Kexin 'Bella' Yang
Kexin Yang

My research designs data-driven human-AI systems for future smart classrooms, leveraging Multimodal Learning Analytics (MMLA) to:

  1. respect students’ and teachers’ boundaries, agency, and preferences,
  2. help educators focus limited attention where it’s most needed,
  3. support personalized, self-paced learning tailored to students’ needs.
I am broadly interested in Human-AI interaction, LLMs, XR, robotics, and social computing, especially their applications in education. I bring expertise in qualitative, quantitative, and human-centered methods, including A/B testing, interviews, surveys, prototyping, participatory design, think-aloud protocols, field testing, log-data analysis, and statistical modeling. My first-authored work has been published at top venues such as CHI, CSCW, AIED, EDM, and EC-TEL. I am currently on the job market and excited to explore research opportunities in academia and industry.

Publications

AIED Tool Design and Development

Computer-supported Collaborative Learning

Human-AI Interaction

Data Mining, Visualization and Simulation

LLM/ Natural Language Processing

Social Computing and Crowdsourcing

Multi-modal Learning Analytics