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Qinyi Liu

Ph.D. Student
University of Bergen
qinyi.liu@uib.no or qinyi876@outlook.com


About Me

Hi! I’m Qinyi Liu. I am a PhD Candidate in the Centre for the Science of Learning & Technology (SLATE) at Univerisity of Bergen, working with Prof. Mohammad Khalil. During my PhD period, I also visited the University of South Australia and Monash University as a visiting scholar funded by Meltzerfondet.

I have an interdisciplinary background in computer science and social science, holding degrees in both fields. I pursued my master’s studies at the University of Edinburgh and the University of Liverpool. Previously, I received my Bachelor’s degree from Chongqing University. During my time at Edinburgh, I was part of the Covid-19 Response Governance Mapping Project. I also worked at the University of North Carolina at Chapel Hill with Prof Joseph Tucker, where I contributed to website development and research dissemination.

Research Interests

My research interests are mainly in privacy-enhancing technology (PET) + learning analytics, aiming to utilizing state-of-arts privacy-enhancing technology to advance trustworthy AI for education, with a focus on develop and fine-tune domain specific PET for application.

🚀 If you see our research intersect, feel free to reach out — I am always open to discussing potential collaborations.

Publications

Peer-Reviewed Conference Papers

  1. Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
    Q Liu, M Khalil, N Goel
    The Web Conference (WWW) Main Papers Track
    Code Accepted · 23% acceptance rate · CORE-A · CCF-A
  2. Quality Degradation Attack in Synthetic Data
    Q Liu, D Liu, F Vadiee, M Khalil, and P Barrios
    International Conference on Data Science and AI for Social Good and Responsible Innovation (DASGRI-2026), Springer
    PDF Slides Accepted
  3. Measuring the Impact of Student Gaming Behaviors on Learner Modeling
    Q Liu, L Li, V Švábenský, C Borchers, M Khalil
    LAK26: 16th International Learning Analytics and Knowledge Conference (2026)
    arXiv Code Slides 🏆 Best Full Paper Award (Top 2%) 28% acceptance rate · CORE-A
  4. Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning
    C Borchers, A Gurung, Q Liu, D Thomas, M Khalil, K Koedinger
    LAK26: 16th International Learning Analytics and Knowledge Conference (2026)
    arXiv Code 28% acceptance rate · CORE-A
  5. Advancing privacy in learning analytics using differential privacy
    Q Liu, M Khalil, R Shakya, J Jovanovic
    LAK25: 15th International Learning Analytics and Knowledge Conference (2025)
    DOI Slides Accepted · 29% acceptance rate · Best Paper Honorable Mention · CORE-A
  6. Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms
    Q Liu, O Deho, F Vadiee, M Khalil, S Joksimovic, G Siemens
    LAK25: 15th International Learning Analytics and Knowledge Conference (2025)
    DOI Slides Code 29% acceptance rate · CORE-A
  7. Towards Privacy-Preserving Data-Driven Education: the Potential of Federated Learning
    M Khalil, R Shakya, Q Liu
    International Conference on New Trends In Computing Sciences (ICTCS), IEEE Xplore (2025)
    DOI
  8. Creating artificial students that never existed: Leveraging large language models and CTGANs for synthetic data generation
    M Khalil, F Vadiee, R Shakya, Q Liu
    LAK25: 15th International Learning Analytics and Knowledge Conference (2025)
    DOI 29% acceptance rate · CORE-A
  9. Scaling While Privacy Preserving: a Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics
    Q Liu, M Khalil, R Shakya, J Jovanovic
    LAK24: 14th International Learning Analytics and Knowledge Conference (2024)
    DOI Slides Code 30% acceptance rate · CORE-A
  10. Explainable AI in Learning Analytics: Improving Predictive Models and Advancing Transparency and Trust
    Q Liu, M Khalil
    2024 IEEE Global Engineering Education Conference (EDUCON), pp. 1-7 (2024)
    DOI

Journal Articles

  1. A systematic review on student engagement in undergraduate mathematics: Conceptualization, measurement, and learning outcomes
    E Papageorgiou, J Wong, Q Liu, M Khalil, A Cabo
    Educational Psychology Review (2025)
    DOI IF=10.1 · SSCI
  2. Ensuring privacy through synthetic data generation in education
    Q Liu, R Shakya, J Jovanovic, M Khalil, J de la Hoz-Ruiz
    British Journal of Educational Technology, 00, 1–21 (2025)
    DOI IF=6.7 · SSCI
  3. Learning analytics for enhanced professional capital development: a systematic review
    J de La Hoz-Ruiz, M Khalil, J.D. Segovia, Q Liu
    Frontiers in Psychology, 15 (2024)
    DOI IF=3.8 · SSCI
  4. Understanding privacy and data protection issues in learning analytics using a systematic review
    Q Liu, M Khalil
    British Journal of Educational Technology, 54, 1715–1747 (2023)
    DOI IF=6.7 · SSCI

Book Chapters & Contributions to Edited Volumes

  1. AI for data generation in education: Towards learning and teaching support at scale [Editorial]
    M Khalil, Q Liu, J Jovanovic
    British Journal of Educational Technology (2025)
    DOI IF=6.7 · SSCI
  2. How to plan and manage a blended learning course module using generative artificial intelligence?
    M Khalil, R Shakya, Q Liu, M Ebner
    Case Studies on Blended Learning in Higher Education (pp. 53–72). Springer (2024)
    DOI

Open Source Projects

  1. Awesome Synthetic Data Application Education
    Q Liu
    Curated list of resources and papers for synthetic data application in education
    GitHub
  2. Generative AI for Synthetic Data in Learning Analytics – LAK 2026 Workshop
    Qinyi Liu et al. (with Mohammad Khalil et al.)
    Hands-on workshop materials, slides, datasets, and lab resources for the LAK 2026 workshop on synthetic data generation and privacy-presing learning analytics
    GitHub Workshop Page

Teaching

Teaching

Mentoring

Funding & Awards

Funding & Awards

Funding

Awards

Services

Guest Editor

Organization Committee Member

Conference Reviewers

Journal Reviewers


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