Research
Completing my MS in Human-Centered Computing at the University of Nebraska Omaha, exploring how humans and AI systems can work together more effectively.
Current Thesis Focus
The Effects of AI-Driven Adaptive Scaffolding vs. Static Worked Examples on Cognitive Load and Skill Acquisition
I'm studying whether AI systems that adapt their level of assistance based on user performance (scaffolding) are more effective at reducing cognitive load and improving skill acquisition compared to static, pre-prepared examples. This research directly applies to product design—should AI systems adapt their help levels dynamically, or provide consistent static guidance?
Broader Research Interests
Areas I'm exploring as part of my academic work or future research directions.
Cognitive Load in Human-AI Systems
Adaptive Learning Interfaces
Trust & Mental Model Alignment
AI Code Quality & Developer Efficacy
Human Factors in Technology Adoption
Participate in Research
Your participation is anonymous and helps advance knowledge in human-computer interaction and learning sciences.
A controlled study of how AI-driven adaptive scaffolding affects learning efficiency and cognitive load, comparing dynamic scaffolding against a fixed faded schedule on Python for loop tasks. The protocol, instruments and multi-agent scaffolding system are built and the study is awaiting Institutional Review Board approval before any participant data is collected.
Design: Between-subjects, two conditions
Status: Awaiting IRB approval — not yet recruiting
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- All responses are anonymous
- Participation is completely voluntary
- You may withdraw at any time
- Data used for academic research only
- No personally identifying information collected
Questions about this research? Contact me.