I am a tenure-track Junior (=Assistant) Professor of Technology-Enhanced Learning in the Department of Computer Science at Saarland University in Germany. I am also a Faculty Associate at Harvard University’s Berkman Klein Center for Internet & Society in the US. I direct the interdisciplinary LaLa Lab (“Learning to Adapt, Learning with Agency Lab”), where we explore–together with community members (such as teachers)–how digital environments can be architected to foster human agency, metacognition, and deep conceptual understanding.
My recent research focuses on Human-Centered AI in the context of learning – specifically, how to co-design adaptive systems that foster human agency and metacognition. As a Learning Scientist and HCI researcher, I combine expertise in the learning sciences, design research, interaction design, and data-driven informatics to build systems.
Current research topics include:
- Designing for Learner Agency: Investigating how different forms of AI support can help students self-regulated and avoid over-reliance on AI in during learning.
- LS Principles for Effective Human-AI Interactions: Designing and testing various scaffolding approaches, friction, and reflective interactions to help humans work with AI more effectively.
- Metacognitive Modeling: Using interaction data to analyze learners’ decision-making patterns, with the goal of developing systems that promote self-regulated learning behaviors.
Affective Computing in Learning: Exploring how AI-based affective interventions can be designed to effectively support cognitive and affective aspects of learning.
Co-Design with Stakeholders: Engaging in participatory research with teachers and students to ensure that intelligent tutors are designed responsibly and that they align with classroom-based pedagogical practices and social context.
I received my Ph.D. in Human-Computer Interaction from Carnegie Mellon University’s HCI Institute in the US in 2022, working with Dr. Vincent Aleven. Prior to attending CMU, I completed my Master’s degree in Learning, Design, and Technology at Stanford Graduate School of Education, working with Dr. Candace Thille.
I am also an activist working in the area of open education as a member of Creative Commons Japan and as an OER Research Fellow at Open Education Group.
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I currently do not have funding for a PhD position, but if you are interested in securing a scholarship [e.g., DAAD] and join my lab, please contact me via email with your CV/Resume, a letter of motivation, and most recent transcript.
Thesis students [see here]
Research
Recent Publications
- 🆕 Nagashima, T., Siegrist, L., Scholz, N., Sato, S., Vincoli, M., & Su, M. (2026). Identifying alignments and misalignments between teachers’ and students’ views on AI use in the school classroom. In Proceedings of the ACM on Human-Computer Interaction (PACMHCI). CSCW2026.
- 🆕 Vincoli, M., Scholz, N., & Nagashima, T. (2026). “I think I would rather decide what to do”: Students’ perception of control in an AI-infused classroom. In Proceedings of the 21st European Conference on Technology Enhanced Learning (EC-TEL2026), Valencia, Spain. [acceptance rate: 26.0%].
- 🆕 Rief, V., Hladký, M., Yoo, M., Heel, S., Sato, S., & Nagashima, T. (2026). Conversational AI meets mindfulness: Exploring LLMs as a socio‑emotional layer in a math intelligent tutoring system. In Proceedings of the 21st European Conference on Technology Enhanced Learning (EC-TEL2026), Valencia, Spain.
- 🆕 Nagashima, T., Hladký, M., & Rief, V. (2026). Warning about AI fallibility increases help-seeking in a math intelligent tutor. In Proceedings of the 21st European Conference on Technology Enhanced Learning (EC-TEL2026), Valencia, Spain. [link]
- 🆕 Borchers, C., Zhang, L., Yang, K., & Nagashima, T., & Dominigue, B. (2026). Understanding student effort using reaction time propensities during problem solving at scale. In Proceedings of the ACM Conference on Learning at Scale (L@S2026), Seoul, South Korea. [acceptance rate: 22.0%]. [link]
- 🆕 Nagashima, T., Sato, S., Hladký, M., Scholz, N., & Siegrist, L. (2026). Teachers’ perspectives on decision-making in AI-supported classrooms: A cross-cultural study of Germany and Japan. In Proceedings of the International Conference on Artificial Intelligence in Education (AIED2026), Seoul, South Korea. [acceptance rate: 16.7%]. [link]
- 🆕 Sato, S., Cutumisu, M., & Nagashima, T. (2026). A systematic review of empirical studies on Intelligent Tutoring System feedback in K-12 Classrooms. In Proceedings of the International Conference on Artificial Intelligence in Education (AIED2026), Seoul, South Korea [acceptance rate: 28.6%] [link]
- 🆕 Nair, A., Su, M., & Nagashima, T. (2026). Investigating human-AI agency in interactive simulations for complex systems education: The effects of questioning and content agency on student learning. In Proceedings of the Annual Meeting for the International Society of the Learning Sciences (ISLS2026), Irvine, CA.
- 🆕 Sato, S. & Nagashima, T. (2026). Understanding teachers’ feedback strategies in classrooms with an intelligent tutor: A survey study. In Proceedings of the Annual Meeting for the International Society of the Learning Sciences (ISLS2026), Irvine, CA.
- 🆕 Han, B., Coghlan, S., Buchanan, G., & McKay, D. (2026). Ethical gaps and power dynamics in decision-making about AI adoption: The case of AI for monitoring student learning in education. International Journal of Human-Computer Studies, 103810. (*work conducted prior to joining the group) [link]
- 🆕 Han, B., Coghlan, S., Buchanan, G., & McKay, D. (2026). The values of academic integrity in the era of monitoring AI in classrooms. In Artificial Intelligence and Academic Integrity: Navigating Ethical Challenges of AI in Education (pp. 141-157). Singapore: Springer Nature Singapore. (*work conducted prior to joining the group) [link]
- 🆕 Han, B., Ma, Y., Coghlan, S., McKay, D., Buchanan, G., & Smith, W. (2026). AI sensing and intervention in higher education: Student perceptions of learning impacts, affective responses, and ethical priorities. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (pp. 1-21). Best Paper Award (*work conducted prior to joining the group) [link]
- 🆕 Reinwarth, A. L., Balzert, E., Hilpert, B., Hladký, M., Gebhard, P., & Schneeberger, T. (2026). TACSIA: A Framework for Passenger Expectations on Trust in Autonomous Cars with Socially Interactive Agents. In Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (pp. 1-6). [link]
- 🆕 Su, M., Chi, M. T., & Nagashima, T. (2026). Applying the PAIR-C Framework to foster deep understanding and address misconceptions in science education. Journal of the Learning Sciences, 35(1), 130–175. [link]