Publications

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INVITE Exclusive

• Jaldi, C. D., Ilkou, E., Schroeder, N., & Shimizu, C. (2024). Education in the era of Neurosymbolic AI. Journal of Web Semantics, 100857.
• 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. 
• 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.
• 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. 
• Barrett, J. (2023). Thinking Critically: Classroom Activities to Examine Ethics in Computing. In ACM EngageCSEdu. ACM, New York, NY, USA.
• 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.
• 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.
• 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., 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. 
• ChengXiang Zhai, 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, Dec. 15-18, 2024, pp. 8820-8824.
• Zhang, S., Meshram, P. S., Ganapathy Prasad, P., Israel, M., & Bhat, S. (2025, February). 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).
• Hum, S., Gadbury, M., Ginger, J., Duda, K., & Lane, H. C. (2025, July). BarrelBots: ChatGPT Feedback for Middle School Student Creative Minecraft Artifacts. In International Conference on Artificial Intelligence in Education. Cham: Springer Nature Switzerland.
• 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.
• Earle-Randell, T. V., Zhang, S., Schroeder, N., Boyer, K. E., Dorley, E. (2025, July). How Virtual Agents Can Shape Human-Human Collaboration: A Systematic Review. In International Conference on Artificial Intelligence in Education. Cham: Springer Nature Switzerland.

Works Partially Supported by INVITE

• 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.
• Schroeder, N. L., Davis, R. O., & Yang, E. (2024). Designing and Learning With Pedagogical Agents: An Umbrella Review. Journal of Educational Computing Research, 07356331241288476.  
• 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.
• 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.
• Sparks, J. R., Lehman, B., & Zapata-Rivera, D. (2024). Caring assessments: Challenges and opportunities. Frontiers in Education – Assessment, Testing, and Applied Measurement, 9:1216481.
• 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.
• 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.
• 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.
• 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).
• Zeng, Z., and Bhat, S. (2023). Unified Representation for Non-compositional and Compositional Expression. In Findings of the Association for Computational Linguistics: EMNLP 2023.
• 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.
• 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.
• 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.
• 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]
• 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.
• 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. 

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.