Newly Published! The “Handbook of Generative AI in Education” is out now.

Members of AI-ALOE contributed to the ‘Generative AI Risk and Benefits within Human-Machine Teams’ section with the following ‘Evolution of A4L: A Data Architecture for AI-Augmented Learning’. Contributed by Ploy Thajchayapong, Suzanne Carbonaro, Tim Couper, Blaine Helmick, Spencer Rugaber and Ashok Goel. [Read Here]

Here’s an excerpt from the opening blurb on the collection:

“This Handbook provides a comprehensive, research-informed foundation for integrating GenAI into educational practice across K–12, higher education, and professional learning contexts. Bringing together contributions from 74 scholars across five continents, the volume synthesizes perspectives from learning sciences, instructional design, assessment, and educational technology to advance a human-centered approach to GenAI adoption. Organized into five thematic parts—vision and frameworks; human-centered learning sciences; pedagogical innovation and instructional design; assessment, evidence, and learning analytics; and emerging horizons—the chapters collectively emphasize augmentation rather than replacement of educators.“

Link to full book [here].

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., & Kim, M. K. (2026). Four years of AI-powered formative assessment: Exploring the 2-sigma effect and fairness. In B. K. Litts, D. DeLiema, C. Lee, S. Krist, A. Mawasi, L. Martin, & K. Kumpulainen (Eds.), Proceedings of the 20th International Conference of the Learning Sciences – ICLS 2026 (pp. 1382–1386). International Society of the Learning Sciences.

Dass, R., Puri, S., Khandelwal, A., Jin, X., Kundra, A., Hall, J., & Goel, A. (2026). Beyond the Chatbot: Deploying Ivy, a Knowledge-Based AI Coach, in a Georgia Tech Online AI Course. In Georgia Tech Academic AI Playbook, 2026 Edition, edited by David A. Joyner & Nimisha Roy (Georgia Institute of Technology, 2026). Licensed under CC BY-NC-SA 4.0.

Dass, R., Puri, S., Khandelwal, A., Jin, X., and Goel, A. (2026). Developing Models of Procedural Skills using an AI-assisted Text-to-Model Approach. In Proceedings of the Thirteenth ACM Conference on Learning @ Scale (L@S ’26). Association for Computing Machinery, New York, NY, USA, 69–78. https://doi.org/10.1145/3774398.3811603

Dede, C. (2026) Increased Immersion in the Digital Multiverse: Implications for Adult Motivation. In IEEE Transactions on Learning Technologies, vol. 19, pp. 706-710, 2026. https://doi.org/10.1109/TLT.2026.3711900

Dede, C. (2026). What Works When for Whom Under What Conditions: Learning Engineering as an Enabler of Component-based Research. In Craig, S. D., Arner, T., & McNamara, D. S. (Eds.), Proceedings of the Learning Engineering Research Network Convening (LERN 2026): From Insights to Implementation, Learning Engineering in Action. LERN Convention Proceedings. https://doi.org/10.59668/2551.25412

Elshabrawy, S., Dass, R. K., & Goel, A. K. (2026). Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage. arXiv preprint arXiv:2606.12767.

Goel, A.K., Sushri, S., Alico, N.E., Luu, J., Khan, R.M., Rugaber, S. (2026). Generative AI for Metacognition in AI Agents. In: Mayrath, M.C., Behrens, J.T., Robinson, D.H. (eds) Handbook of Generative AI in Education. Springer, Cham. https://doi.org/10.1007/978-3-032-24819-0_23

Han, H., Kim, S., Abdeen, S., & Kim. M. (2026) AI-scaffolded summarization in STEM: How do students differ in writing and revision? In Proceedings of the 20th International Conference of the Learning Sciences – ICLS 2026. International Society of the Learning Sciences.

Jin, X., Dass, R. K., & Goel, A. K. (2026). From Explanation to Diagnosis: Next Generation Interactive Video Coach with Misstep Awareness. arXiv preprint arXiv:2606.02970.

Kim, J. Y., Dede, C. (2026) Reconceptualizing the Federal Role in AI Education: Evidence From U.S. Congressional Proposals. Educational Policy. https://doi.org/10.1177/08959048261481539

Kim, J., Islam, M., & Goel, A. (2026). A Metacognitive Architecture for Correcting LLM Errors in AI Agents. Association for the Advancement of Artificial Intelligence. https://dilab.gatech.edu/test/wp-content/uploads/2026/02/A-Metacognitive-Architecture-for-Correcting-LLM-Errors-in-AI-Agents.pdf

Kim, J., Islam, M., Goel, A. (2026). A Metacognitive Architecture for ToM Revision in AI Agents. https://dilab.gatech.edu/test/wp-content/uploads/2026/02/A-Metacognitive-Architecture-for-ToM-Revision-in-AI-Agents.pdf

Kim, S., Han, H., & Kim. M. (2026). Design principles for AI-supported chatbots to scaffold problem-based learning in undergraduate nursing education. In Proceedings of the 20th International Conference of the Learning Sciences – ICLS 2026 (pp. 3238–3240). International Society of the Learning Sciences.

Malcolm, B., Vickery, M., Louis-Strakes Lopez, J., Siciliano, L., Simon, S., Xing, G., Kim, J., Kim, C., Zhao, Y., Desai, A., Sol Gadong, E., Mabadeje, Y., Mhungu, B., Haddadian, G., Eloy, A., Soodhani, N., Prasad, R., & Bae., Y (2026). Fostering Educational Intimacy: ILSSA Intergenerational Partnerships for Purposeful Community Building. In Proceedings of the 20th International Conference of the Learning Sciences – ICLS 2026 (pp. 2424–2436). International Society of the Learning Sciences.

Park, J., Medhat, Y., Wai, H.P., Thajchayapong, P., Goel, A. (2026). Evaluating Learner Representations for Differentiation Prior to Instructional Outcomes. In: Blanchard, E.G., Chen, G., Chi, M., Isotani, S. (eds) Artificial Intelligence in Education. AIED 2026. Lecture Notes in Computer Science(), vol 16585. Springer, Cham. https://doi.org/10.1007/978-3-032-29770-9_4

Reddig, J., Gupta, A., Siddiqui, M., Izmirilian,  E.,  MacLellan, C. (2026). Static, Dynamic, or Adaptive? Comparing Interface Scaffolding Methods in an Intelligent Tutoring System. Learning@Scale: The Seventh Annual Workshop on A/B Testing and Platform-Enabled Learning Engineering. https://tail.cc.gatech.edu/files/reddig-pele-2026.pdf

Reddig, J. M., Moon, S., Crutcher, K., & MacLellan, C. J. (2026). AI Unplugged: Embodied Interactions for AI Literacy in Higher Education. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 47, pp. 40679-40687). https://tail.cc.gatech.edu/files/reddig-eaai-2026.pdf

Reddig, J., Smith, G., Siyahrood, S. A., Morris, W., Bae, Y., Crutcher, K., Kos, J., Dass, R., Kim, J., Siddiqui, M., Weitekamp, D.,  Thajchayapong, P., Kakar, S., Endert, A.,  Crossley, S., Kim, M. K., Dede, C., Goel, A., and MacLellan, C. (2026). Guidelines for Designing AI Technologies to Support Adult Learning. In Proceedings of the 2026 Designing Interactive Systems Conference (DIS ’26). Association for Computing Machinery, New York, NY, USA, 2474–2496. https://doi.org/10.1145/3800645.3813102

Thajchayapong, P., Carbonaro, S., Couper, T., Helmick, B., Rugaber, S., & Goel, A. (2026). Evolution of A4L: A Data Architecture for AI-Augmented Learning. Generative AI Risks and Benefits within Human-Machine Teams, edited by William Lawless and Marco Brambilla, Elsevier, 2026, pp. 73–91. https://aialoe.org/wp-content/uploads/2026/10/Partial-A4L2.0-Book-Chapter-Published.pdf

Wang M, Dede C, Lee CJ, Li X (2026) Comparative policy regimes of AI pedagogy: state-platform, market-ecosystem and rights-regulation. Education Innovations: Systems and Future Learning, Vol. 1 No. 1 pp. 428–444, doi: https://doi.org/10.1108/EISFL-03-2026-0026

Wang, L., Gao, Y., Anyi, C. L., & Goel, A. (2026). Futuring Social Assemblages: How Enmeshing AIs into Social Life Challenges the Individual and the Interpersonal. arXiv.org. https://arxiv.org/abs/2602.03958

Weitekamp, D., Smith, G., Koedinger, K., MacLellan, C. (2026) STAND: Self-Aware Precondition Induction for Interactive Task Learning. Proceedings of the Forty-Third International Conference on Machine Learning. https://arxiv.org/abs/2409.07653

An, S., Hammock, J., Goel, A. (2025). How Online Learners Engage in Self-Directed Modeling: A Behavioral Analysis. https://link.springer.com/article/10.1007/s40593-025-00506-5

Basappa, R., Tekman, M., Lu, H., Faught, B., Kakar, S., & Goel, A. K. (2025). Self-explanation in social AI agents. arXiv.org. https://arxiv.org/abs/2501.13945

Basu, S., Brown, J., Lum, C., Park, J., & Goel, A. (2025). Bidirectional Feedback-Based Personalization of Learning using Multi-tier AI: A Real-World Assessment of its Efficacy in Classrooms. In Procs. AAAI Spring Symposium on Current and Future Varieties of Human-AI Collaborations. https://dilab.gatech.edu/test/wp-content/uploads/2025/06/Bidirectional-Feedback-Based-Personalization-of-Learning-using-Multi-tier-AI-A-Real-World-Assessment-of-its-Efficacy-in-Classrooms.pdf

Buckley, S., Kos, J., Dass, R., Teng, C., Eaton, K., Zhang, S., Goel, A. (2025) A Personalized AI Coach to Assist in Self-Directed Learning. In Proc. at DAI, March 2025. https://dilab.gatech.edu/test/wp-content/uploads/2025/02/A-Personalized-AI-Coach-to-Assist-in-Self-Directed-Learning-1.pdf

Cao, L., Etemadi, A., Wheeler, M., & Dede, C. (2025). Keeping the “glass box” transparent: Comparing expert and AI-generated ratings and feedback in stealth assessment for judgement-focused negotiation simulations. Journal of Research on Technology in Education, 1–23. https://doi.org/10.1080/15391523.2025.2568526

Crossley, S. A., Kim, M., Wan, Q., Allen, L. K., Tywoniw, R., & McNamara, D. (2025). Assessing writing quality using crowdsourced non-expert comparative judgement ratings. Assessment in Education: Principles, Policy & Practice, 32(1), 33–59. https://doi.org/10.1080/0969594X.2025.2467664

Dass, R., Bowlin, T., Li, Z., Jin, X., & Goel, A. (2025). Improving Procedural Skill Explanations via Constrained Generation: A Symbolic-LLM Hybrid Architecture. arXiv preprint arXiv:2511.20942. https://arxiv.org/pdf/2511.20942

Dass, R. K., Madhusudhana, R., Deye, E., Verma, S., Bydlon, T., Brazil, G., & Goel, A. (2025) Ivy: A Hybrid Knowledge-Based and Generative AI Coach for Explaining Procedural Skills. In International Conference on Artificial Intelligence in Education (pp. 233-246). Cham: Springer Nature Switzerland. https://link.springer.com/chapter/10.1007/978-3-031-98417-4_17

Dass, R. K., Madhusudhana, R. H., Deye, E. C., Verma, S., Bydlon, T. A., Brazil, G., & Goel, A. K. (2025). Enhanced question-answering for skill-based learning using knowledge-based AI and Generative AI. arXiv.org. https://arxiv.org/abs/2504.07463

Fryer, K., Thajchayapong, P., & Goel, A. (2025) AI Adoption in Education: How Cognitive, Motivational, and Demographic Factors Shape Help-Seeking with a Virtual Teaching Assistant. In Proc. EDULEARN25.  https://dilab.gatech.edu/test/wp-content/uploads/2025/04/FRYER2025AIA.pdf

Goel, A., Thajchayapong, P., Nandan, V., Sikka, H., & Rugaber, S. (2025) AI in Learning and Education: The Need for a Data Architecture. Online Higher Education Administration: Strategies for Maximizing Returns and Improving Learning Outcomes, Kathleen S. Ives, Ray Schroeder, and Marie Cini (editors), Routledge Press.

Golnoush, H.,  Panzade, P., Takabi, D., & Kim, M. (2025). Problem-centered post-secondary computer science education: A study of the private artificial intelligence curriculum. International Journal of Technology in Education (IJTE), 8(2), 1-26. https://doi.org/10.46328/ijte.1071 

Gupta, A., & MacLellan, C. (2025). Intelligent tutors beyond K-12: An observational study of adult learner engagement and academic impact. arXiv.org. https://arxiv.org/abs/2502.16613

Gupta, A., Reddig, J., Calo, T., Weitekamp, D., & MacLellan, C. J. (2025). Beyond final answers: Evaluating large language models for math tutoring. arXiv.org. https://arxiv.org/abs/2503.16460

Haddadian, G., Han, H., Kim, J., Abdeen, M. S., & Kim, M. K. (2025). Exploring Ai-Generated Expert Models: Instructor Interaction and Learner Perceptions in a Physics Class. In Rajala, A., Cortez, A., Hofmann, R., Jornet, A., Lotz-Sisitka, H., & Markauskaite, L. (Eds.), Proceedings of the 19th International Conference of the Learning Sciences – ICLS 2025 (pp. 1684-1688). International Society of the Learning Sciences. https://doi.org/10.22318/icls2025.213524

Haddadian, G., Kim, M. K., & Haddadian, N. (2025). A Systematic Review of Automated Writing Evaluation Tools in Argumentative Writing for English as Foreign Language Education. Research Synthesis in Applied Linguistics, 1–51. https://doi.org/10.1080/29984475.2025.2598266

Im, S., Kim, M. K., & Heo, J. (2025). Digital Literacy in Physical Education: A Systematic Review of Frameworks and Development Areas. In Rajala, A., Cortez, A., Hofmann, H., Jornet, A., Lotz-Sisitka, H., & Markauskaite, L. (Eds.), Proceedings of the 19th International Conference of the Learning Sciences – ICLS 2025 (pp. 2883-2885). International Society of the Learning Sciences. https://doi.org/10.22318/icls2025.395909

J. C. -K. Lee, C. Dede, M. Wang and X. Li. (2025) Building Trust in AI Through Dialogues With Eastern Ethics: Toward Ethical Partnerships in Education,” in IEEE Transactions on Learning Technologies, vol. 18, pp. 833-841, 2025, doi: 10.1109/TLT.2025.3604616.

Kim, J., Galindo-Parra, M. F., Kim, S., Bae, Y., Hendry, T. M., Galen, S. S., & Kim, M. K. (2025). Design-Based Research for Scenario-Based, Generative Ai-Augmented Simulation in Nursing Education. In Rajala, A., Cortez, A., Hofmann, R., Jornet, A., Lotz-Sisitka, H., & Markauskaite, L. (Eds.), Proceedings of the 19th International Conference of the Learning Sciences – ICLS 2025 (pp. 2801-2803). International Society of the Learning Sciences. https://doi.org/10.22318/icls2025.571004

Kos, J., Eaton, K., Zhang, S., Dass, R., Goel, A. (2025) Using Comparative Machine Learning Methods to Validate Educational Content. In Proc. at iRaise AAAI 2025. https://dilab.gatech.edu/test/wp-content/uploads/2025/02/Using-Comparative-Machine-Learning-Methods-to-Validate-Educational-Content.pdf

Kos, J., Singh, R., Lum, C., & Goel, A. (2025). Scaffolding Learners’ Ecological Domain Knowledge to Support Inquiry Based Modeling. In Proc. EDULEARN25. https://dilab.gatech.edu/test/wp-content/uploads/2025/04/KOS2025SCA.pdf

Kos, J., Thajchayapong, P., Fryer, M., Lu, H., Lyndgaard, S., & Goel, A. (2025). Effects of Personality on help seeking in Online Learning Environments. To appear in arXiv.

Lum, C., Deye, E., Brazil, G., Bydlon, T., Verma, S., Madhusudhana, R., Dass, R., & Goel, A. (2025, June). Designing an AI coaching system for interactive video-based skill learning. In International Conference on Intelligent Tutoring Systems (pp. 281-291). Cham: Springer Nature Switzerland. https://link.springer.com/chapter/10.1007/978-3-031-98281-1_23

Lum, C., Deye, E., Brazil, G., Bydlon, T., Verma, S., Madhsudhana, R., Dass, R., & Goel, A. (2025). Evolving AI coaching: Designing an interactive tutoring system based on learner inquiries in online learning. To appear in the Proceedings of the 21st International Conference on Intelligent Tutoring Systems (ITS).  

MacLellan, C. J. (2025). Model Human Learners: Computational Models to Guide Instructional Design. In Proceedings of the 47th Annual Conference of the Cognitive Science Society. https://doi.org/10.48550/arXiv.2502.02456

MacLellan, C., Smith, G. (2025) L.E.A.R.N.: A Hybrid Architecture For Language-Guided Induction
of Hierarchical Task Networks. Proceedings of the Twelfth Annual Conference on Advances in Cognitive Systems. https://openreview.net/pdf?id=aKJl1p8hWb

Maiti, Pratyusha, Goel, Ashok (2025). Can an AI Partner Empower Learners To Ask Critical Questions? https://dl.acm.org/doi/10.1145/3708359.3712134

Miranda, M., Islam, M., Basappa, R., Taylor, T., Goel, A. (2025) Adaptable Social AI Agents. In Proc. Association for the Advancement of Artificial Intelligence. https://dilab.gatech.edu/test/wp-content/uploads/2025/03/Adaptable-Social-AI-Agents.pd

Morris, W., Crossley, S., Holmes, L., & Choi, J. (2025). Distinguishing effective writing styles in the PERSUADE corpus. Journal of Writing Research. https://www.jowr.org/jowr/article/view/1491

Noroozi, O., Haddadian, G., Gao, X. et al. (2025) The value of GenAI for peer feedback provision: student perceptions and impacts. Int J Educ Technol High Educ 22, 61. https://doi.org/10.1186/s41239-025-00558-6

Olson, A., Maiti, P., & Goel, A. (2025). The textbook of tomorrow: Rethinking course material interfacing in the era of GPT. arXiv.org. https://arxiv.org/abs/2501.03618

Ou, C., Joyner, D. (2025) The Dual role of AI in online project-based learning at scale. In Proceedings of Learning at Scale Conference, July 2025. https://doi.org/10.1145/3698205.3733950

Ou, C., Joyner, D. (2026) Assess or discuss: Comparing peer assessment and online discussion for enhancing learning at scale. In Proceedings of Learning at Scale Conference, July 2025. https://doi.org/10.1145/3698205.3733927

Park, J., &  Goel, A. (2025). Human-Centric Teaching at Scale in Online Education through Bidirectional Feedback in Human-AI Interaction. In Procs. AAAI Spring Symposium on Current and Future Varieties of Human-AI Collaborations. https://dilab.gatech.edu/test/wp-content/uploads/2025/06/Human-Centric-Teaching-at-Scale-in-Online-Education-through-Bidirectional-Feedback-in-Human-AI-Interaction.pdf

Rahimi, S., Dede, C., Esmaeiligoujar, S., & Babaee, M. (in press). Augmenting human creativity with responsible and ethical use of generative AI. In M. Worwood & J. C. Kaufman (Eds.), Generative artificial intelligence and creativity: Possibilities, precautions, and perspectives. Academic Press. https://doi.org/10.1016/B978-0-443-34073-4.00010-1

Reddig, J., Arora, A., MacLellan, C. (2025) Generating In-Context, Personalized Feedback for Intelligent Tutors with Large Language Models. International Journal of Artificial Intelligence in Education, vol. 35, pp. 3459-3500. https://doi.org/10.1007/s40593-025-00505-6

Santana, J.A., Thajchayapong, P., Rugaber, S., & Goel, A. (2025) A Highly Configurable Data Analytics Pipeline for Learning. In Proc. EDULEARN25. https://dilab.gatech.edu/test/wp-content/uploads/2025/04/SANTANA2025AHI.pdf

Storey, R., Lyndgaard, S. F., & Kanfer, R. (2025). The future of work and learning hinges on social and relational processes. Industrial and Organizational Psychology, 18(1), 128–133. doi:10.1017/iop.2024.68

Taneja, K., & Goel, A. K. (2025). MuDoC: An interactive multimodal document-grounded conversational AI system. arXiv.org. https://arxiv.org/abs/2502.09843

Taneja, K., Singh, A., & Goel, A. (2025) Towards a Multimodal Document-grounded Conversational AI System for Education. In Proc. 26th International Conference on Artificial Intelligence in Education 2025.  https://www.researchgate.net/publication/390990210_Towards_a_Multimodal_Document-grounded_Conversational_AI_System_for_Education

Thajchayapong, P., Carbonaro, S., Couper, T., Helmick, B., Rugaber, S., & Goel, A. (2025). Evolution of A4L: A data architecture for AI-Augmented Learning. arXiv.org. https://arxiv.org/abs/2511.11877

Thajchayapong, P., &  Goel, A. (2025) Personalized Learning through AI-Driven Data Pipeline. In Procs. AAAI Spring Symposium on Current and Future Varieties of Human-AI Collaborations. https://dilab.gatech.edu/test/wp-content/uploads/2025/06/Personalized-learning-through-ai-driven-data-pipeline.pdf

Tian, Y., Crossley, S., & Van Waes, L. (2025). The KLiCKe corpus: Keystroke logging in compositions for knowledge evaluation. Journal of Writing Research. https://www.jowr.org/jowr/article/view/1556

Weitekamp, D. & MacLellan, C.J. (2025). Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency. In Proceedings of the 47th Annual Conference of the Cognitive Science Society. https://arxiv.org/abs/2505.10422

Wójcik, J., Thajchayapong, P., Harbison, B., Nandan, V., Tai, H.W., & Goel, A. (2025) Measuring the Impact of AI Social Agents on Belonging in Learning Communities. In Proc. EDULEARN25. https://dilab.gatech.edu/test/wp-content/uploads/2025/04/WOJCIK2025MEA.pdf

Zappatore, M., Wang, M., Dede, C., Li, X. (2025) Guest Editorial: Special Issue Intelligence Augmentation and the Future of Education: Transforming Learning Landscapes Across Modalities and Lifecycles,” in IEEE Transactions on Learning Technologies, vol. 18, pp. 1112-1115, 2025, doi: 10.1109/TLT.2025.3639317

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

Dede, C. (2024). External strategies for improving national education systems: policies and implementation practices by which The Digital School is achieving scale. Rivista di Digital Politics (2/2024), 319-342, https://doi.org/10.53227/115057 

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

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