This page provides a comprehensive record of software systems, funded projects, and scholarly outputs produced by AIEDU Lab members and collaborators.

Software and Systems

  • Lin, M. P. C., Chang, D. H., Yang, D., Ho, M., Winne, P. H., & Ryoo, J. (2025). MetaMentorAI [Computer software]. https://MetaMentorAI.online.
  • Chang, D. H., Lin, M. P. C., & Ryoo, J. (in progress). Re-Map [Computer software in development].
  • Lin, M. P. C., Chang, D. H., & Winne, P. H. (2019). DD Chatbot: Intelligent Writing Support System [Computer software].

Funded Research Projects

  1. Insight Grant (IG)
    Social Sciences and Humanities Research Council of Canada (SSHRC), $415,539
    Topic: Tracing Self-Regulated Learning in Computer Science Education with an AI-Enhanced Learning Tool (2026–2030)
    Team: Dr. Michael Lin (Mount Saint Vincent University, PI), Dr. Daniel Chang (SFU/AU, Co-Applicant), Dr. Maiga Chang (Athabasca University, Co-Applicant), Dr. Eric Pitras (Dalhousie University, Collaborator), Dr. Phil Winne (Simon Fraser University, Collaborator), Dr. Jeeho Ryoo (Fairleigh Dickinson University, Collaborator), Dr. Marco Ho (BCIT, Collaborator)
  2. Insight Development Grant (IDG)
    Social Sciences and Humanities Research Council of Canada (SSHRC), $70,724
    Topic: Evolving Pedagogy: Integrating Generative AI into Teaching Practices (2024–2026)
    Team: Dr. Michael Lin (Mount Saint Vincent University, PI), Dr. Daniel Chang (SFU, Co-Applicant), Dr. Eric Pitras (Dalhousie University, Co-Applicant), Dr. Phil Winne (Simon Fraser University, Collaborator), Dr. Oscar Lin (Athabasca University, Collaborator)
  3. Insight Development Grant (IDG)
    Social Sciences and Humanities Research Council of Canada (SSHRC), $74,962
    Topic: Collaborative Argument Visualization in Post-Secondary Education (2023–2025)
    Team: Dr. Daniel Chang (PI), Dr. John Nesbit (Co-Applicant), Dr. Qing Liu (Co-Applicant)

Full Team Publications

In Press and Accepted

  • Lin, M. P. C., Chang, D. H., Janarthanan, V., Hsiao, M., Ho, M., & Ryoo, J. (in press). Designing for self-regulated learning in AI-assisted quizzing: A classroom pilot study. International Journal of Emerging Technologies in Learning.
  • Huang, J. Y., Yap, S. Q., Lin, M. P. C., Chang, D., & Ryoo, J. (2026). Towards procedural transparency in the use of large language models for qualitative analysis: A systematic review in educational research. Accepted in ITS 2026.

2026

  • Lin, M. P. C., Chang, D. H., Lin, V., & Ryoo, J. (2026). Meta-analysis of argument visualization tools in higher education: Examining effects on student achievement and moderating factors. Technology, Knowledge and Learning, 1-28. [ESCI] [Link]
  • Lin, M. P. C., Huang, J. Y., Chang, D. H., Tembrevilla, G., Bowen, G. M., Poitras, E., Janarthanan, V., & Ryoo, J. (2026). Mapping open-source large language models in education: A narrative review of evidence, pedagogical roles, and learning outcomes. AI in Education, 2(1), 4. [Link]
  • Poitras, E., Durai, J. P., Yunus Raida, N. Z., Sachdeva, J., Doucette, K., & Lin, M. P. C. (2026). Data-informed decision making in introductory programming instruction. In R. Dhakal, W. Davis, & K. Heske (Eds.), Fostering Educational Culture for Student Success (pp. 305–336). IGI Global Scientific Publishing. [Link]
  • Lin, M. P. C., Chang, D. H., Huang, J. Y., Ho, M., & Ryoo, J. (2026). Question-posing as assessment in the age of GenAI: A CHAT-ACTS perspective. In Proceedings of EdMedia 2026 Edinburgh (pp. 1010-1015). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). [Link]
  • Lin, M. P. C., Chang, D. H., Huang, J. Y., & Ryoo, J. (2026). What counts as learning? Assessment design for GenAI in higher education. In Proceedings of EdMedia 2026 Edinburgh (pp. 1016-1020). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). [Link]
  • Lin, M. P. C., Chang, D. H., Huang, J. Y., Ho, M., & Ryoo, J. (2026). From use to design: A CHAT-ACTS lens on scaffolding generative AI for active learning and self-regulated learning in higher education. In Proceedings of EdMedia 2026 Edinburgh (pp. 164-172). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). [Link]
  • Lin, M. P. C., Poitras, E., Tembrevilla, G., Bowen, G. M., Lobczowski, N., Chang, D. H., Huang, J. Y., Lan, Y. F., & Ryoo, J. (2026). Amplifier or substitute? A rapid review of generative AI in higher education active learning tasks and self-regulated learning. In Proceedings of SITE 2026 (pp. 799–808). Association for the Advancement of Computing in Education (AACE). [Link]
  • Lin, M. P. C., Jhajj, G., Chang, D., Lin, F., Poitras, E., & Ryoo, J. (2026). Generative AI’s role in computer science classrooms: A rapid mapping review. The Washington DC Conference on Education. Washington, DC, USA: The International Academic Forum (IAFOR).
  • Lin, M. P. C., Liu, A. L., Saffari, S., & Chang, D. (2026). Mapping AI tools in education: A topic modeling analysis of cognitive, metacognitive, and affective insights. In A. Sifaleras & F. Lin (Eds.), Generative Intelligence and Intelligent Tutoring Systems (pp. 88–101). Springer Nature Switzerland. [Engineering Index] [Link]
  • Jhajj, G., Gustafson, J. R., Morland, R., Gutierrez, C. E., Lin, M. P. C., Dewan, M. A. A., & Lin, F. (2026). Neuromorphic knowledge representation: SNN-based relational inference and explainability in knowledge graphs. In S. Graf & A. Markos (Eds.), Generative Systems and Intelligent Tutoring Systems (pp. 159–165). Springer Nature Switzerland. [Engineering Index] [Link]

2025

  • Ballantyne, E., Lin, M. P. C., Chang, D. H., & Poitras, E. (2025). Bridging the gap: Leveraging CHAT-ACTS for equity and access in AI-driven higher education. Journal of Computing in Higher Education, 37, 1564–1589. [SSCI] [Link]
  • Ryoo, J., Lin, M. P. C., Rai, S., He, W., Park, S. M., & Ho, M. (2025). WIP: Multi-agent artificial intelligence model to enhance self-regulated learning and conceptual understanding in computer science education. 2025 IEEE Frontiers in Education Conference (FIE), 1–5. [Engineering Index] [Link]
  • Nguyen, J. C., Lin, M. P. C., & Chang, D. H. (2025, November 25). Navigating AI literacy in education: A scoping review of generative AI’s impact on writing, learning, and policy [Poster session]. The 17th Asian Conference on Education (ACE2025), Tokyo, Japan.
  • Chang, D. H., Lin, M. P. C., & Hwang, G. J. (2025). Charting the field: A review of argument visualization research for writing, learning, and reasoning. Frontiers in Education, 10. [ESCI] [Link]
  • Bowen, G. M., Lin, M. P. C., & Royce, C. A. (2025). Supplementing AI “curriculum” using Teachers-Pay-Teachers resources: What there is and what there isn’t. In S. Kadry (Ed.), Artificial Intelligence in Education: Creating an Equitable, Creative, and Effective Learning Environment. IntechOpen. [Link]
  • Lin, M. P. C., Lin, F., Lan, Y. F., & Ryoo, J. (2025). Perceiving Generative AI in Teacher Practice: A Design-Based Case Study in a Graduate Course. 2025 IEEE Smart World Congress (SWC), 271–274. [Engineering Index] [Link]
  • Ho, M., Orr, C., Jeon, R., Lin, M. P. C., & Ryoo, J. (2025). AI Literacy Through a Project-Based Learning Course. 2025 IEEE Smart World Congress (SWC), 283–288. [Engineering Index] [Link]
  • Poitras, E., Durai, J. P. S., Boisvert, J., Doucette, K., Lin, M. P. C., Kryven, M., & Sampangi, R. (2025). Clustering and Profiling Student Study Behaviors and Interactions with an AI Coding Assistant. 2025 IEEE Smart World Congress (SWC), 294–299. [Engineering Index] [Link]
  • Saffari S. & Lin, M. P. C. (2025). Repurposing Generative AI for Learning A Topic Modeling Approach Beyond EdTech. 2025 IEEE Smart World Congress (SWC), 321–326. [Engineering Index] [Link]
  • Park, S. M., Ho, M., Lin, M. P. C., & Ryoo, J. (2025, April). Evaluating the impact of assistive AI tools on learning outcomes and ethical considerations in programming education. In 2025 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–10). IEEE. [Engineering Index] [Link]
  • Hajian, S., Chang, D., Wang, Q., & Lin, M. P. C. (2025). Motivational theories in action: A guide for teaching artificial intelligence prompts to support student learning motivation. International Journal of Instruction, 18(4), 601–626. [ESCI] [Link]
  • Chang, D., Chen, Q., & Lin, A. M. Y. (2025). Translingual approach in assessing academic writing for emerging multilingual writers in EMI higher education. Linguistics and Education, 87, 101403. [SSCI]

2024

  • Lin, M. P. C., Liu, A. L., Poitras, E., Chang, M., & Chang, D. (2024). An exploratory study on the efficacy and inclusivity of AI technologies in diverse learning environments. Sustainability, 16(20), 8992. [SSCI] [Link]
  • Poitras, E., Crane, B. G., Dempsey, D., Bragg, T. A., Siegel, A. A., & Lin, M. P. C. (2024). Cognitive apprenticeship and artificial intelligence coding assistants. In C. Bosch, L. Goosen, & J. Chetty (Eds.), Navigating Computer Science Education in the 21st Century (pp. 261–281). IGI Global Scientific Publishing. [Link]
  • Lin, M. P. C., Chang, D., & Winne, P. H. (2024). A proposed methodology for investigating student-chatbot interaction patterns in giving peer feedback. Educational Technology Research and Development, 1–34. [SSCI] [Link]
  • Nesbit, J., Liu, Q., Sharp, J., Cukierman, D., Hendrigan, H., Chang, D., Shahabi, B., Deng, Q., & Pakdaman Savoji, A. (2024). Argument visualization with DMaps: Cases from postsecondary learning. Journal of Interactive Learning Research, 35(2), 223–253.
  • Lin, M. P. C., & Chang, D. (2024). Exploring inclusivity in AI education: Perceptions and pathways for diverse learners. In A. Sifaleras & F. Lin (Eds.), Generative Intelligence and Intelligent Tutoring Systems (pp. 237–249). Springer Nature Switzerland. [Engineering Index]
  • Lin, M. P. C., Chang, D., Hall, S., & Jhajj, G. (2024). Preliminary systematic review of open-source large language models in education. In A. Sifaleras & F. Lin (Eds.), Generative Intelligence and Intelligent Tutoring Systems (pp. 68–77). Springer Nature Switzerland. [Engineering Index]

2023 and Earlier

  • Chang, D., Lin, M. P. C., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), 12921. [SSCI] [Link]
  • Lin, M. P. C., & Chang, D. (2023). CHAT-ACTS: A pedagogical framework for personalized chatbot to enhance active learning and self-regulated learning. Computers and Education: Artificial Intelligence, 1–8. [ESCI] [Link]
  • Raković, M., Winne, P. H., Marzouk, Z., & Chang, D. (2021). Automatic identification of knowledge-transforming content in argument essays developed from multiple sources. Journal of Computer Assisted Learning, 1–22. [SSCI]
  • Lin, M. P. C., & Chang, D. (2020). Enhancing post-secondary writers’ writing skills with a chatbot: A mixed-method classroom study. Educational Technology & Society, 23(1), 78–92. [SSCI] [Link]

Preprints and Works in Progress

  • Lin, M. P. C., Huang, J. Y., Chang, D., Tembrevilla, G., Bowen, G. M., Poitras, E., Janarthanan, V., & Ryoo, J. (2025). Mapping open-source large language models in education: A review of evidence, pedagogical roles, and learning outcomes [Preprint]. Available at SSRN 6079527. [Link]
  • Lin, M. P. C., Chang, D., Lin, V., & Ryoo, J. (2025). Meta-analysis of argument visualization tools in higher education: Examining effects on student achievement and moderating factors [Preprint]. Available at SSRN 5643672. [Link]
  • Lin, M. P. C., Jhajj, G., Lin, F., Poitras, E., Chang, D., & Ryoo, J. (2025, September 11). Generative AI’s influence in computer science classrooms: A rapid review methodology [Preprint]. EdArXiv. [Link]