HUMAN With AI — Exploring Anthropomorphic Interfaces
Research Insights: Effects of AI Anthropomorphism on Learning Experience
- Demonstrated that AI interface types affect immersion, cognitive load, and satisfaction differently depending on learner proficiency, providing empirical evidence for adaptive AI learning design.
- Identified a clear trade-off in human-like AI interfaces — offering higher immersion and satisfaction but also increasing psychological burden, especially for low-proficiency learners.
- Revealed that simplified, low-burden interfaces are more effective for beginner learners, providing actionable UX insights for personalized AI education platforms.
Role
UX Researcher / Product Designer
Timeline
May – Aug 2024 (3 Months)
Team
1 Researcher
Tools
Figma · Illustrator · Photoshop · SPSS / Excel
Contribution
Led research and design on AI-driven conversation prototypes, focusing on user immersion, cognitive load, and learning satisfaction across different levels of anthropomorphism.
- Designed and tested four AI interface types (text, animal, avatar, human-like).
- Conducted quantitative + qualitative analysis with 100 learners.
- Created adaptive UI prototypes reflecting level-based insights.
Overview
This research investigated how different levels of anthropomorphism in AI learning interfaces — from plain text to human-like avatars — shape immersion, cognitive load, and learner satisfaction. The findings inform adaptive UX strategies for AI education platforms.
Visual Research
Full Paper
The complete research paper includes methodology, statistical analysis, and design implications across all four AI interface conditions.
View Full Research Paper ↗