Publications
Anton, G., Ayalan, E., Mathayas, N., Zhou, M., Danish, J., & Enyedy, N. (2023). Computational Tinkering with Movement in Embodied Models. To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Ayalon, E., Anton, G., & Enyedy, N. (2023). “Are You a Match?”: Coordinated Embodied Activity Using Multiple Perspectives to Support Algorithmic Solutions. To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Ayalon, E., Anton, G., & Enyedy, N. (2023). “Walk, Walk, Walk”: Identifying Computational Thinking in Embodied Models Through Movement. Proceedings of the Annual Meeting of the American Educational Research Association 2023.
Basu Roy Chowdhury, S., Zhao, C., & Chaturvedi, S. (2022). Unsupervised Extractive Opinion Summarization Using Sparse Coding. Proceedings of the Association for Computational Linguistics.
Cheng, F., Wang, X., Lei, J., Crandall, D., Bansal, M., & Bertasius, G. (2022). VindLU: A Recipe For Effective Video-and-Language Pretraining. https://arxiv.org/abs/2212.05051
Goree, S., Khoo, W., & Crandall, D. (2023). Correct for Whom? Subjectivity and the Evaluation of Personalized Image Aesthetics Assessment Models. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
Hmelo-Silver, C. E., Puntambekar, S., Glazewski, K. D., Lawrence, L., Rummel, N., Aleven, V., Biswas, G., Uttamchandani, S., Saleh, A., Bae, H., Brush, T., Mott, B., Lester, J., Goss, W., Gnesdilow, D., Passonneau, R., Singh, P., Kim, C.M., & Worsley, M. (2022). Artificial Intelligence and Ambitious Learning Practices. Proceedings of the International Conference on Computer-Supported Collaborative Learning 2022.
Hong, D., Uttamchnandani, S., Wang, T., Glazewski, K., Hmelo-Silver, C., Mott, B., & Lester, J. (2023). Connecting Collaboration Patterns to Knowledge Co-construction under the symposium paper titled “Transactivity and Knowledge Co-Construction in Collaborative Problem Solving.” To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Hong, D., Zou, X., Uttamchandani, S., Wang, T., Hmelo-Silver, C., Glazewski, K., Mott, B., & Lester, J. (2023). Towards Understanding Collaborative Scientific Inquiry Practices in CSCL Classrooms with In-game Data. To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Hu, Y., Zhang, S., Sathy, V., Panter, A., & Bansal, M. (2022). SETSum: Summarization and Visualization of Student Evaluations of Teaching. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations, pp. 71–89, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2023). Large Language Models Struggle to Learn Long-Tail Knowledge. To appear in Proceedings of the International Conference on Machine Learning. https://arxiv.org/abs/2211.08411
Kandpal, N., Wallace, E., & Raffel, C. (2022) Deduplicating Training Data Mitigates Privacy Risks in Language Models. Proceedings of the International Conference on Machine Learning.
Kumaran, V., Rowe, J., Mott, B., Chaturvedi, S. & Lester, J. (2023). Improving Classroom Dialogue Act Recognition from Limited Labeled Data with Self-Supervised Contrastive Learning Classifiers. To appear in Findings of the Association for Computational Linguistics 2023.
Lee, S.J., Lane, A.C., Jen, T., Anton, G., & Enyedy, N. (2023). Choosing, Imagining, and Changing as Agency in a Mixed-Reality STEM Learning Environment. To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Lee, S.J., Choussat, F., Anton, G., & Enyedy, N. (2023). Researcher-Teacher Co-Design in a Mixed-reality Science and Computational Thinking Curriculum. To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Lin, Y., Sung, Y., Lei, J., Bansal, M.,& Bertasius, G. (2023). Vision Transformers are Parameter-Efficient Audio-Visual Learners. To appear in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.
Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M., & Raffel, C. (2022). Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. To appear in Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS-2022).
Madasu, A., & Srivastava, S. (2022). What do Large Language Models Learn Beyond Language? Proceedings of the Empirical Methods in Natural Language Processing Conference.
Maharana, A., Hannan, D., & Bansal, M. (2022). StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation. To appear in Proceedings of the 17th European Conference on Computer Vision (ECCV-2022).
Roschelle, J. (2023). “The ChatAlgebra Educational Revolution.” [Blog Post]. Communications of the ACM. January 23, 2023.
Roschelle, J. (2023). “ChatGPT for Provost.” [Blog Post]. Communications of the ACM. February 16, 2023.
Roschelle, J. (2023). “AI in the Public Interest: Education and Democracy.” [Blog Post]. Communications of the ACM. March 23, 2023
Saha, S., Hase, P., Rajani, N., & Bansal, M. (2022). Are Hard Examples Also Harder to Explain? A Study with Human and Model-Generated Explanations. Proceedings of the Empirical Methods in Natural Language Processing Conference. https://arxiv.org/abs/2211.07517
Shi, Y., Schmucker, R., Chi, M., Barnes, T., & Price, T. (2023). KC-Finder: Automated Knowledge Component Discovery for Programming Problems. To appear in Proceedings of the International Conference on Educational Data Mining 2023.
Shahriar, T., & Noboru, M. (2023). What and how you explain matters: Inquisitive Teachable Agent Scaffolds Knowledge-building for Tutor Learning. To appear in Proceedings of the AI in Education Conference.
Steinberg, S., Hmelo-Siver, C., Zou, X., Danish, J.,& Lester, J. (2023). Seeing Student
Engagement in Classroom Video: Affordances of Cognitive and Sociocultural Frameworks. To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Sung, Y., Cho, J., & Bansal., M. (2022). LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning. To appear in Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS-2022).
Swarnadeep, S., Xhang, S., Hase, P., & Bansal, M. (2023). Summarization Programs: Interperatable Abstractive Summarization with Neural Modular Trees. Proceedings of the Eleventh International Conference on Learning Representations.
Tam, D., Mascarenhas, A., Zhang, S., Kwan, S., Bansal, M., & Raffel, C. (2022). Evaluating the factual consistency of large language models through summarization. To Appear in the Proceedings of the Sixty First Annual Meeting of the Association for Computational Linguistics 2023.https://arxiv.org/abs/2211.08412
Tang, Z., Cho, J., Nie, Y., & Bansal, M. (2022). TVLT: Textless Vision-Language Transformer. Proceedings of the Conference on Neural Information Processing Systems. https://arxiv.org/abs/2209.14156
Wan, D., & Bansal, M. (2022). Evaluating and Improving Factuality in Multimodal Abstractive Summarization. Proceedings of the Empirical Methods in Natural Language Processing Conference. https://arxiv.org/abs/2211.02580
Wan, D. & Bansal, M. (2022). FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1010–1028, Seattle, United States. Association for Computational Linguistics.
Wang, T., Uttamchandani, S., Zou, X., Hmelo-Silver, C., Rowe, J., & Lester, J.(2023). Learning with Stories: Characteristics and Learning Outcomes in Narrative-Centered Science Learning Environments. To appear in Proceedings of the International Society for the Learning Sciences Annual Meeting 2023.
Ying, Z., Hase, P., & Bansal, M. (2022). VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. Proceedings of the Conference on Neural Information Processing Systems. https://arxiv.org/abs/2206.11212
Zala, A., Cho, J., Kottur, S., & Chen, X. (2023). Hierarchical Video-Moment Retrieval and Step-Captioning. To appear in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.
Zhao, C., Brahman, F., Song, K., Yao, W., Yu, D., & Chaturvedi, S. (2022). NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization. Proceedings of the Empirical Methods in Natural Language Processing Conference.
Zhao, C., Huang, T., Basu Roy Chowdhury, S., Chandrasekaran, M.K., McKeown, K., & Chaturvedi, S. (2022). Read Top News First: A Document Reordering Approach for Multi-document News Summarization. Proceedings of the Annual Meeting of the Association for Computational Linguistics 2022.
Presentations
Crandall, D. (2022). Machines that See: The Past, Present, and Future of Computer Vision. Peebles Memorial Lecture, Indiana University, Bloomington, IN.
Crandall, D. (2022). Studying People to Improve Computer Vision. Department of Computer Science Colloquium, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Crandall, D. (2022). Studying People to Improve Computer Vision. Department of Computer Science and Engineering Colloquium, University of Notre Dame, Notre Dame, IN.
Crandall, D. (2022). Educational Applications of Artificial Intelligence. Indiana Commission on Higher Education, Indianapolis, IN.
Hmelo-Silver, C. E. (2021). The National Science Foundation AI Institute for Engaged Learning. IU AI Week, Indiana University, Bloomington, IN.
Hmelo-Silver, C. E. (2022). Affordances of Technologies for Ambitious Learning Practices. Presented at Augmented Intelligence Workshop, Indiana University, Bloomington, IN.
Hmelo-Silver, C. E. (2022). Scaffolding engagement in ambitious learning practices: An Ecojourney Towards AI. Invited keynote at the Learning Sciences Graduate Conference, Bloomington, IN.
Hmelo-Silver, C. E. (2022). AI and Ambitious Learning Practices. International Society for the Learning Sciences.
Lester, J. (2021). AI-Driven Narrative-Centered Learning Environments to Enhance STEM Learning, National Academy of Education.
Lester, J. (2021). AI and the Future of Education. AI in Learning.
Lester, J. (2021). The National Science Foundation AI Institute for Engaged Learning. NCSU University Research Committee Meeting, North Carolina State University, Raleigh, NC.
Lester, J. (2021). The National Science Foundation AI Institute for Engaged Learning. NCSU College of Engineering Foundation Board of Directors Meeting, North Carolina State University, Raleigh, NC.
Lester, J. (2021). AI and the Future of Education. ACM Multimedia, 2021.
Lester, J. (2021). AI Institute for Engaged Learning, CIRCLS 2021 Annual Convening, Redwood City, CA.
Lester, J. (2021). The National Science Foundation AI Instate for Engaged Learning. NCSU Research Week, North Carolina State University, Raleigh, NC.
Lester, J. (2021). The National Science Foundation AI Institute for Engaged Learning. NCSU College of Engineering Industry Day, North Carolina State University, Raleigh, NC.
Lester, J. (2022). The National Science Foundation AI Institute for Engaged Learning. The North Carolina State University Center & Institute Directors Meeting, North Carolina State University, Raleigh, NC.
Lester, J. (2022). The National Science Foundation AI Institute for Engaged Learning. The North Carolina State University Board of Visitors, North Carolina State University, Raleigh, NC.
Lester, J. (2023). The National Science Foundation AI Institute for Engaged Learning. Symposium on Research Themes Across the Three National Science Foundation–Funded National Artificial Intelligence Institutes on Education, AERA-2023, Chicago.
Lester, J. (2023). The National Science Foundation AI Institute for Engaged Learning. AI in Education Mini-Symposium, University of Florida, Virtual.
Lester, J., Biswas, G., Hmelo-Silver, C., & Roschelle, J. (2022). The AI Institute for Engaged Learning. Panel discussion at the Empowering Learners for the Age of AI Conference, Virtual.
Lester, J., & Wiebe, E. (2022 March). The National Science Foundation AI Institute for Engaged Learning. The Friday Institute for Educational Innovation, North Carolina State University, Raleigh, NC.
Roschelle, J. (2022). Scaling AI to Address the Long Tail of Learners’ Strengths and Needs. Invited keynote at the Empowering Learners for the Age of AI Conference, Virtual.
Rowe, J. (2021). Engaging the AI-Enriched Future of Learning. CMU First-Year Seminar.
Rowe, J. (2023). A Tale of Three Studies: Modeling Engagement in Narrative-Centered Learning Environments with Multimodal Learning Analytics. Invited keynote at the LAK 2023 Workshop on Situating Affect in Learning Analytics: Addressing Educational Challenges, co-located with the 13th International Conference on Learning Analytics and Knowledge, Arlington, TX.
Srivastava, S. (2022, September). Few-shot learning with interactive language. IBM Research, Almaden.