Projects
AI-Assisted Computing Education
Developing AI-assisted personalized learning in project-based programming education for K-12 students, and exploring social science students motivation and coding performance in higher education.
Sub-Project 1: AI-Assisted Personalized Learning in Project-Based Programming Education
Developing AI-assisted personalized learning in project-based programming education for K-12 students.
Collaborator:
- Zexin Xu, University of Texas at Dallas
Sub-Project 2: Vibe Coding Research
Exploring social science students' motivation and coding performance in higher education, quasi-experiment.
Collaborator:
- Zhiru Sun, University of Southern Denmark
LLM-Assisted Learning Analytics
Validating LLM-based diagnosis of students emotion and engagement in science inquiry learning, and associating students learning trajectories with engagement survey and performance.
Collaborators:
- Li Cheng, University of North Texas
- Sudeshana Paramita Ghose (PhD student), University of North Texas
- Srinikesh Mucha (Master student), University of North Texas
Conference Proceeding:
- He, J., Jin, B., Xie, K., & Zhang, D. (2025). Diagnose Academic Emotions from Facial Expressions: Relationship with Science Learning Performance in Web-Based Self-directed Learning. ISLS 2025.
AI-Facilitated Feedback
Designing and evaluating AI-facilitated feedback systems using Feedback Triangle Theory to enhance teacher implementation and student learning outcomes in higher education settings.
Collaborators:
- Kui Xie, University of Missouri
- Zilu Jiang, Johns Hopkins University
- Tingting Li, Washington State University
Related Publications:
- He, J., & Xie, K. (2026). Who wrote this? College students' perceptions and evaluations of human and AI-facilitated feedback. Educational Psychology.
- He, J., Li, T., Xu, Z., & Xie, K. (2025). Leveraging Generative AI in Designing and Delivering Individualized Responsive Feedback for Pre-service Teachers in Higher Education. Book Chapter.
- He, J., Jiang, Z., & Sun, Z. (2026). Linguistic Cues in Instructor and AI Feedback: Linking Language to Student Motivation. ISLS 2026 (Short Paper).
Web-Based Science Inquiry Learning
Exploring students motivation and engagement process in web-based science inquiry learning, and the role of instructional feedback on students learning.
Platform: WISE
Collaborators:
- Danhui Zhang, Beijing Normal University
- Bihui Jin, Beijing Normal University
- Yue Liu, Beijing Normal University
Published Papers:
- He, J., Liu, Y., Zhao, W., Xie, K., & Zhang, D. (2026). The Impact of Social-Affective Support Feedback on Students' Science Inquiry Learning. Learning and Motivation.
- He, J., Liu, Y., Ran, T., & Zhang, D. (2022). How students' perception of feedback influences self-regulated learning: the mediating role of self-efficacy and goal orientation. European Journal of Psychology of Education.
- He, J., Jin, B., Xu, Z., & Zhang, D. (2022). Measuring elementary students' behavioral engagement in web-based science inquiry learning. Journal of Online Learning Research.