Hui Yang

STEM & CS Researcher, SRI Education

Hui Yang, PhD, is an education researcher specializing in learning sciences and learning technologies, with a focus on STEM and computer science (CS) education. Her work explores how teachers integrate emerging technologies with content and pedagogy to foster meaningful student engagement and improved learning outcomes. Yang is especially interested in how technology‐rich learning environments inspire creativity and spark students’ interest in STEM and CS fields. Her recent research centers on advancing artificial intelligence (AI) literacy in K–12 by leveraging the synergy between computational thinking and AI. She aims to support the preparation of AI-literate educators and students for the increasingly technology‐driven future.

At SRI, Yang brings extensive expertise spanning classroom-level innovations to system-level evaluations that support learning for all students. She contributes to a broad portfolio of design, research, and evaluation projects across CS, integrated STEM, mathematics education, and state, national, and international evaluation programs. Grounded in contemporary learning sciences research, her work draws on rigorous mixed-methods approaches to generate actionable insights that inform teaching practices and our understanding of student learning behaviors. Yang’s varied skill set includes developing formative assessments aligned with standards (e.g., the Computer Science Teachers Association K–12 Standards, the Next Generation Science Standards), applying learning analytics to examine student engagement, collaboration, and outcomes, and designing tools and environments for formal and informal learning contexts. Yang also supports technical assistance projects that bridge research and practice, providing educators and policymakers with data-driven strategies to improve teaching and learning.

Before joining SRI, Yang was a postdoctoral researcher in the Department of Information Science and at the Future of Learning Lab at Cornell University. She earned her PhD in learning sciences from the University of Delaware. She also holds an MEd in educational technology, a BSE in elementary education, and a BS in educational information technology. Yang has teacher certifications in multiple subject areas.

Key projects

  • CIGALE: Collaboration for Innovations: Designing Generative AI-embedded Agents for Supporting Teachers’ AI Literacy Integration in K-12
  • EMPIRES: Assessing the Efficacy and Implementation of a Technology-based Mathematics Intervention for Middle School Students
  • ASSIST-MSCS: Developing A Suite of Standards-based Instructionally Supportive Tools for Middle School Computer Science (https://assistcs.org/)
  • CoolThink@Jockey Club: Evaluation of a pilot CS program in Hong Kong
  • Collaborative Research: RETTL: Story Studio: Coaching Data Storytelling at Scale

Recent publications

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Selected Publications

  • Yang, H., Rachmatullah, A., Alozie, N., & Cao, Q. (2025, March). From AI-Literate Teacher to AI-Literate Student: Where to Start?. In Society for Information Technology & Teacher Education International Conference (pp. 3232-3236). Association for the Advancement of Computing in Education (AACE).
  • Basu, S., Yang, H., Rachmatullah, A., Tate, C., & Rutstein, D. (2025, March). Using Formative Assessments to Examine Student Understanding of Middle School Algorithms and Programming Concepts. In Society for Information Technology & Teacher Education International Conference (pp. 1826-1835). Association for the Advancement of Computing in Education (AACE).
  • Tate, C., Basu, S., Rachmatullah, A., Yang, H., & Rutstein, D. (2025, February). Implementing Standards-Focused Professional Development for Middle School CS Teachers: An Experience Report. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (pp. 1113-1119).
  • Rachmatullah, A., Alozie, N., & Yang, H. (2024). Mapping out the structural relationship of middle school students’ use of talk and gestures and group outcomes’ quality in collaborative science problem-solving activities. International Journal of Science Education, 46(14), 1432-1479.
  • Yu, R., Yang, H., Lin, X., Yao, C., Burkander, P., Thomas, K., & Mislevy, J. (2024, July). Technology-Based Instructional Strategies Show Promise in Improving Self-Regulated Learning Skills at Broad-Access Postsecondary Institutions. In Proceedings of the Eleventh ACM Conference on Learning@ Scale (pp. 408-411).
  • Global Benchmarking of Computational Thinking Education in Primary Schools.

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