Postdoctoral Associate · University of Miami

Hi, I'm Zifeng (Lauren) Liu, Ph.D.

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I study how artificial intelligence, learning analytics, and educational data mining can support equitable and effective learning.
About My Research Contact Me

About

Portrait of Zifeng Liu

I am a Postdoctoral Associate at the University of Miami. I earned my Ph.D. in Curriculum and Instruction, specializing in Educational Technology, from the University of Florida. I also hold a master's degree in Computer Software and Theory from Beijing Normal University and a bachelor's degree in Computer Science and Technology from Beijing Technology and Business University.

My research sits at the intersection of educational data mining, learning analytics, artificial intelligence in education, and computer science education. I welcome research collaborations and am working toward a tenure-track faculty career where I can integrate rigorous scholarship, inclusive teaching, and student mentorship. Outside of research, I enjoy traveling, music, hiking, and catching up on well-deserved sleep.

Current Position

University of Miami · Miami, Florida

Postdoctoral Associate

2026–Present

I conduct interdisciplinary research on AI in education, learning analytics, educational data mining, and computer science education, with an emphasis on translating evidence into more effective and equitable learning experiences.

Projects

AI4VS project
AI4VS

AI Across the Curriculum for Virtual Schools.

Accomplishments
  • Interactive learning activities introduce artificial intelligence concepts across subject areas in virtual-school settings.
M-flow programming environment
M-flow

A flow-based programming platform designed for elementary students.

Accomplishments
  • Zifeng Liu, Shan Zhang, Maya Israel, Robert Smith, Wanli Xing, and Victor Minces*. 2025. Engaging K-12 Students with Flow-Based Music Program- ming: An Experience Report on Its Impact on Teaching and Learning. In Proceedings of the 56th ACM Technical Symposium on Computer Science Edu- cation V. 1 (SIGCSE TS 2025), February 26-March 1, 2025, Pittsburgh, PA, USA. ACM, New York, NY, USA, 7 pages. DOI
LogicDS data science learning environment
LogicDS

An integrated data science foundations course built on mathematical logic.

Accomplishments
  • Liu, Z., Monteith, B., Chao, J., & others. (2024, August 30). Using entropy analysis to explore student engagement in an online high school data science course. TechRxiv. DOI
AI fairness research project
AI Fairness

A project dedicated to ensuring equitable and unbiased outcomes in online learning systems.

Accomplishments
  • Liu, Z., Jiao, X., Li, C., & Xing, W. (2024, July). Fair Prediction of Students' Summative Performance Changes Using Online Learning Behavior Data. Proceedings of the 17th International Conference on Educational Data Mining, 686–691. DOI
  • Liu, Z., Xing, W., & Li, C. (2024, July). Explainable analysis of AI-generated responses in online learning discussions. In Educational Data Mining 2024 Workshop: Leveraging Large Language Models for Next-Generation Educational Technologies. DOI
Generative AI in education project
Generative AI in Education

A project applying Generative AI to support online K-12 STEAM education.

Accomplishments
  • Zifeng Liu, Xinyue Jiao, Wanli Xing, and Wangda Zhu. 2025. Detecting AI-Generated Pseudocode in High School Online Pro- gramming Courses Using an Explainable Approach. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE TS 2025), February 26-March 1, 2025, Pittsburgh, PA, USA. ACM, New York, NY, USA, 7 pages. DOI.
  • Song, Y., Kim, J., Liu, Z., Li, C., & Xing, W. (2024). Students' Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Class. arXiv preprint arXiv:2409.06721.
eSPAC3 Minecraft learning environment
eSPAC3

A Minecraft learning environment for upper-elementary students to strengthen spatial computational thinking skills.

Accomplishments
  • The Minecraft-based environment uses visual programming challenges to develop spatial computational thinking.

Publications

Journal Papers

  • Liu, Z., Xing, W., Jiang, Y., Li, C., Kim, T., & Li, H. (2025). Leveraging contrastive learning to improve group and individual fairness in predictive analytics for online learning. Journal of Computing in Higher Education. DOI
  • Liu, Z., Xing, W., Jiao, X., & Li, C. (2025). Exploring fairness and explainability in LLM-generated support for online learning discussion forums. Journal of Learning Analytics, 1–26. DOI
  • Liu, Z., Xing, W., Li, C., Zhang, F., Li, H., & Minces, V. (2025). Exploring automated assessment of primary students’ creativity in a flow-based music programming environment. Journal of Learning Analytics, 12(2), 83–104. DOI
  • Liu, Z., Xing, W., Jiao, X., & Li, C. (2025). What are the differences between student and ChatGPT-generated pseudocode? Detecting AI-generated pseudocode in high school programming using explainable machine learning. Education and Information Technologies. DOI
  • Liu, Z., Xing, W., Ngo, B., Jiao, X., et al. (2025). Engagement patterns of middle school students with AI teachable agents in mathematics learning. Scientific Reports, 15, 40971. DOI
  • Liu, Z., Cheon, S., Stanbury, A., Jiao, X., Xing, W., & Kang, H. (2026). Towards contextual-based AI: A scoping review of artificial intelligence in X reality for personalized learning. Computers and Education: Artificial Intelligence, 10, 100523. DOI

Refereed Conference Papers

  • Liu, Z., Li, H., Chao, J., & Xing, W. (2026). Brains vs. Algorithms? How experts and students see AI-generated distractors. AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-26). [≈30% acceptance rate]
  • Liu, Z., Li, L., Chao, J., Mondol, A., Xing, W., & Zhang, Y. (2026). Do all roads lead to AI literacy? Clustering behavioral patterns and examining outcomes in an online AI literacy module for secondary school students. Proceedings of LAK ’26, ACM. [≈30% acceptance rate]
  • Liu, Z., Xing, W., & Fang, Z. (2026). Talking the Talk: Linking instructional discourse patterns to student in-video dropout and learning outcome. Proceedings of LAK ’26, ACM. [≈30% acceptance rate]
  • Liu, Z., Jiang, Y., & Xing, W. (2026). Exploring the use of LLMs for assessing creativity in student programming artifacts. Proceedings of SIGCSE TS 2026, ACM.
  • Liu, Z., Ganapathy Prasad, P., Ngo, B., Jiao, X., & Xing, W. (2025). A human–AI collaborative assessment of AI-generated vs. human-created MCQ distractors. Proceedings of ICCE 2025, Chennai, India.
  • Liu, Z., Song, Y., Yang, Q., Xing, W., & Guo, J. (2025). Exploring the Impact of a Simulation-Based Learning Tool on Undergraduate Quantum Computing Education. ASEE Annual Conference & Exposition 2025.
  • Liu, Z., Zhang, S., Israel, M., Smith, R., Xing, W., & Minces, V. (2025). A NSF ITEST Program: Integrating Music and Flow-Based Programming Builds Teachers’ Confidence in Computer Science. ASEE Annual Conference & Exposition 2025.
  • Liu, Z., Monteith, B., Chao, J., Wiedemann, K., Fofang, J. B., Li, L., Ma, D., Mohamed, R., Mondol, A., Jo, Y., Fleetwood, A., Lipien, L., Zhang, Y., & Xing, W. (2025). Using entropy analysis to explore student engagement in an online high school data science course. DSE-K12 Conference 2025, San Antonio, TX.
  • Liu, Z., Guo, R., Song, Y., & Xing, W. (2024). WIP: Understanding students’ in-video dropout behavior in a large online math learning platform. 2024 IEEE Frontiers in Education (FIE 2024), Washington, D.C.
  • Liu, Z., Jiao, X., Li, C., & Xing, W. (2024). Fair prediction of students’ summative performance changes using online learning behavior data. Proceedings of EDM 2024, 686–691.
  • Liu, Z., Xing, W., & Li, C. (2024). Explainable analysis of AI-generated responses in online learning discussions. EDM 2024 Workshop on LLMs in Education. DOI
  • Liu, Z., Guo, R., Jiao, X., Gao, X., Oh, H., & Xing, W. (2024). How AI assisted K-12 computer science education: A systematic review. ASEE 2024 Annual Conference & Exposition.

Patent

  • Cai, S., Liu, Z., & Zhang, Y. (2023). A grid-based self-attention facial expression recognition method using supervised contrastive learning. (Patent pending).
  • Cai, S., Liu, Z., Changhao Liu, & Haitao Zhou. (2021). A non-invasive brain-computer interface-based attention feedback method (Patent No. ZL 2021 1 1283053.5).

Education

University of Florida logo

University of Florida (2023–2026)

Gainesville, Florida, USA

Degree: Ph.D. in Curriculum and Instruction
Specialization: Educational Technology
Advisor: Dr. Wanli Xing
Lab: Advanced and Inclusive Computing for Education (AICE) Lab

    Honors/Awards:

    • Vernice Law Hearn Scholarship (2025–2026): $2,000

Beijing Normal University logo

BNU (2020–2023)

Beijing, China

Degree: Master of Computer Software and Theories
Advisor: Dr. Su Cai
Lab: VR/AR + Education Lab

    Honors/Awards:

    • First Prize of Excellent Graduate Student Scholarship (2020–2021, 2021–2022)
    • Outstanding Freshman Scholarship (2020)
    • Excellent Individual of Summer Volunteer Teaching Program of BNU (2022)

Beijing Technology and Business University logo

BTBU (2016–2020)

Beijing, China

Degree: Bachelor of Computer Science and Technology
Advisor: Dr. Yi Chen and Dr. Zhongming Han
Lab: Beijing Key Laboratory of Big Data Technology for Food Safety

    Honors/Awards:

    • Excellent Graduate of Beijing (2020, top 5%)
    • National Scholarship of China (2018-2019, top 0.2%)
    • Headmaster Scholarship of BTBU (2018-2019, top 0.3%)
    • Outstanding Student Scholarship of BTBU (2018, 2019)
    • Student Leadership Award of BTBU (2017-2018)
    • National Scholarship for Encouragement of China (2016-2017, 2017-2018)

Contact

liuzifeng@ufl.edu

github.com/ZifengLiu98

linkedin.com/in/zifeng-lauren-liu