At the forefront of integrating artificial intelligence with educational paradigms, our institute champions innovative research that spans a variety of domains within AI and education, particularly emphasizing adult learning and online education.
Our publications investigate the complexities of designing AI systems that support and enhance learning experiences for adults, leveraging the latest advancements in technology to tailor educational content and methodologies to the needs of lifelong learners. Our work also explores the broader implications of AI in educational settings, from ethical considerations and participatory design to the development of robust technological infrastructures that facilitate online learning. Through our rigorous research, we aim to contribute valuable insights and practical solutions that address the challenges and opportunities presented by the intersection of AI and education.
Here, you will find a complete list of our publications, showcasing the breadth and depth of our work in this dynamic field. Whether you are a fellow researcher, an educator, or simply someone passionate about educational innovation, our publications offer a window into the innovative approaches we are taking to shape the future of AI and education. Alternatively, you may also follow us on Google Scholar.
Bae, Y., Kim, J., Davis, A., & Kim, M. (2024). A study on AI-augmented concept learning: Impact on learner perceptions and outcomes in STEM education. The 18th International Conference of the Learning Sciences – ICLS 2024. Buffalo, New York: International Society of the Learning Sciences. https://doi.org/10.22318/icls2024.796516
Baffour, P., & Crossley, S. A. (2024). Advances in automating feedback for argumentative writing: Feedback Prize as a case study. In J. Wilson & M. Shermis (Eds). Routledge International Handbook of Automated Essay Evaluation. New York: Routledge. https://doi.org/10.4324/9781003397618-19
Barari, N., Lian, X., & MacLellan, C. J. (2024). Incremental Concept Formation over Visual Images Without Catastrophic Forgetting. ArXiv.org. https://arxiv.org/abs/2402.16933
Basappa, R., Tekman, M., Lu, H., Faught, B., Kakar, S. & Goel, A. (2024) Social AI agents too need to explain themselves. Procs. 20th International Conference on Intelligent Tutoring Systems, 2024. https://doi.org/10.1007/978-3-031-63028-6_29
Bondie, R., Mancenido, Z., Adams, H., Dede, C. (2024). Exploring data visualization in mixed reality simulations to measure teacher responsiveness. In: Bourguet, ML., Krüger, J.M., Pedrosa, D., Dengel, A., Peña-Rios, A., Richter, J. (eds) Immersive Learning Research Network. iLRN 2023, pp. 173-181. Communications in Computer and Information Science, vol 1904. Springer, Cham. https://doi.org/10.1007/978-3-031-47328-9_13
Boyle, J., & Crossley, S. (2024). Semantic Similarity of Teacher and Student Discourse Linked to Quality Ratings from Classroom Observations. Proceedings of the 17th International Conference on Educational Data Mining, 797–801. https://doi.org/10.5281/zenodo.12729954
Calo, T., & Maclellan, C. (2024). Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces. ArXiv (Cornell University). https://doi.org/10.1145/3657604.3664694
Chen, J., Wang, M., Grotzer, T. A., & Dede, C. (2024). Analysing students’ concept mapping style and its association with task performance in computer‐based inquiry learning. Journal of Computer Assisted Learning, 40(4), 1727–1744. https://doi.org/10.1111/jcal.12984
Coscia, A., Holmes, L., Morris, W., Choi, J. S., Crossley, S., & Endert, A. (2024, March). iScore: Visual analytics for interpreting how language models automatically score summaries. In Proceedings of the 29th International Conference on Intelligent User Interfaces (pp. 787-802). https://doi.org/10.1145/3640543.3645142
Crossley, S. A. (2024). Developing Linguistic Constructs of Text Readability Using Natural Language Processing. Scientific Studies of Reading, 1–23. https://doi.org/10.1080/10888438.2024.2422365
Crossley, S., & Choi, J. S. (2024). Measuring Phonological Complexity Using the Perfect Rhymes Dictionary (PeRDict). Reading Psychology, 45(8), 775–802. https://doi.org/10.1080/02702711.2024.2359925
Crossley, S., Perpetual Baffour, Mihai Dascalu, & Ruseti, S. (2024). A World CLASSE Student Summary Corpus. ACL Anthology, 99–107. https://aclanthology.org/2024.bea-1.9
Crossley, S. A., Tian, Y., Holmes, L., Morris, W., & Choi, J. S. (2024). Plagiarism detection using keystroke logs. Proceedings of the 17th International Conference on Educational Data Mining (EDM). Atlanta, GA https://educationaldatamining.org/edm2024/proceedings/2024.EDM-short-papers.47/2024.EDM-short-papers.47.pdf
Crossley, S.A., Tywoniw, R. & Choi, J.S. The Tool for Automatic Measurement of Morphological Information (TAMMI). Behav Res 56, 5918–5929 (2024). https://doi.org/10.3758/s13428-023-02324-w
Dede, C. (2024). Designing a Model for Massive Digital Lifelong Learning. https://doi.org/10.1145/3657604.3665449
Goel, A., & Ou, C. (Eds.) (2024). NSF’s National AI Institutes [Special Issue]. AI Magazine, 45(1). https://onlinelibrary.wiley.com/toc/23719621/2024/45/1
Goel, A., Dede, C., Garn, M., & Ou, C. (2024). AI-ALOE: AI for reskilling, upskilling, and workforce development. AI Magazine, 45(1), 77–82. https://doi.org/10.1002/aaai.12157
Guo, G., Kumar, A. M. S., Gupta, A., Coscia, A., MacLellan, C., & Endert, A. (2024). Visualizing intelligent tutor interactions for responsive pedagogy. In Proceedings of the 2024 International Conference on Advanced Visual Interfaces. 1–9. https://doi.org/10.1145/3656650.3656667
Heffernan, N., Wang, R., MacLellan, C., Arto Hellas, Li, C., C, Walkington, ace, Littenberg-Tobias, J., Joyner, D., Moore, S., Singla, A., Zach Pardos, Pankiewicz, M., Kim, J., Shashank Sonkar, Cohn, C., Botelho, A., Lan, A., Jiang, L., & Feng, M. (2024). Leveraging Large Language Models for Next-Generation Educational Technologies. Zenodo. https://doi.org/10.5281/zenodo.12730049
Holmes L., Crossley, S.A., Wang, J., Zhang, W. (2024). The cleaned repository of annotated personally identifiable information. Proceedings of the 17th International Conference on Educational Data Mining (EDM) (2024). https://doi.org/10.5281/zenodo.12729952
Jung, Y. & Crossley, S. (2024). Stylistic alignment in natural conversation involving second language speakers. Applied Linguistics Review, 15(3), 871-900. https://doi.org/10.1515/applirev-2020-0048
Kakar, S., Basappa, R., Camacho, I., Griswold, C., Houk, A., Leung, C., Tekman, M., Westervelt, P., Wang, Q., Goel, A.K. (2024). SAMI: An AI actor for fostering social interactions in online classrooms. In Proceedings of 20th International Conference, ITS 2024, Springer, Thessaloniki, Greece. https://doi.org/10.1007/978-3-031-63028-6_12
Kakar, S., Maiti, P., Taneja, K., Nandula, A., Nguyen, G., Zhao, A., Nandan, V., Goel, A. (2024). Jill Watson: Scaling and deploying an AI conversational agent in online classrooms. In Proceedings of the 20th International Conference, ITS 2024, Springer, Thessaloniki, Greece. https://doi.org/10.1007/978-3-031-63028-6_7
Kim, M., Kim, J., Bae, Y., Morris, W., Holmes, L., & Crossley, S. (2024). How AI evaluate learner comprehension: A comparison of knowledge-based and large language model (LLM)-based AI approaches. The 18th International Conference of the Learning Sciences – ICLS 2024. Buffalo, New York: International Society of the Learning Sciences. https://doi.org/10.22318/icls2024.534046
Kim, M., Kim, J., & Heidari, A. (2024). Exploring the multi-dimensional human mind: Model-based and text-based approaches for automated summary assessment. Assessing Writing, 61, 100878. https://doi.org/10.1016/j.asw.2024.100878.
Kim, J., Bae, Y., Stravelakis, J., & Kim, M. (2024). Investigating the influence of AI-augmented summarization on concept Learning, summarization skills, argumentative essays, and course outcomes in online adult Education. The18th International Conference of the Learning Sciences – ICLS 2024. Buffalo, New York: International Society of the Learning Sciences. https://doi.org/10.22318/icls2024.101630
Kim, J., Lee, T., Bae, Y., & Kim, M. (2024). A comparison between AI and human evaluation with a focus on generative AI. The 18th International Conference of the Learning Sciences – ICLS 2024. Buffalo, New York: International Society of the Learning Sciences. https://doi.org/10.22318/icls2024.930382
Kos, J., Ayyappan D., & Goel, A. K. (2024) A Constructivist framing of wheel spinning: Identifying unproductive behaviors with sequence analysis. Proceedings of 20th International Conference, ITS 2024, Springer, Thessaloniki, Greece. https://doi.org/10.1007/978-3-031-63028-6_14
Lawley, L., & MacLellan, C. J. (2024). VAL: Interactive Task learning with GPT dialog parsing. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613904.3641915
Li, C., Crossley, S., Burke, M., & Rossetti, Z. (2024). Relation of Linguistic Indicators to Civic Engagement in Special Education. Proceedings of the 17th International Conference on Educational Data Mining, 727–731. https://doi.org/10.5281/zenodo.12729930
Lian, X., Baglodi, N., & MacLellan, C. J. (2024). Incremental and Data-Efficient Concept Formation to Support Masked Word Prediction. ArXiv.org. https://doi.org/10.48550/arXiv.2409.12440
Lian, X., Varma, S., & MacLellan, C. J. (2024). Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning. ArXiv.org. https://arxiv.org/abs/2403.03835
Lindgren, R., Kakar, S., Maiti, P., Taneja, K., and Goel, A. (2024) Does Jill Watson Increase Teaching Presence? To appear in Proceedings of the Eleventh ACM Conference on Learning @ Scale, L@S ’24. New York, NY: ACM. https://doi.org/ 10.1145/3657604.3664679
Lyndgaard, S. F., & Kanfer, R. (2024). Interpersonal, intrapersonal, and cognitive tactics: A thematic analysis of adults’ 21st century learning management. Journal of the Learning Sciences, 33(1), 175-211. https://doi.org/10.1080/10508406.2024.2303777
Lyndgaard, S. F., Storey, R., & Kanfer, R. (2024). Technological support for lifelong learning: The application of a multi-level, person-centric framework. Journal of Vocational Behavior, 104027–104027. https://doi.org/10.1016/j.jvb.2024.104027
Lyndgaard, S. F., Tatel, C. E., Pham, V., Melkers, J. E., & Kanfer, R. (2024). Towards a multidimensional measure of self-efficacy in the adult learning ecosystem. International Journal of Lifelong Education, 1–24. https://doi.org/10.1080/02601370.2024.2369895
Maiti, P., & Goel, A. K. (2024). How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings? ArXiv.org. https://doi.org/10.48550/arXiv.2407.17429
Morris, W., Crossley, S., Holmes, L., Ou, C., Dascalu, M., & McNamara, D. (2024). Formative feedback on student-authored summaries in intelligent textbooks using large language models. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00395-0
Madhusudhana, R. H., Dass, R. K., Luu, J., & Goel, A. K. (2024). Integrating cognitive AI with generative models for enhanced question answering in skill-based learning. arXiv preprint arXiv:2407.19393. https://doi.org/10.48550/arXiv.2407.19393
Nattamai Subramanian Rajkumar, U., Lyndgaard, S. F., & Kanfer, R. (2024). Artificial intelligence for adult learning at scale: A narrative review. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S ’24). Association for Computing Machinery. https://doi.org/10.1145/3657604.3664692
Ou, C., Thajchayapong, P., & Joyner, D. (2024). Open, collaborative, and AI-augmented peer assessment: Student participation, performance, and perceptions. In Proceedings of the Eleventh ACM Conference on Learning @ Scale, L@S ’24. July 18-20, 2024, Atlanta, Georgia, USA. ACM, New York, NY, USA. https://doi.org/10.1145/3657604.3664705
Qi, Z., Lyndgaard, S. F., Melkers, J. E., & Kanfer, R. (2024). Toward Sustainable Lifelong Learning: Feedforward Effects of Challenge Recollections on Adult Learning Identity. Applied Cognitive Psychology, 38(5). https://doi.org/10.1002/acp.4248
Reilly, J. M., McGivney, E., Dede, C., & Grotzer, T. (2024). Assessing science identity exploration in immersive virtual environments: A Mixed Methods Approach. Virtual Learning Environments, 27–48. https://doi.org/10.4324/9781032726663-4
Rugaber, S., & Goel, A. AI Tai Chi / Using AI to combat technological unemployment caused by AI. 23rd Annual Hawaii International Conference on Education, Kona, Hawaii, January 2-6, 2024.
Siddiqui, M. N., Gupta, A., Reddig, J. M., & MacLellan, C. J. (2024). HTN-Based Tutors: A New Intelligent Tutoring Framework Based on Hierarchical Task Networks. https://doi.org/10.1145/3657604.3664702
Smith, G., Gupta, A., & MacLellan, C. (2024). Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors. ArXiv.org. https://arxiv.org/abs/2404.07883
Sushri, S., Dass, R., Basappa, R., Lu, H., & Goel, A. (2024). Combining cognitive and generative AI for self-explanation in interactive AI agents. HEXED (Human-Centric eXplainable AI in Education) Workshop in conjunction with the 17th International Conference on Educational Data Mining (EDM). July 2024. https://doi.org/10.48550/arXiv.2407.18335
Taneja, K., & Goel, A. (2024). Can Active Label Correction Improve LLM-based Modular AI Systems? ACL Anthology, 9019–9031. https://aclanthology.org/2024.emnlp-main.509/
Taneja, K., Maiti, P., Kakar, S., Guruprasad, P., Rao, S., & Goel, A. (2024) Jill Watson: A virtual teaching assistant powered by ChatGPT. Procs. 24th International Conference on AI in Design, Brazil, July 2024. https://doi.org/10.1007/978-3-031-64302-6_23
Wang, L., Li, X., Zhou, D., & Goel, A. (2024) Applying theory of mind to personalize AI for supporting life transitions. In Proc. CHI 2024 Workshop on Theory of Mind, May 2024. https://dilab.gatech.edu/test/wp-content/uploads/2024/05/Applying-Theory-of-Mind-to-Personalize-AI-for-Supporting-Life.pdf
Wang, Q., Anyi, Chidimma L, Swain, V. D., & Goel, A. K. (2024). Navigating AI Fallibility: Examining People’s Reactions and Perceptions of AI after Encountering Personality Misrepresentations. ArXiv.org.
https://doi.org/10.48550/arXiv.2405.16355
Wang, Q., & Goel, A. (2024) Mutual theory of mind for human-AI communication. In Proc. CHI 2024 Workshop on Theory of Mind, May 2024. https://dilab.gatech.edu/test/wp-content/uploads/2024/05/Mutual-Theory-of-Mind-for-Human-AI-Communication-2.pdf
Wang, Q., Walsh, S., Si, Mei, Kephart, J., Weisz, J., & Goel, A. (2024) Theory of mind in human-AI interaction. CHI 2024 Extended Abstracts, 493:1-6. https://doi.org/10.1145/3613905.3636308
Wen, X., Weber, R. O., Sen, A., Hannan, D., Nesbit, S. C., Chan, V., Goffi, A., Morris, M., Hunninghake, J. C., Villalobos, N. E., Kim, E., & MacLellan, C. J. (2024). The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data. ArXiv.org. https://arxiv.org/abs/2407.06206
Xing, G, Ahmadzadeh Siyahrood, S., Wang, X.C., Xiong J. (2024). Toward productive multivocality in AI development: Excavating ethics concerns among an interdisciplinary team. In Proceedings of the 18th International Conference of the Learning Sciences – CSCL 2024. Buffalo, New York: International Society of the Learning Sciences. https://doi.org/10.22318/cscl2024.306568
Zhou, J., & MacLellan, C. (2024). Improving Interface Design in Interactive Task Learning for Hierarchical Tasks based on a Qualitative Study. The 37th Annual ACM Symposium on User Interface Software and Technology, 1–3. https://doi.org/10.1145/3672539.3686326
Bae, Y., Kim, J., & Kim, M. (2023). Clustering cognitive engagement changes in a longitudinal traced discussion data from an online course. Proceedings of the 17th International Conference of the Learning Sciences/Computer-Supported Collaborative Learning (ICLS/CSCL-2023). Montréal, Canada: International Society of the Learning Sciences. https://doi.org/10.22318/cscl2023.344145
Baffour, P., Saxberg, T., & Crossley, S. (2023). Analyzing bias in large language model solutions for assisted writing feedback tools: Lessons from the feedback prize competition series. Proceedings of the 2023 BEA 18th Workshop on Innovative Use of NLP for Building Educational Applications. (pp. 242–246). https://doi.org/10.18653/v1/2023.bea-1.21
Bondie, R., Zusho, A., Wiseman, E., Dede, C., & Rich, D. (2023). Can differentiated and personalized mixed-reality Simulations transform teacher learning? Technology, Mind, and Behavior, 4(1: Spring 2023). https://doi.org/10.1037/tmb0000098
Bondie, R., & Dede, C. (2023). What we want versus what we have: Transforming teacher performance analytics to personalize professional development. In P.D. Moskal, C.D. Dziuban, & A Picciano (Eds.), Data Analytics and Adaptive Learning, Research Perspectives, pp. 23-37. New York, NY: Taylor & Francis/Routledge. https://doi.org/10.4324/9781003244271-4
Coscia, A., & Endert, A. (2023). KnowledgeVIS: Interpreting language models by comparing fill-in-the-blank prompts. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2023.3346713
Crossley, S. A., Tywoniw, R., & Choi, J. S. (2023). The Tool for automatic measurement of morphological information (TAMMI). Behavior Research Methods. https://doi.org/10.3758/s13428-023-02324-w
Crossley, S., Choi, J. S., Scherber, Y., & Lucka, M. (2023). Using large language models to develop readability formulas for educational settings. International Conference on Artificial Intelligence in Education. https://doi.org/10.1007/978-3-031-36336-8_66
Crossley, S. A., Heintz, A., Choi, J., Batchelor, J., Karimi, M., & Malatinszky, A. (2023). A large-scaled corpus for assessing text readability. Behavior Research Methods, 55, 491–507. https://doi.org/10.3758/s13428-022-01802-x
Dede, C., & Lidwell, W. (2023). Developing a next-generation model for massive digital learning. Education Sciences, 13(8), 845. https://doi.org/10.3390/educsci13080845
Donlon, J., & Goel, A. (2023). Looking back, looking ahead: Strategic initiatives in AI and NSF’s AI Institutes Program. AI Magazine, 44(3), 345–348. https://doi.org/10.1002/aaai.12107
Hannan, D., Nesbit, S.C., Wen, X., Smith, G., Zhang, Q., Goffi, A., Chan, V., Morris, M.J., Hunninghake, J.C., Villalobos, N.E., Kim, E., Weber, R.O., MacLellan, C.J. (2023). MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples. In Proceedings of The Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i13.26859
Harpstead, E., Stowers, K., Lawley, L., Zhang, Q., MacLellan, C.J. (2023). Speculative Game Design of Asymmetric Cooperative Games to Study Human-Machine Teaming. In Proceedings of The Foundations of Digital Games 2023 (FDG 2023). https://doi.org/10.1145/3582437.3587200
Holmes, L., Crossley, S. A., Morris, W., Sikka, H.,& Trumbore, A. (2023). Deidentifying Student Writing with Rules and Transformers. In Proceedings of the 24thInternational Conference on Artificial Intelligence in Education (AIED). Tokyo, Japan. https://doi.org/10.1007/978-3-031-36336-8_109
Holmes, L., Crossley, S., Sikka, H., & Morris, W. (2023). PIILO: an open-source system for personally identifiable information labeling and obfuscation. Information and Learning Sciences, 124(9/10), 266-284. https://doi.org/10.1108/ils-04-2023-0032
Hornback, A., Buckley, S., Kos, J., Bunin, S., An, S., Joyner, D., & Goel, A. (2023). A scalable architecture for conducting A/B experiments in educational settings. Proceedings of the Tenth ACM Conference on Learning@ Scale. https://doi.org/10.1145/3573051.3596190
Kim, M., Kim, N., Haddadian, G., & Heidari, A. (2023). A test of learning progress models using an AI-enabled knowledge representation system. Proceedings of the 17th International Conference of the Learning Sciences/Computer-Supported Collaborative Learning(ICLS/CSCL-2023). Montréal, Canada: International Society of the Learning Sciences. https://doi.org/10.22318/icls2023.200138
Kim, J., Haddadian, G., & Kim, M. (2023). An investigation of knowledge-based AI vs. human evaluation in academic summary evaluation: Similarities, dissimilarities, and being toward mutual understandings. Proceedings of the 17th International Conference of the Learning Sciences/Computer-Supported Collaborative Learning (ICLS/CSCL-2023). Montréal, Canada: International Society of the Learning Sciences. https://doi.org/10.22318/icls2023.633243
Lawley, L., & MacLellan, C.J. (2023). Interactive learning of hierarchical tasks from dialog with GPT. arXiv preprint arXiv:2305.10349. https://doi.org/10.48550/arXiv.2305.10349
Morris, W., Crossley, S., Holmes, L., Ou, C., McNamara, D., Dascalu, M. (2023) Using Transformer Language Models to Provide Formative Feedback in Intelligent Textbooks. In Proceedings of the 24thInternational Conference on Artificial Intelligence in Education (AIED). Tokyo, Japan. https://doi.org/10.1007/978-3-031-36336-8_75
Morris, W., Crossley, S., Holmes, L., & Turmbore, A (2023). Using transformer language models to validate peer-assigned essay scores in Massive Open Online Courses (MOOCs). In Proceedings of the 13th International Learning Analytics and Knowledge Conference (LAK2023). pp. 315-323. https://doi.org/10.1145/3576050.3576098
Kim, M. K., Gaul, C. J., Bundrage, C. N., & Madathany, R. J. (2023). Technology supported reading comprehension: a design research of the student mental model analyzer for research and teaching (SMART) technology. Interactive Learning Environments, 31(3), 1377-1401. https://doi.org/10.1080/10494820.2020.1838927
Lee, J., Soylu, M. Y., & Ou, C. (2023). Exploring insights from online students: Enhancing the design and development of intelligent textbooks for the future of online education. International Journal on Innovations in Online Education, 7(2). https://doi.org/10.1615/intjinnovonlineedu.2023049742
Ou, C., & Joyner, D. (2023). Seven years of online project-based learning at scale. International Journal on Innovations in Online Education, 7(1). https://doi.org/10.1615/intjinnovonlineedu.2023049968
Shane, M., & Bressler, M., D., & Reilly, J., & McGivney, E., & Grotzer, A., T., & Dede, C. (2023). Toward a framework for robust design-based research. Educational Innovations and Emerging Technologies, 3(3), 1-7. https://doi.org/10.35745/eiet2023v03.03.0001
Wang, Q., Madaio, M., Kane, S., Kapania, S., Terry, M., & Wilcox, L. (2023, April). Designing responsible AI: Adaptations of UX practice to meet responsible AI challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-16) https://doi.org/10.1145/3544548.3581278
Zhang, Q. (2023). Understanding human-AI teaming dynamics through gaming environments. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1), 440-443. https://doi.org/10.1609/aiide.v19i1.27541
An, S., Rugaber, S., Hammock, J., & Goel, A. (2022) Understanding Self-Directed Learning with Sequential Pattern Mining. Proceedings of International Conference on AI in Education Conference, Durham, UK, July 2022. https://doi.org/10.1007/978-3-031-11647-6_102
An, S., Rugaber, S., Hammock, J., & Goel, A. K. (2022). Contextualizing Large-Scale Domain Knowledge for Conceptual Modeling and Simulation. arXiv preprint arXiv:2209.02579. https://doi.org/10.48550/arXiv.2209.02579
An, S., Weigel, E., & Goel, A. K. (2022). Effects of Guidance on Learning About Ill-defined Problems. In International Conference on Intelligent Tutoring Systems (pp. 303-312). Springer, Cham. https://doi.org/10.1007/978-3-031-09680-8_28
Botarleanu, R.-M., Dascalu, M., Allen, L. K., Crossley, S. A., & McNamara, D. S. (2022). Multitask summary scoring with Longformers. Lecture Notes in Computer Science, 756–761. https://doi.org/10.1007/978-3-031-11644-5_79
Bunin, S., Celestin, W., Hornback, A., & Rugaber, S. (2022, June). Incorporating Habitats in Conceptual Models and Agent-Based Simulations: Expanding the Virtual Ecological Research Assistant (VERA). In Proceedings of the Ninth ACM Conference on Learning@ Scale (pp. 472-474). https://doi.org/10.1145/3491140.3528261
Crossley, S. A., & Holmes, L. (2022). Assessing receptive vocabulary using state of the art natural language processing techniques. Journal of Second Language Studies. https://doi.org/10.1075/jsls.22006.cro
Crossley, S. A., McNamara, D. S., Dalsen, J., Anderson, C., & Steinkuehler, C. (2022). Linking natural language to science. In A. Alavi, P. Jiao, & B. McLaren (Eds). In Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology. Boca Raton, FL: CRC Press Taylor & Francis. https://doi.org/10.1201/9781003181187-24
Davis, H.D. & Garn, M. (2022). Embracing Disruption and New Educational Models to
Transform Learning Across Higher Education Systems. In J.S. Gagliardi & J.E.Lane (Eds.) Higher education system redesigned: From perception to innovation in student success. State University of New York Press, Albany, NY. https://doi.org/10.1515/9781438487694-008
Dede, C. (2022). The Coming Sea-Change in Teacher Education. Journal of Technology and Teacher Education, 30(2), 117-125. Waynesville, NC USA: Society for Information Technology & Teacher Education. https://www.learntechlib.org/primary/p/221170/.
Dede, C., & Etemadi, A. (2022). Skills are not enough: Developing workers’ dispositions to succeed in an uncertain, disruptive world. Evolllution (3/24/2022)
Dieterle, E, Dede, D., & Walker, M. (2022). The cyclical ethical effects of using artificial intelligence in education. AI & Society. https://doi.org/10.1007/s00146-022-01497-w
Goel, A., Nandan, V., Gregori, E., An, S., & Rugaber, S. (2022). Explanation as Question Answering based on User Guides. in Proceedings of AAAI-2022 Workshop on Explanation in Agency. https://doi.org/10.1201/9781003355281-4
Goel, A., Sikka, H, & Gregori, E. (2022). Agent Smith: Teaching Question Answering to Jill Watson. In Proceedings of AAAI 2022 Spring Symposium on Combining Machine Learning and Knowledge Engineering, Stanford University, March 2022. https://doi.org/10.48550/arXiv.2112.13677
Harmon, S. (2022, September 19). The Newcomers: Opening an Institution to More and More Learners. The Evolllution. https://evolllution.com/attracting-students/accessibility/the-newcomers-opening-an-institution-to-more-and-more-learners/
Holmes, L., Crossley, S. A., Haynes, R., Kuehl, D., Trumbore, T., & Gutu, G. (2022). Deidentification of student writing in technologically mediated educational settings. Proceedings of the 7th conference on Smart Learning Ecosystems and Regional Development (SLERD). Bucharest, Romania. https://doi.org/10.1007/978-981-19-5240-1_12
Kim, N., & Kim, M. (forthcoming). Teacher’s perceptions of using an artificial intelligence-based educational tool. Frontiers in Education. https://doi.org/10.3389/feduc.2022.755914
Kim, M., & Kim, N. (2022). AI-supported scaffolding for writing academic arguments. In C. Chinn, E. Tan, C. Chan, & Y. Kali (Eds.), Proceedings of the 16th International Conference of the Learning Sciences-ICLS2022 (pp. 1129-1132). Hiroshima, Japan: International Society of the Learning Sciences. https://repository.isls.org//handle/1/8426
MacLellan, C. J., Matsakis, P., & Langley, P. (2022). Efficient Induction of Language Models via Probabilistic Concept Formation. In Proceedings of the Tenth Annual Conference on Advances in Cognitive Systems. https://doi.org/10.48550/arXiv.2212.11937
MacLellan, C. J., Stowers, K., Brady, L. (2022). Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning. Advances in Cognitive Systems, 10, 1-18. https://chrismaclellan.com/media/publications/maclellan-acs-journal-2022.pdf
McCarthy, K. S., Crossley, S. A., Meyers, K., Boser, U., Allen, L. K., Chaudhri, V. K., Collins-Thompson, K., D’Mello, S., De Choudhury, M., Garg, K., Goel, A., Gosha, K., Heffernan, N., Hooper, M. A., Hyman, E., Jarratt, D. C., Khalil, D., Kizilcec, R. F., Litman, D., Malatinszky, A., Marks, K., McNamara, D. S., Menko, R., Palermo, C., Porcaro, D., Roscoe, R., Shapiro, S., Khanh-Phoung, T., Trumbore, A. M., White, C., Wong, W., Yang, D., & Zampieri, M. (2022). Toward more effective and equitable learning: Identifying barriers and solutions for the future of online education. Technology, Mind, & Behavior. https://doi.org/10.1037/tmb0000063
Ou, C., Goel, A., & Joyner, D. (2022). Towards a pedagogical framework for designing and developing iTextbook. Proceedings of the 23rd International Conference on Artificial Intelligence in Education, Fourth Workshop on Intelligent Textbook, July 2022. https://ceur-ws.org/Vol-3192/itb22_p6_full4226.pdf
Radu, I., Dede, C., Wang, J., Nie, G., Bhola, K., & Scuzzarella, M. (2022). Using virtual environments to reveal teacher bias towards students’ socioeconomic status. 2022 8th International Conference of the Immersive Learning Research Network (iLRN), pp. 1-8. https://doi.org/10.23919/ilrn55037.2022.9815955
Taneja, K., Sikka, H. & Goel, A. (2022). Human-AI Interaction Design in Machine Teaching. arXiv preprint arXiv:2206.05182. https://doi.org/10.48550/arXiv.2206.05182
Wang, Q, Jing, S., & Goel, A (2022). Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities. Proceedings of Computer Supported Collaborative Work, Taipei, Taiwan, November 2022. https://doi.org/10.1145/3512977
Wang, Q., Camacho, I., & Goel, A. K. (2022). Investigating the potential of AI-based social matching systems to facilitate social interaction among online learners. Social and Emotional Learning and Complex Skills Assessment, 279–298. https://doi.org/10.1007/978-3-031-06333-6_13
Wang, Q., & Goel, A. K. (2022). Mutual Theory of Mind for Human-AI Communication. arXiv preprint arXiv:2210.03842. https://doi.org/10.48550/arXiv.2210.03842
Wang, Q., Jing, S., & Goel, A. K. (2022). Co-designing AI agents to support social connectedness among online learners: Functionalities, social characteristics, and ethical challenges. Designing Interactive Systems Conference. https://doi.org/10.1145/3532106.3533534
Zhang, Q., Chen, Z., Lalwani, N., & MacLellan, C. (2022). Modifying Deep Knowledge Tracing for Multi-step Problems. In Proceedings of the 15th International Conference on Educational Data Mining (p. 684). https://doi.org/10.5281/zenodo.6853145
Zhang, Q. & MacLellan, C. J. (2022). (A)I Will Teach You to Play Gomoku: Exploring the Use of Game AI to Teach People. Proceedings of the Ninth ACM Conference on Learning @ Scale (pp. 263-266). https://doi.org/10.1145/3491140.3528331
