Research Publications and Presentations
Datta, D., Bywater, J. P., Phillips, M., Lilly, S., Chiu, J. L., Watson, G. S., & Brown, D. E. (2023). Classifying Mathematics Teacher Questions to Support Mathematical Discourse. In International Conference on Artificial Intelligence in Education (pp. 372-377). Cham: Springer Nature Switzerland.
Bywater, J.P. (2023, July). The Mathematical Discourse Teaching Simulator (MDTSim). Poster presented at the Artificial Intelligence in Education (AIED) Conference, Tokyo, Japan.
Bywater, J., Chiu, J. L., Adewole, S., Watson, G., & Brown, D. (2023, April). Developing an artificial intelligence simulator to support mathematics teacher questioning: The A.I. classroom teaching simulator (ACTS). Annual meeting of the American Educational Research Association, Chicago, IL.
Chiu, J. L., Bywater, J., & Lilly, S. (2022). The role of AI to support teacher learning and practice: A review and future directions. In A. Alavi, P. Jiao, B. McLaren, & F. Ouyang (Eds.), Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology (pp. 163-174). Auerbach/CRC Press.
Datta, D., Phillips, M., Bywater, J., Lillly, S., Chiu, J. L., Watson, G., & Brown, D. (2022). Human-in-the-Loop Data Collection and Evaluation for Improving Mathematical Conversations. In Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://10.1007/978-3-031-11647-6_113
Phillips, M. (2022). Moving the dialogue forward: Virtual student conversational agent design in low data environments. Doctoral Dissertation, University of Virginia.
Datta, D., Phillips, M., Bywater, J. P., Chiu, J. L., Watson, G. S., Barnes, L., & Brown, D. (2021). Virtual Pre-Service Teacher Assessment and Feedback via Conversational Agents. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 185-198).
Bywater, J., Chiu, J. L., Lilly, S., Datta, D., Phillips, M., Watson, G., & Brown, D. (2022, April). Calibrating an automated rater for use with a validated observational measure of teacher questioning. Annual meeting of the American Educational Research Association, San Diego, CA.
Datta, D., Phillips, M., Bywater, J. P., Chiu, J. L., Watson, G. S., Barnes, L., & Brown D. E. (2021, Dec). Evaluation of mathematical questioning strategies using weak supervision. Paper presented to the Math AI for Education workshop at the Neural Information Processing Systems Conference, Virtual.
Datta, D., Phillips, M., Bywater, J. P., Chiu, J. L., Watson, G. S., Barnes, L., & Brown D. E. (2021, Dec). Improving mathematical questioning in teacher training. Paper presented to the Human Centered AI workshop at the Neural Information Processing Systems Conference, Virtual.
Phillips, M. (2021, June). Leveraging unstructured text within the context of conversational agents. Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, 308-314.
Bywater, J., Datta, D., Phillips, M., Watson, G., Lilly, S., Brown, D., & Chiu, J. L. (2021, April). Deep learning approaches to classifying teacher questions within the AI-based classroom simulation (ACTS). Annual meeting of the American Educational Research Association. Virtual conference.