Noboru Matsuda

Beyond prior knowledge: the predictive role of knowledge-building in tutor learning

Ameen, F., Shahriar, T., Mallavarapu, A., Jiang, S., & Matsuda, N. (2026, February 17). Behaviormetrika, Vol. 2.

From Tool to Partner: Exploring the Roles of Embodiment on AI Agent in Pair Programming

Yang, X., Zhan, Y., Matsuda, N., & Jin, Q. (2025, October 7).

“I Am Confused! How to Differentiate Between…?” Adaptive Follow-Up Questions Facilitate Tutor Learning with Effective Time-On-Task

Shahriar, T., & Matsuda, N. (2024, January 1). Lecture Notes in Computer Science, Vol. 14830, pp. 17–30.

Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives

Shimmei, M., Bier, N., & Matsuda, N. (2023, January 1). Lecture Notes in Computer Science, Vol. 13916, pp. 3–15.

What and How You Explain Matters: Inquisitive Teachable Agent Scaffolds Knowledge-Building for Tutor Learning

Shahriar, T., & Matsuda, N. (2023, January 1). Lecture Notes in Computer Science, Vol. 13916, pp. 126–138.

Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium

In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Lecture notes in computer science.

Teaching How to Teach Promotes Learning by Teaching

Matsuda, N., Lv, D., & Zheng, G. (2022, August 31). International Journal of Artificial Intelligence in Education, Vol. 8.

Learning Association Between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions

Shimmei, M., & Matsuda, N. (2021, January 1). Lecture Notes in Computer Science, pp. 320–324.

Teachable Agent as an Interactive Tool for Cognitive Task Analysis: A Case Study for Authoring an Expert Model

Matsuda, N. (2021, July 12). International Journal of Artificial Intelligence in Education, Vol. 7, pp. 48–75.

“Can You Clarify What You Said?”: Studying the Impact of Tutee Agents’ Follow-Up Questions on Tutors’ Learning

Shahriar, T., & Matsuda, N. (2021, January 1). Lecture Notes in Computer Science.

Development and validation of the teachers’ digital learning identity survey

Zimmer, W. K., McTigue, E. M., & Matsuda, N. (2020, December 1). International Journal of Educational Research, Vol. 105.

The Effect of Metacognitive Scaffolding for Learning by Teaching a Teachable Agent

International Journal of Artificial Intelligence in Education, 30(1), 1–37.

Applications of Reinforcement Learning to Self-Improving Educational Systems

In A. M. Sinatra, A. C. Graesser, X. Hu, K. Brawner, & V. Rus (Eds.), Design Recommendations for Intelligent Tutoring Systems: Vol. 7: Self-Improving Systems (pp. 77–96). Orlando, FL: US Army Research Lab.

Evidence-Based Recommendation for Content Improvement Using Reinforcement Learning

Shimmei, M., & Matsuda, N. (2019, January 1). Lecture Notes in Computer Science, pp. 369–373.

PASTEL: Evidence-based learning engineering method to create intelligent online textbook at scale

CEUR Workshop Proceedings, 2384, 70–80. http://www.scopus.com/inward/record.url?eid=2-s2.0-85067813785&partnerID=MN8TOARS

Metacognitive Scaffolding Amplifies the Effect of Learning by Teaching a Teachable Agent

Matsuda, N., Sekar, V. P. C., & Wall, N. (2018, January 1). Lecture Notes in Computer Science, pp. 311–323.

The State-of-the-Art Pedagogical Agent Technology in the Field of Learning Science

Transactions of Japanese Society for Information and Systems in Education, 35(1), 13–20. Retrieved from

Using Design Patterns for Math Preservice Teacher Education

Inventado, P. S., Inventado, S. G. F., Matsuda, N., Li, Y., Scupelli, P., Ostrow, K., … McGuire, P. (2018, July 4). ACM International Conference Proceeding Series.

Instructional Strategy

In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (pp. 1157–1159). Tokyo, Japan: Japan Society of Artificial Intelligence.

Intelligent Pedagogical Agents

In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (pp. 1152–1153). Tokyo, Japan: Japan Society of Artificial Intelligence.

Investigating the effects of cognitive and metacognitive scaffolding on learners using a learning by teaching environment

Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings, 1–10. http://www.scopus.com/inward/record.url?eid=2-s2.0-85051544348&partnerID=MN8TOARS

Natural language processing in educational systems

In H. Matsubara (Ed.), Encyclopedia of Artificial Intelligence (p. 1101). Tokyo, Japan: Society of Artificial Intelligence.

Regional Cultural Differences in How Students Customize Their Avatars in Technology-Enhanced Learning

Yarzebinski, E., Dumdumaya, C., Rodrigo, M. M. T., Matsuda, N., & Ogan, A. (2017, January 1). Lecture Notes in Computer Science, pp. 598–601.

Cognitive Tutors Produce Adaptive Online Course: Inaugural Field Trial

Matsuda, N., Velsen, M., Barbalios, N., Lin, S., Vasa, H., Hosseini, R., … Bier, N. (2016, January 1). Lecture Notes in Computer Science, Vol. 9684, pp. 327–333.

Development of a Peer Review System for Art Education and its Evaluation

Transactions of Japan Society of Kansei Engineering, 15(4), 425–430.

How quickly can wheel spinning be detected?

Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, 607–608. http://www.scopus.com/inward/record.url?eid=2-s2.0-85067333848&partnerID=MN8TOARS

Tell Me How to Teach, I’ll Learn How to Solve Problems

Matsuda, N., Barbalios, N., Zhao, Z., Ramamurthy, A., Stylianides, G. J., & Koedinger, K. R. (2016, January 1). Lecture Notes in Computer Science, Vol. 9684, pp. 111–121.

Application of Waka-Kansei Database for Learning Japanese Waka in Middle School

Japan Journal of Educational Technology, 38(4), 329–340.

Authoring Example-based Tutors for Procedural Tasks

In R. Sottilare, A. Graesser, X. Hu, & K. Brawner (Eds.), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools & Expert Modeling Techniques (Vol. 3, pp. 71–94). Orlando, FL: U.S. Army Research Laboratory.

Authoring tutors with complex solutions: A comparative analysis of Example Tracing and SimStudent

CEUR Workshop Proceedings, 1432, 35–44. http://www.scopus.com/inward/record.url?eid=2-s2.0-84944328396&partnerID=MN8TOARS

Methods for evaluating simulated learners: Examples from SimStudent

CEUR Workshop Proceedings, 1432, 45–54. http://www.scopus.com/inward/record.url?eid=2-s2.0-84944315929&partnerID=MN8TOARS

Understanding Students’ Use of Code-Switching in a Learning by Teaching Technology

Yarzebinski, E., Ogan, A., Rodrigo, M. M. T., & Matsuda, N. (2015, January 1). Lecture Notes in Computer Science, Vol. 9112, pp. 504–513.

Authoring Tutors with SimStudent: An Evaluation of Efficiency and Model Quality

MacLellan, C. J., Koedinger, K. R., & Matsuda, N. (2014, January 1). Lecture Notes in Computer Science, pp. 551–560.

Integrating representation learning and skill learning in a human-like intelligent agent

Li, N., Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2014, December 4). Artificial Intelligence, Vol. 219, pp. 67–91.

Investigating the Effect of Meta-cognitive Scaffolding for Learning by Teaching

Matsuda, N., Griger, C. L., Barbalios, N., Stylianides, G. J., Cohen, W. W., & Koedinger, K. R. (2014, January 1). Lecture Notes in Computer Science, pp. 104–113.

Teaching the Teacher: Tutoring SimStudent Leads to More Effective Cognitive Tutor Authoring

Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2014, May 19). International Journal of Artificial Intelligence in Education, Vol. 25, pp. 1–34.

Cognitive anatomy of tutor learning: Lessons learned with SimStudent.

Journal of Educational Psychology, 105(4), 1152–1163.

Exploring the Implications of Tutor Negativity Towards a Synthetic Agent in a Learning-by-Teaching Environment

Philippine Computing Journal, 8(1), 15–20.

Impact of prior knowledge and teaching strategies on learning by teaching

CEUR Workshop Proceedings, 1009, 71–80. http://www.scopus.com/inward/record.url?eid=2-s2.0-84924982067&partnerID=MN8TOARS

Studying the Effect of a Competitive Game Show in a Learning by Teaching Environment

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., & Koedinger, K. R. (2013, October 29). International Journal of Artificial Intelligence in Education, Vol. 23, pp. 1–21.

Toward a reflective SimStudent: Using experience to avoid generalization errors

CEUR Workshop Proceedings, 1009, 51–60. http://www.scopus.com/inward/record.url?eid=2-s2.0-84924980730&partnerID=MN8TOARS

"Oh dear stacy!"

Ogan, A., Finkelstein, S., Mayfield, E., D'Adamo, C., Matsuda, N., & Cassell, J. (2012, May 5). Conference on Human Factors in Computing Systems - Proceedings, pp. 39–48.

An Application of Peer Review for Art Education: A Tablet PC Becomes a Language for Students Who are Hard of Hearing

Namatame, M., & Matsuda, N. (2012, March 1). Proceedings 2012 17th IEEE International Conference on Wireless, Mobile and Ubiquitous Technology in Education, WMUTE 2012, pp. 190–192.

An empirical study on the effect of Kansei-database for middle school students to learn Waka-reading comprehension

Japan Journal of Educational Technology, 36(2), 125–134.

Building a Conversational SimStudent

Carlson, R., Keiser, V., Matsuda, N., Koedinger, K. R., & Rosé, C. P. (2012, January 1). Lecture Notes in Computer Science, pp. 563–569.

Motivational Factors for Learning by Teaching

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G., & Koedinger, K. R. (2012, January 1). Lecture Notes in Computer Science, pp. 101–111.

Studying the Effect of Tutor Learning Using a Teachable Agent that Asks the Student Tutor for Explanations

Matsuda, N., Cohen, W. W., Koedinger, K. R., Keiser, V., Raizada, R., Yarzebinski, E., … Stylianides, G. (2012, March 1). Proceedings 2012 4th IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, DIGITEL 2012, pp. 25–32.

A machine learning approach for automatic student model discovery

EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining, 31–40. http://www.scopus.com/inward/record.url?eid=2-s2.0-84863408562&partnerID=MN8TOARS

Learning by Teaching SimStudent – An Initial Classroom Baseline Study Comparing with Cognitive Tutor

Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., Cohen, W. W., & Koedinger, K. R. (2011, January 1). Lecture Notes in Computer Science, pp. 213–221.

Learning by Teaching SimStudent – Interactive Event

Matsuda, N., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W., & Koedinger, K. R. (2011, January 1). Lecture Notes in Computer Science, p. 623.

Learning by Teaching SimStudent

Matsuda, N., Keiser, V., Raizada, R., Stylianides, G., Cohen, W. W., & Koedinger, K. (2010, January 1). Lecture Notes in Computer Science, p. 449.

Learning by Teaching SimStudent: Technical Accomplishments and an Initial Use with Students

Matsuda, N., Keiser, V., Raizada, R., Tu, A., Stylianides, G., Cohen, W. W., & Koedinger, K. R. (2010, January 1). Lecture Notes in Computer Science, pp. 317–326.

Towards a computational model of why some students learn faster than others

AAAI Fall Symposium - Technical Report, FS-10-01, 40–46. http://www.scopus.com/inward/record.url?eid=2-s2.0-79960149222&partnerID=MN8TOARS

Tuning cognitive tutors into a platform for learning-by-teaching with SimStudent technology

CEUR Workshop Proceedings, 587, 20–25. http://www.scopus.com/inward/record.url?eid=2-s2.0-84888214372&partnerID=MN8TOARS

Why tutored problem solving may be better than example study: Theoretical implications from a simulated-student study

Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 111–121.

Evaluating a simulated student using real students data for training and testing

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 107–116). http://www.scopus.com/inward/record.url?eid=2-s2.0-37249054311&partnerID=MN8TOARS

How to get a Ph.D in America

In A. Arimoto & I. Kitagaki (Eds.), University Authority (pp. 132–137). Tokyo, Japan: Minervashobo Publishers Inc.

Applying programming by demonstration in an intelligent authoring tool for cognitive tutors

AAAI Workshop - Technical Report, WS-05-04, 1–8. http://www.scopus.com/inward/record.url?eid=2-s2.0-33646048713&partnerID=MN8TOARS

Instructional strategies

In H. Tanaka (Ed.), Encyclopedia of Artificial Intelligence. Tokyo, Japan: Japan Society of Artificial Intelligence.

Natural language processing in educational systems

In H. Tanaka (Ed.), Encyclopedia of Artificial Intelligence. Tokyo, Japan: Japan Society of Artificial Intelligence.

GRAMY: A Geometry Theorem Prover Capable of Construction

Matsuda, N., & VanLehn, K. (2004, January 1). Journal of Automated Reasoning, Vol. 32, pp. 3–33.

Modeling hinting strategies for geometry theorem proving

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2702, 373–377. http://www.scopus.com/inward/record.url?eid=2-s2.0-8344255630&partnerID=MN8TOARS

A reification of a strategy for geometry theorem proving

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1839, p. 660). http://www.scopus.com/inward/record.url?eid=2-s2.0-84944321228&partnerID=MN8TOARS

Cognitive model of geometry theorem proving with construction and its application to intelligent tutoring systems

In Y. Sugiyama (Ed.), Towards new practical theories in mathematics education. Tokyo, Japan: Toyokan Publishers Inc.

An object oriented distributed working environment to integrate cooperative work and personal work

Transactions of Information Processing Society of Japan, 39(1), 123–130.

The system for supporting to learn/diagnose Z notation

Transaction of Japan Society for Information and Systems in Education, 14(1), 3–12.

Intelligent CAI for geometric theorem proving with dynamic manipulative interface

Transactions of Information Processing Society of Japan, 37(9), 1679–1687.

A study of the relationship between programming abilities and academic achievement in junior high school mathematics

Japan Journal of Educational Technology, 19(2), 85–100.

Application of analogical reasoning and extraction of tutoring rules for concept-formation learning

Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 77(3), 75–86.

Study of CAI with algorithm diagnosis system for novice C programmers

Journal of Japan Society for CAI, 11(2), 63–74.

Computer networking

In T. Okamoto (Ed.), Introduction to Information Education for Teachers: Cases in High-School Education (pp. 180–197). Tokyo: Personal Media.

On the system of learning and diagnosis for fostering space concept

Journal of Japan Society for CAI, 10(3), 114–121.

Student modeling for procedural problem solving

IEICE Transactions on Information and Systems, E77-D(1), 49–56.

Foundations of Computers

In T. Okamoto (Ed.), Introduction to Information Education for Teachers: Cases in Middle-School Education (pp. 88–119). Tokyo, Japan: Personal Media.

Mental model of the process of composing geometric proofs using an intelligent tutoring system

Japan Journal of Educational Technology, 15(4), 167–182.

Overview on the studies of intelligent CAIs/ITSs in Japan

Educational Technology Research, 15(1-2), 1–8.

Student model and its recognition by hypothesis-based reasoning in ITS

Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 75(8), 85–95.

A knowledge based CAD to support students’ learning elementary geometric concepts and diagnosing their misconceptions

Japan Journal of Educational Technology, 14(4), 147–157.

An automatic generation of knowledge-base for an intelligent CAI on geometry theorem proving and a GUI to draw geometric figures

Transactions of the Institution of Electronics, Information, and Communication Engineering, J73-D-II(1), 88–99.

Knowledge communication

In T. Okamoto & R. Mizoguchi (Eds.), Artificial Intelligence and Tutoring Systems (pp. 447–456). Tokyo, Japan: Ohmu Inc.

What is CAI?

In T. Okamoto (Ed.), Introduction to C Programming (pp. 201–236). Tokyo, Japan: Personal Media.

Learning to recognize students’ plan in geometry proof using intelligent CAI

Transactions of Information Processing Society of Japan, 30(8), 1046–1057.

An intelligent CAI for geometry proof

Transactions of Information Processing Society of Japan, 29(3), 311–324.

Drill, Practice, and Machine Learning

In T. Okamoto, K. Akahori, & S. Yokoyama (Eds.), Computer environments for children (pp. 21–40). Tokyo, Japan: Personal Media.