tools

  • Two-module Blackboard course (four lessons each in Music and Theater)
  • Mixed-methods evaluation: pre-/post-lesson assessments, usability and engagement surveys
  • Reflective discussion-board submissions of learner-produced digital art
  • Optional follow-up interviews and consent-based participation

Design Contribution

The program engages pre-service teachers in hands-on creative work with GenAI tools, SUNO AI, Boomy, Animake, Invideo AI, Synthesis AI, Soundtrap, across four music and four theater lessons. Each lesson scaffolds prompt engineering, AI feedback evaluation, and AI ethics reasoning alongside the artistic output: songs, soundtracks, animated performances, digital choreography, and dramatic storytelling.

Outcomes

  • Designed and deployed a complete online professional-development module set with structured pre/post measures, reflective tasks, and discussion-board sharing.
  • Built evidence connecting arts-integrated GenAI engagement with growth in computational thinking, prompt engineering, problem-solving, AI feedback evaluation, AI ethics literacy, creativity, and collaboration.
  • Ran a participant cohort April 22, June 20, 2025 with completion incentives and a multi-instrument data set ready for thematic and statistical analysis.

Critical Reflection

Arts integration turns GenAI literacy into something pre-service teachers DO rather than something they read about, and that change of stance is what makes the competencies transferable to their own future classrooms.