Publications

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INVITE-supported publications

2025

• ChengXiang Zhai. (2025). Information Retrieval for Artificial General Intelligence: A New Perspective of Information Retrieval Research. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25). Association for Computing Machinery, New York, NY, USA, 3876–3886.
• Dean E. Alvarez and ChengXiang Zhai. (2025). TINK: Text Information Navigation Kit. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25). Association for Computing Machinery, New York, NY, USA, 4056–4060.
• Earle-Randell, T. V., Zhang, S., Schroeder, N., Boyer, K. E., Dorley, E. (2025). How Virtual Agents Can Shape Human-Human Collaboration: A Systematic Review. In International Conference on Artificial Intelligence in Education. Cham: Springer Nature Switzerland.
• Eric Modesitt, Ke Yang, Spencer Hulsey, Xin Liu, ChengXiang Zhai, and Volodymyr Kindratenko. (2025). ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study. In Findings of the Association for Computational Linguistics: ACL 2025, pages 907–926, Vienna, Austria. Association for Computational Linguistics.
• Gladstone, J. R., Schroeder, N. L., Heidig, S., Zhang, S., Palaguachi, C., Pitera, M. (2025). Do Pedagogical Agents Enhance Student Motivation? Unraveling the Evidence Through Meta-Analysis. Educational Psychology Review.
• Hou, X., Forsyth, C., Andrews-Todd, J., Rice, J., Cai, Z., Jiang, Y., Zapata-Rivera l, D., and Graesser, A. (2025). An LLM-enhanced Multi-Agent Architecture for Conversation-Based Assessment. International Conference of Artificial Intelligence in Education. AIED 2025. pp 119-134.
• Hum, S., Gadbury, M., Duda, K., Ginger, J., Lane, H. C., & Nadler, D. (2025). BarrelBots: Measuring Self-efficacy and Puzzle-based Computational Thinking in Minecraft. In Proceedings of the 19th International Conference of the Learning Sciences-ICLS 2025. International Society of the Learning Sciences.
• Hum, S., Gadbury, M., Ginger, J., Duda, K., & Lane, H. C. (2025). BarrelBots: ChatGPT Feedback for Middle School Student Creative Minecraft Artifacts. In International Conference on Artificial Intelligence in Education. Cham: Springer Nature Switzerland.
• Kabir, A., Tankala, C., & Lowd, D. (2025). On the Practicality of Differential Privacy for Knowledge Tracing. In Educational Data Mining (EDM’25).
• Kevin Ros, Rahul Suresh, and ChengXiang Zhai. (2025). InstInfo: A Just-in-Time Literature Recommendation System for Presentations. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25). Association for Computing Machinery, New York, NY, USA, 4046–4050.
• Rajarathinam, R. J., & Kang, J. (2025). Taking the Lead: Exploring Collaborative Initiative in CSCL Contexts. In Proceedings of the 19th International Conference of the Learning Sciences–ICLS 2025. International Society of the Learning Sciences.
• Tension, C., Beigman Klebanov, B., Schroeder, N., Zhang, S., Suhan, M., & Zhang, C. (2025). Towards assessing persistence in reading in young learners using pedagogical agents. In Artificial Intelligence in Measurement and Education Conference (AIME–Con). Special Interest Group of the National Council on Measurement in Education.
• Yuji Zhang, Sha Li, Cheng Qian, Jiateng Liu, Pengfei Yu, Chi Han, Yi R. Fung, Kathleen McKeown, ChengXiang Zhai, Manling Li, and Heng Ji. (2025). The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23340–23358, Vienna, Austria. Association for Computational Linguistics.
• Zhang, S., Meshram, P. S., Ganapathy Prasad, P., Israel, M., & Bhat, S. (2025). An LLM-Based Framework for Simulating, Classifying, and Correcting Students’ Programming Knowledge with the SOLO Taxonomy. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1681-1682).
• Zhou, Y., Kang, J., & Nguyen, H. (2025). Can we trust LLM for video analysis: An exploration of hallucination in Multimodal Large Language Model. In Proceedings of the 19th International Conference of the Learning Sciences–ICLS 2025. International Society of the Learning Sciences.

2024

• Beigman Klebanov, B., Weeks, J., Sinharay, S. (2024). Predicting student engagement in interactive reading. In Proceedings of the 25th International Conference on Artificial Intelligence in Education: AIED 2024.
• Bhavya, S.S., Xiong, J., Zhai. C. (2024). AnaDE1. 0: A Novel Data Set for Benchmarking Analogy Detection and Extraction. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL).
• Bhavya, S.S., Zhou, Y., Sehgal, S., Bhat, S., Zhai, C. (2024). Analego: Let’s build analogies together! Demo presentation at the AAAI 2024 Workshop on AI for Education (AI4Ed). 2024.
• ChengXiang Zhai. (2024). LiveDataLab: A Cloud-based Open Lab for Integrating Big Data Research, Education, and Applications. In Proceedings of 2024 IEEE International Conference on Big Data, Washington DC, pp. 8820-8824.
• Forsyth, C.M., Zapata-Rivera, D., Graf, A., & Jiang, Y. (2024). Complex Conversations: LLMs vs. Knowledge Engineered Conversation-based Assessment. In The Proceedings of the International Conference on Educational Data Mining. 862-867.
• Gladstone, J. R., Tallberg, M., Boston Jaxon, J., & Cimpian, A. (2024). What makes a STEM role model motivating for young girls? The effects of the role model’s growth versus fixed mindsets about ability and interest. Journal of Experimental Child Psychology, 238, 105775.
• Hemmat, A., Davies, A., Lamb, T., Yuan, J., Torr, P., Khakzar, A., & Pinto, F. (2024). Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models. In Thirty-eighth Conference on Neural Information Processing Systems.
• Hum, S. Gadbury, M., Shipley, E., Lane, H.C., Ginger, J. (2024). Mars, Minecraft, and AI: A Deep Learning Approach to Improve Learning by Building. 25th International Conference on Artificial Intelligence in Education, Recife, Brazil, 2024.
• Jaldi, C. D., Ilkou, E., Schroeder, N., & Shimizu, C. (2024). Education in the era of Neurosymbolic AI. Journal of Web Semantics, 100857.
• Lehman, B., Sparks, J. R., Zapata-Rivera, D., Steinberg, J., & Forsyth, C. (2024). A culturally enhanced framework of caring assessments for diverse learners. Practical Assessment, Research, and Evaluation, 29(1), 9.
• Mahajan, J., Hum, S., Henhapl, J., Yunus, D., Gadbury, M,. Brown, E., Ginger, J., Lane, H.C. (2024). MineObserver 2. 0: A Deep Learning & In-Game Framework for Assessing Natural Language Descriptions of Minecraft Imagery. Association for the Advancement of Artificial Intelligence 2024 Conference, Vancouver.
• Mannekote, A., Davies, A., Pinto, J. D., Zhang, S., Olds, D., Schroeder, N. L., Lehman, B., Zapata-Rivera, D., Zhai, C. (2024). Large Language Models for Whole-Learner Support: Opportunities and Challenges. Frontiers in Artificial Intelligence, 7, 1460364.
• Ojha, V., West, L., & Lewis, C. M. (2024). Computing Self-Efficacy in Undergraduate Students: A Multi-Institutional and Intersectional Analysis. Proceedings of the 2024 ACM SIGCSE Technical Symposium on Computer Science Education.
• Schroeder, N. L., Davis, R. O., & Yang, E. (2024). Designing and Learning With Pedagogical Agents: An Umbrella Review. Journal of Educational Computing Research, 07356331241288476.
• Sparks, J. R., Lehman, B., & Zapata-Rivera, D. (2024). Caring assessments: Challenges and opportunities. Frontiers in Education – Assessment, Testing, and Applied Measurement, 9:1216481.
• Zapata-Rivera, D., Arslan, B. (2024). Learner Modeling Interpretability and Explainability in Intelligent Adaptive Systems. In: Santoianni, F., Giannini, G., Ciasullo, A. (eds) Mind, Body, and Digital Brains. Integrated Science, vol 20. Springer, Champp. 95–109.
• Zapata-Rivera, D., Forsyth, C. M., Graf, A., & Jiang, Y. (2024). Designing and Evaluating Evidence-Centered Design based Conversations for Assessment with LLMs. Proceedings of EDM 2024 Workshop: Leveraging Large Language Models for Next Generation Educational Technologies.
• Zhang, S., Earle-Randell, T. V., Shen, Q, Botelho, A. F., Israel, M., Boyer, K. E., Lynch, C. F., & Wiebe, E. (2024). Predicting and Analyzing Students’ Higher-Order Questions in Collaborative Problem-Solving. International Conference on Computers in Education. [Nominated for the Best Overall Paper Award]
• Zhang, S., Jaldi, C., Schroeder, N. L., & Gladstone, J. R. (2024). Pedagogical agents in K-12 education: A scoping review. Journal of Research on Technology in Education, 1–28.
• Zhang, S., Jaldi, C., Schroeder, N. L., López, A. A., Gladstone, J. R., & Heidig, S. (2024). Pedagogical agent design for K-12 education: A systematic review. Computers & Education. 105165.
• Zhang, S., Palaguachi, C., Pitera, M., Jaldi, C. D., Schroeder, N. L., Botelho, A. F., & Gladstone, J. R. (2024). Semi-automating the Scoping Review Process: Is it Worthwhile? A Methodological Evaluation. Educational Psychology Review, 36(4), 1-35.

2023

• Barrett, J. (2023). Thinking Critically: Classroom Activities to Examine Ethics in Computing. In ACM EngageCSEdu. ACM, New York, NY, USA.
• Zeng, Z., and Bhat, S. (2023). Unified Representation for Non-compositional and Compositional Expression. In Findings of the Association for Computational Linguistics: EMNLP 2023.
• Zeng, Z., Cheng, K., Nanniyur, S., Zhou, J., & Bhat, S. (2023). IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP).
• Zhou, J., Zeng, Z., Gong, H. & Bhat, S. (2023). Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning. In Findings of the Association for Computational Linguistics: EMNLP 2023.

Informing Literature

Some sample works that have contributed to the knowledge base INVITE is inspired by:

Learning STEM in INVITE platforms

• Fostering Balanced Contributions Among Children Through Dialogue Visualization
  Celepkolu, M., Galdo, A. C., & Boyer, K. E. (2022). IEEE Transactions on Learning Technologies.
• Triggering STEM interest with Minecraft in a Hybrid Summer Camp
  Lane, H. C., Gadbury, M., Ginger, J., Yi, S., Comins, N., Henhapl, J., & Rivera-Rogers, A. (2022). Technology, Mind, and Behavior.
• Mining for STEM Interest Behaviors in Minecraft
  Gadbury, M., & Lane, H. C. (2022). The 23th International Conference on Artificial Intelligence in Education (AIED).
• Would you? Could you? On a tablet? Analytics of Children’s eBook Reading
  Klebanov, B. B., Loukina, A., Madnani, N., Sabatini, J., & Lentini, J. (2019). The 9th International Conference on Learning Analytics & Knowledge (LAK).

Learner Modeling and Adaptive Educational Systems

• Enhancing Personalization by Integrating Top-down and Bottom-up Approaches to Learner Modeling
  Zapata-Rivera D., & Arslan, B. (2021). The 23rd HCI International Conference.
• Learner Modeling in the Context of Caring Assessments
  Zapata-Rivera, D., Lehman, B., & Sparks, J. R. (2020). The 22nd HCI International Conference.
• The Challenge of Noisy Classrooms: Speaker Detection During Elementary Students’ Collaborative Dialogue
  Ma Y, Wiggins J, Celepkolu M, Boyer K, Lynch C, Wiebe E. (2021). The 22nd International Conference on Artificial Intelligence in Education (AIED).
• Adaptive Educational Systems
  Shute, V. J., & Zapata-Rivera, D. (2012). Adaptive Technologies for Training and Education.
• Learning Intercultural Communication Skills with Virtual Humans
  Lane, H.C., Hays, M.J., Core, M.G., & Auerbach, D. (2013). Journal of Educational Psychology.
• Autonomous Agent that Provides Automated Feedback Improves Negotiation skills
  Monahan, S., Johnson, E., Lucas, G., Finch, J., & Gratch, J. (2018). The 19th International Conference on Artificial Intelligence in Education (AIED).

Learning Analytics and Educational Data Mining

• Modeling MOOC Student Behavior With Two-Layer Hidden Markov Models
  Geigle, C., & Zhai, C. (2017). The 4th Annual ACM Conference on Learning at Scale.
• A Sequential Decision Formulation of the Interface ard Model for Interactive IR
  Zhang, Y., & Zhai, C. (2016). The 39th International ACM SIGIR conference on Research and Development in Information Retrieval.
• Detecting Learning in Noisy Data: The Case of Oral Reading Fluency
  Klebanov, B. B., Loukina, A., Lockwood, J., Liceralde, V. R. T., Sabatini, J., Madnani, N., … & Lentini, J. (2020). The 10th International Conference on Learning Analytics & Knowledge.
• Exploring Emergent Features of Student Interaction within an Embodied Science Learning Simulation
  Kang, J., Lindgren, R., & Planey, J. (2018). Journal of Multimodal Technologies and Interaction.

assessment

• Assessing Collaborative Computing: Development of the Collaborative-Computing Observation Instrument (C-COI)
  Israel, M., Wherfel, Q. M., Shehab, S., Ramos, E. A., Metzger, A., & Reese, G. C. (2016). Computer Science Education.
• Culturally Responsive Evaluation
  Hood, S., Hopson, R. K., & Kirkhart, K. E. (2015). Handbook of Practical Program Evaluation.
• Hitting a High Note on Math Tests: Remembered Success Influences Test Preferences
  Finn, B., & Miele, D. B. (2016). Journal of Experimental Psychology: Learning, Memory, and Cognition.

MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

• Knowledge-Driven Data Construction for Zero-Shot Evaluation in Commonsense Question Answering
  Ma, K., Ilievski, F., Francis, J., Bisk, Y., Nyberg, E., & Oltramari, A. (2021). The 35th AAAI Conference on Artificial Intelligence (AAAI).
• Illinimet: Illinois System for Metaphor Detection with Contextual and Linguistic Information
  Gong, H., Gupta, K., Jain, A., & Bhat, S. (2020). The 2nd Workshop on Figurative Language Processing.
• Idiomatic Expression Paraphrasing without Strong Supervision
  Zhou, J., Zeng, Z., Gong, H., & Bhat, S. (2022). The 36th AAAI Conference on Artificial Intelligence (AAAI).
• On Robustness and Regularization of Structural Support Vector Machines
  Torkamani, M. A., & Lowd, D. (2014). The 31st International Conference on Machine Learning.