Zifeng (Lauren) Liu, Ph.D. | Postdoctoral Associate

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

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
                            AI Across the Curriculum for Virtual Schools.
                          
AccomplishmentsInteractive learning activities introduce artificial intelligence concepts across subject areas in virtual-school settings.

M-flow
                  A flow-based programming platform designed for elementary students.
                
AccomplishmentsZifeng 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. https://doi.org/10.1145/3641554.3701902

LogicDS
                  An integrated data science foundations course built on mathematical logic.
                
AccomplishmentsLiu, 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. https://doi.org/10.36227/techrxiv.172503593.33731605/v1

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. https://doi.org/10.5281/zenodo.12729918
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. https://doi.org/10.13140/RG.2.2.24309.38881

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. https://doi.org/10.1145/3641554.3701942.
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

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. https://doi.org/10.1007/s12528-025-09468-y
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. https://doi.org/10.18608/jla.2025.8885
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. https://doi.org/10.18608/jla.2025.8835
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. https://doi.org/10.1007/s10639-025-13385-z
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. https://doi.org/10.1038/s41598-025-24841-8
Liu, Z., Cheon, S., Stanbury, A., Jiao, X., Xing, W., & Kang, H. (accepted, in press). Towards contextual-based AI: A scoping review of artificial intelligence in X reality for personalized learning. Computers and Education: Artificial Intelligence.
Xing, W., Liu, Z., Song, Y., & Kim, T. (2025). Why do students leave instructional videos: understanding students’ in-video dropout behavior in a large online math learning platform? Distance Education. https://doi.org/10.1080/01587919.2025.2579792
Song, Y., Kim, J., Liu, Z., Li, C., & Xing, W. (2025). Students’ perceived roles, opportunities, and challenges of a generative AI-powered teachable agent: a case of middle school math class. Journal of Research on Technology in Education, 1–19. https://doi.org/10.1080/15391523.2024.2447727
Xing, W., Song, Y., Li, C., Liu, Z., Zhu, W., & Oh, H. (2025). Development of a generative AI-powered teachable agent for middle school mathematics learning: a design-based research study. British Journal of Educational Technology, 2043–2077. https://doi.org/10.1111/bjet.13586
Song, Y., Kim, J., Xing, W., Liu, Z., Li, C., & Oh, H. (2025). Elementary school students’ and teachers’ perceptions toward creative mathematical writing with Generative AI. Journal of Research on Technology in Education, 1–23. https://doi.org/10.1080/15391523.2025.2455057
Xing, W., Fang, Z., Zhang, H., Kamiyama, T., Liu, Z., & Kim, T. (2025). Making the ‘mathematics register’ accessible to students: an exploratory study of two teachers’ discourse in an online lesson on polynomial expressions. Language and Education, 1–24. https://doi.org/10.1080/09500782.2025.2542861
Li, H., Xing, W., Zhu, W., Zhang, S., & Liu, Z. (2025). Should educational AI models include gender attribute? Explaining the why based on environmental psychology course with gender imbalance. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-025-09467-z
Zhu, W., Xing, W., Kim, E. M., Li, C., Wang, Y., Yang, Y., & Liu, Z. (2025). Integrating image-generative AI into conceptual design in computer-aided design education: Exploring student perceptions, prompt behaviors, and artifact creativity. Educational Technology & Society, 28(3), 166–183. https://doi.org/10.30191/ETS.202507_28(3).SP11
Cai, S., Liu, Z., Liu, C., & others. (2022). Effects of a BCI-based AR inquiring tool on primary students’ science learning: A quasi-experimental field study. Journal of Science Education and Technology, 31, 767–782. https://doi.org/10.1007/s10956-022-09991-y
Liu, E., Cai, S., Liu, Z., & Liu, C. (2023). WebART: Web-based augmented reality learning resources authoring tool and its user experience study among teachers. IEEE Transactions on Learning Technologies, 16(1), 53–65. https://doi.org/10.1109/TLT.2022.3214854

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. https://doi.org/10.13140/RG.2.2.24309.38881
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.
Kim, T., Liu, Z., Xing, W., Li, H., & Oh, H. (2025). Emotional dynamics in asynchronous math discussions: The impact of negative emotions. ICLS 2025, Finland. https://repository.isls.org/handle/1/11805 [≈30% acceptance rate]
Oh, H., Liu, Z., & Xing, W. (2025). Do actions speak louder than words? Unveiling linguistic patterns in online learning communities using cross-recurrence quantification analysis. Proceedings of LAK ’25, ACM, 992–998. https://doi.org/10.1145/3706468.3706569 [≈30% acceptance rate]
Li, H., Xing, W., Li, C., Zhu, W., Lyu, B., Zhang, F., & Liu, Z. (2025). Who Should Be My Tutor? Interactive effects of chatbot personality styles between middle school students and a mathematics chatbot. Proceedings of LAK ’25. https://doi.org/10.1145/3706468.3706537 [≈30% acceptance rate]
Li, H., Xing, W., Zhu, W., Li, C., Lyu, B., Liu, Z., & Heffernan, N. (accepted). Leveraging multi-modality and collaborative filtering for supporting automatic scoring in mathematics education. Proceedings of AIED 2025. https://doi.org/10.1007/978-3-031-99264-3_39 [≈30% acceptance rate]
Li, H., Xing, W., Lyu, B., Zhu, W., Liu, Z., & Li, H. (accepted). An automated aesthetic assessment framework of mathematical story images validated by click counts. Proceedings of ACM Learning@Scale (L@S) 2025. https://doi.org/10.1145/3698205.3733923 [≈30% acceptance rate]
Jiao, X., Huang, H., Liu, Z., Cai, S., & Fan, Z. (2025). Beyond the screen: Enhancing augmented reality collaborative inquiry with social scripts. ICALT 2025, IEEE. [Best Full Paper Award]
Jiao, X., Liu, Z., & Cai, S. (2020). Impact of embedded cognitive scaffolding of augmented reality technology on elementary school students' science learning. ICCE 2020. [Best Paper Nomination]

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 (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
                  
                    Relevant Coursework:
                    
Quantitative Foundations of Educational Research
Quantitative Research Methods in Education
Current Issues and Trends in Computer Science Education
Emerging Technologies in Learning and Education
Human-Computer Interaction (HCI) and the Learner
Designing Integrated Media Environments

                    Honors/Awards:
                  
Vernice Law Hearn Scholarship (2025-2026): $2000

                   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)

                   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