Selected Publications

📖 Ethics, Bias & Responsible AI

  • Berson, I.R., Berson, M.J., & Luo, W. (2025). Innovating responsibly: Ethical considerations for AI in early childhood education. AI Brain Child, 1(2).
  • *J. Lee, T. Shang, D. Duong-tran, S. Yang, L. Li, S. Li.** (2025). An Investigation of large language models in clinical triage: Promising capabilities, persistent racial and gender biases. AAAI 2025 Workshop on Generative AI for Health, Philadelphia, PA.
  • Mahammed Kamruzzaman, Md. Shovon, & Gene Kim. (2024). Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models. ACL 2024.
  • Parush Gera & Tempestt Neal. (2022). A Comparative Analysis of Stance Detection Approaches and Datasets. ACL Workshop on NLP Evaluation.

📖 AI in Health, Crisis Response & Simulation

  • H. Yu, J. Zhou, L. Li, T.L. Assimes, X. Ma, D. Bitterman, L. Fan. (2025). AIPatient: Simulating patients with EHRs and LLM powered agentic workflow. AAAI 2025 Workshop on Advancing LLM-based Multi-Agent Collaboration, Philadelphia, PA.
  • Zitu MM, Le TD, Duong T, et al. (2024). Large language models in cancer: potentials, risks, and safeguards. BJR Artificial Intelligence.
  • Dinh, L., & Walczak, S. (2025). Linguistic patterns in social media content from crisis and non-crisis zones: A case study of Hurricane Ian. [Information Processing & Management, 62(3), 104061].
  • Walczak, S., & Dinh, L. (2025). A Text Mining Analytic Approach for Distinguishing Between Disaster and Non-Disaster Zones from Tweets. [International Journal of Disaster Risk Reduction, 105233].
  • Dinh, L., Yang, P., & Diesner, J. (2024). From plan to practice: Interorganizational crisis response networks from governmental guidelines and real‐world collaborations during hurricane events. [Journal of Contingencies and Crisis Management, 32(3), e12601].
  • Thieu T, Maldonado JC, Ho PS, et al. (2021). A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling. International Journal of Medical Informatics.

📖 AI in Early Childhood & Education

  • Luo, W., He, H., Liu, J., et al. (2023). Aladdin’s Genie or Pandora’s Box for Early Childhood Education? Experts Chat on the Roles, Challenges, and Developments of ChatGPT. Early Education and Development.
  • Berson, I.R., & Berson, M.J. (2024). Fragments of the past: The intersection of AI, historical imagery, and early childhood creativity. Future in Educational Research.
  • Berson, I.R., Berson, M.J., McKinnon, C., et al. (2023). An exploration of robot programming as a foundation for spatial reasoning and computational thinking in preschoolers’ guided play. Early Childhood Research Quarterly.
  • Berson, I.R., Berson, M.J., Connors, B.C., et al. (2023). Using mixed reality to create multimodal learning experiences for early childhood. In Bridging the XR Technology-to-Practice Gap, Vol. 2.

📖 Multimodal Learning & Literacy

  • King, J.R., Burger, L., & Schneider, J.J. (in press). Second-order multimodal discourse synthesis: How ideas become embodied actions of writing teachers as human-centered designers. In International Handbook of Research in Digital Literacies, Routledge.
  • Burger, L., Schneider, J.J., & King, J.R. (2024, Dec). AI Content Creation and Multimodal Text Production: Composing Strategies of Black Youth in a VR Gaming Club. Presented at the Annual Conference of the Literacy Research Association, Atlanta, GA.

📖 Network Dynamics, Reasoning & Learning Analytics

  • L. Fan, W. Hua, L. Li, H. Ling, Y. Zhang, L. Hemphill. (2024). NPHardEval: Dynamic benchmark on reasoning ability of large language models via complexity classes. [ACL], Bangkok, Thailand.
  • S. Lin, W. Hua, L. Li, C. Chang, L. Fan, J. Ji, H. Hua, J. Luo, Y. Zhang. (2024). BattleAgent: Multi-modal dynamic emulation on historical battles to complement historical analysis. [EMNLP], Miami, FL.
  • Dinh, L., Friedman, A., & Hawley, K. (2024). Examining peer review network dynamics in higher education visual communication courses using ERGM. [Computers and Education Open, 7, 100222].
  • Cheng, Y. Y., & Dinh, L. (2025). An experiment on the impact of relation types towards taxonomy alignment problems. [Information Processing & Management, 62(3), 104036].