Name & Title
Qinyi Liu, Assistant Professor
Contact
E-mail Address: qyliu@cityu.edu.mo
Google Scholar, ResearchGate, ORCID
Academic Qualification
Ph.D., Educational Data Science, University of Bergen, Norway April 2026)
M.Sc., Computer Science, University of Liverpool, United Kingdom
M.Sc., Social Research, University of Edinburgh, United Kingdom
B.A., Japanese, Chongqing University, China
Previous positions
Visiting Researcher, Centre for Learning Analytics (Monash University) and Centre for Change and Complexity in Learning (University of South Australia), 2024
Data Analyst, CN Open Data Inc., 2022
Research Coordinator, University of North Carolina Chapel Hill, Social Entrepreneur-ship to Spur Health Project, 2021–2022
Researcher, University of Edinburgh & Edinburgh Innovations, COVID Governance and Data Protection Project, 2021
Teaching
Co-instructor, IGSIN919V Introduction to Practical Machine Learning, University of Bergen, 2026
Co-instructor, Generative AI for Synthetic Data: Applications in Machine Learning, Norwegian Artificial Intelligence Research Consortium Summer School, 2025
Co-instructor, IGSIN919V Introduction to Practical Machine Learning, University of Bergen, 2025
Guest Lecturer, INFO900 Introduction to Learning Analytics, University of Learning Analytics, Sept – Dec 2024
Co-instructor, IGSIN919V Introduction to Practical Machine Learning, University of Bergen, 2024
Co-instructor, IGSIN919 Introduction to Practical Machine Learning, University of Bergen, 2023
Research Interest
AI for education: learning analytics, knowledge tracing, adaptive learning system
Privacy-enhancing techniques: synthetic data generation, differential privacy, machine unlearning, federated learning, zero-knowledge proof
Trust and robust AI: fairness and explainability in AI, Adversarial machine learning, uncertainty in machine learning
Research & Publication
Peer-Reviewed Conference Papers
Urmian, S., Liu, Q., Khalil, M. Decoupled Learning and Selection in Slate Rec-ommendation for Privacy and Stability Under Noisy Scores. In: ACM Conference on Recommender Systems (RecSys) Main Track (Accepted). [18% acceptance rate][CORE-A] [CCF-B]
Liu, Q., Khalil, M., Goel, N. Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN. In: The ACM Web Conference (WWW) Main Research Track (2026). [23% acceptance rate] [CORE-A] [CCF-A] doi.org/10.1145/3774904.3792961
Liu, Q., Li, L.,Švábenský, V., Borchers, C., Khalil, M. Measuring the Impact of Student Gaming Behaviors on Learner Modeling. In: LAK26: 16th International Learning Analytics and Knowledge Conference (2026). [28% acceptance rate] [CORE-A][Best Paper Award] doi.org/10.1145/3785022.3785036
Borchers, C., Gurung, A., Liu, Q.,., Thomas, D., Khalil, M., & Koedinger.,K. Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning. In: LAK26: 16th International Learning Analytics and Knowledge Conference (2026). [28% acceptance rate] [CORE-A] doi.org/10.1145/3785022.378504
Khalil, M., Shakya, R., Liu, Q.. TowardsPrivacy-PreservingData-DrivenEducation:the Potential of Federated Learning. International Conference on New Trends In Computing Sciences (ICTCS), IEEE Xplore (2025). Link
Khalil, M., Vadiee, F., Shakya, R., & Liu, Q. Creating artificial students that never existed: Leveraging large language models and CTGANs for synthetic data generation. In: LAK25: 15th International Learning Analytics and Knowledge Conference (2025). [29% acceptance rate] [CORE-A] doi.org/10.1145/3636555.3636921
Liu, Q., Khalil, M., Shakya, R., & Jovanovic, J. Advancing privacy in learning analytics using differential privacy. In: LAK25: 15th International Learning Analytics and Knowledge Conference (2025). [29% acceptance rate] [Best Paper Honorable Mention] [CORE-A] doi.org/10.1145/3706468.3706493
Liu, Q., Deho, O., Vadiee, F., Khalil, M., Joksimovic, S., & Siemens, G. Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms. In: LAK25: 15th International Learning Analytics and Knowledge Conference (2025). [29% acceptance rate] [CORE-A] doi.org/10.1145/3706468.3706546
Liu, Q., & Khalil, M. Explainable AI in Learning Analytics: Improving Predictive Models and Advancing Transparency and Trust. In: 2024 IEEE Global Engineering Education Conference (EDUCON), pp. 1–7 (2024).doi.org/10.1109/EDUCON60312.2024.10578733
Liu, Q., Khalil, M., Shakya, R., & Jovanovic, J. Scaling While Privacy Preserving: a Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics. In: LAK24: 14th International Learning Analytics and Knowledge Conference (2024). [30% acceptance rate] [CORE-A] doi.org/10.1145/3636555.3636921
Journal Articles
Papageorigou, E., Wong, J., Liu, Q., Khalil, M., & Cabo, A. A systematic review on student engagement in undergraduate mathematics: Conceptualization, measurement, and learning outcomes. Educational Psychology Review (2025). [IF=10.1][SSCI] doi.org/10.1111/bjet.13576
Liu, Q., Shakya, R., Jovanovic, J., Khalil, M., & de la Hoz-Ruiz, J. Ensuring privacy through synthetic data generation in education. British Journal of Educational Technology, 00, 1–21 (2025). [IF=6.7] [SSCI] doi.org/10.1111/bjet.13576
Javier, Khalil, M., Jesús Domingo Segovia, and Liu, Q. Learning analytics for enhanced professional capital development: a systematic review. Frontiers in Psychology, 15 (2024). [IF=3.8] [SSCI] doi.org/10.3389/fpsyg.2024.1302658
Liu, Q., & Khalil, M. Understanding privacy and data protection issues in learning analytics using a systematic review. British Journal of Educational Technology, 54, 1715–1747 (2023). [IF=6.7] [SSCI] doi.org/10.1111/bjet.13388
Book Chapters & Contributions to Edited Volumes
Khalil, M., Liu, Q., & Jovanovic, J. AI for data generation in education: Towards learning and teaching support at scale [Editorial]. British Journal of Educational Technology (2025). [IF=6.7] [SSCI]doi.org/10.1111/bjet.13580
Khalil, M., Shakya, R., Liu, Q., & Ebner, M. How to plan and manage a blended learning course module using generative artificial intelligence? In Case Studies on Blended Learning in Higher Education (pp. 53–72). Springer (2024). doi.org/10.1007/978-981-97-9388-44
Others
Funding
Co-Principal Investigator, Norwegian Artificial Intelligence Research Consortium Summer School AI Course Grant (NOK 50,000), 2024
Co-Principal Investigator, University of Bergen’s Institutional Funding for Synthetic Data (NOK 500,000), 2024
Meltzers Høyskolefond Grant (NOK 48,692), 2024
Meltzers Høyskolefond Grant (NOK 23,691), 2023
Travel Award, Norwegian Conference on Artificial Intelligence Research Consortium (NOK 1000), 2023
Awards
Best Full Paper Award, International Conference on Learning Analytics and Knowledge (LAK’26) , 2026
Doctoral Consortium Fellowship, International Conference on Learning Analytics and Knowledge (LAK’24), Kyoto, Japan (USD 2,000), 2024
Best Poster Award, International Conference on Learning Analytics and Knowledge (LAK’24), Kyoto, Japan, 2024
Post-CHI Usable Privacy and Security Travel Award (USD 2500), 2023
Best Poster Award, International Conference on Learning Analytics and Knowledge (LAK’23), Texas, USA, 2023
Selected Posters & Talks
1. Vadiee, F., Liu, Q., & Khalil, M. ORCHID-RANKER: An Agentic Adaptive RecommenderforEducationwithPrivacyGuarantees. Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing (SAC), pp. 116–117 (2026). Link
2. Liu, Q. Using Synthetic Data for Scalable Privacy Preserving in Learning Analytics. Centre for Change and Complexity in Learning (University of South Australia) (2024,November).
3. Liu, Q., & Khalil, M. Exploring the Generation of Synthetic Educational Tabular Data using LLMs [Workshop paper]. In 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’24), AI for Education (AI4EDU): Advancing Personalized Education with LLM and Adaptive Learning Workshop, Barcelona,Spain (2024, June). [CORE-A]
4. Liu, Q. Privacy and Data Protection for a Trustworthy Learning Analytics in Higher Education [Poster presentation]. The 14th International Learning Analytics and Knowledge Conference, Kyoto, Japan (2023, March). [Best poster award]
5. Liu, Q., Mestre, A., & Khalil, M. Perspectives of Multimodal Data Sharing and Privacy in VR Learning Rooms [Poster presentation]. The 13th International Learning Analytics and Knowledge Conference, Arlington, Texas, USA (2023, March 13–17). [Best poster award]
Services
Guest Editor
British Journal of Educational Technology, Special Issues on AI for Data Generation in Education: Towards Learning and Teaching Support at Scale, 2024
Organization Committee Member
Society for Learning Analytics Research Graduate Student Special Interest Group, 2024
Symposium: Towards a Fairer Future of Education: Algorithmic Inequality and Learning Analytics, 2023
Journal Reviewer (26 international journals)
AI & Security:
IEEE Transactions on Knowledge and Data Engineering (TKDE); IEEE Transactions on Information Forensics and Security (TIFS);
Computers & Security; Artificial Intelligence Review; Neurocomputing;
Journal of Big Data; Journal of System Architecture; PeerJ Computer Science;
Computers in Biology and Medicine; Array; Archives of Computational Methods in Engineering;
Universal Access in the Information Society; AI and Ethics; Scientific Data;
Knowledge-Based System; Discover Informatics; Discover Computing; SoftwareX;
Education Technology:
Computers & Education; International Journal of Artificial Intelligence in Education (IJAIED);
British Journal of Educational Technology (BJET); Computers & Education: Artificial Intelligence;
Journal of Learning Analytics (JLA); Frontiers in Education; International Journal of Emerging Technologies in Learning (iJET).
Conference Reviewer
2024–2026: Learning Analytics and Knowledge Conference (LAK)
2025–2026: International Conference on Artificial Intelligence in Education (AIED)
2025–2026: ACM International Conference on Multimodal Interaction (ICMI)
Mindtrek Conference
NeurIPS Workshop on Generative AI for Education