Adaptive Robot Language Tutoring Based on Bayesian Knowledge Tracing and Predictive Decision-Making
Open accessAbstract
In this paper, we present an approach to adaptive language tutoring in child-robot interaction. The approach is based on a dynamic probabilistic model that represents the inter-relations between the learner's skills, her observed behaviour in tutoring interaction, and the tutoring action taken by the system. Being implemented in a robot language tutor, the model enables the robot tutor to trace the learner's knowledge and to decide which skill to teach next and how to address it in a game-like tutoring interaction. Results of an evaluation study are discussed demonstrating how participants in the adaptive tutoring condition successfully learned foreign language words.
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Physically embodied robots provide adaptive assistance and verbal interaction similar to virtual or conversational agents, yet they foster greater engagement with the physical environment and encourage more advanced social behaviors during learning, leading to improved learning outcomes.
“Physically embodied robots provide adaptive assistance and verbal interaction similar to virtual or conversational agents [5, 46, 63, 65], yet they foster greater engagement with the physical environment and encourage more advanced social behaviors during learning [2], leading to improved learning outcomes [47].”
From SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot (2025) · cites this paper as [65]
Cites
Conflict, arousal, and curiosity.
Berlyne, D. E. (1960) · McGraw-Hill Book Company · doi:10.1037/11164-000 · Cited by 6,018 · see citing works
Knowledge tracing: Modeling the acquisition of procedural knowledge
Corbett, A. T., & Anderson, J. R. (1995) · User Modelling and User-Adapted Interaction · doi:10.1007/bf01099821 · Cited by 2,324 · see citing works
Flow, performance and moderators of challenge-skill balance
Engeser, S., & Rheinberg, F. (2008) · Motivation and Emotion · doi:10.1007/s11031-008-9102-4 · Cited by 860 · see citing works
Information-seeking, curiosity, and attention: computational and neural mechanisms
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.001 · Cited by 784 · see citing works
Habgood, M. P. J., & Ainsworth, S. E. (2011) · Journal of the Learning Sciences · doi:10.1080/10508406.2010.508029 · Cited by 652 · see citing works
Affect and learning: An exploratory look into the role of affect in learning with AutoTutor
Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004) · Journal of Educational Media · doi:10.1080/1358165042000283101 · Cited by 630 · see citing works
Expressive robots in education
Saerbeck, M., Schut, T. G., Bartneck, C., & Janse, M. D. (2010) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753567 · Cited by 323 · see citing works
Kennedy, J., Baxter, P. E., & Belpaeme, T. (2015) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2696454.2696457 · Cited by 256 · see citing works
The Physical Presence of a Robot Tutor Increases Cognitive Learning Gains
Leyzberg, D., Spaulding, S., Toneva, M., & Scassellati, B. M. (2012) · eScholarship (California Digital Library) · Cited by 253 · see citing works
Fridin, M. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.07.043 · Cited by 249 · see citing works
The educational use of home robots for children
Han, J., Jo, M., Park, S., & Kim, S. (2006) · IEEE RO-MAN · doi:10.1109/roman.2005.1513808 · Cited by 179 · see citing works
Storytelling with robots: Learning companions for preschool children's language development
Kory-Westlund, J. M., & Breazeal, C. (2014) · IEEE RO-MAN · doi:10.1109/roman.2014.6926325 · Cited by 172 · see citing works
“Sociable robot improves toddler vocabulary skills,”
Movellan, J., Eckhardt, M., Virnes, M., & Rodriguez, A. (2009) · Proceedings of the 4th ACM/IEEE international conference on Human robot interaction · doi:10.1145/1514095.1514189 · Cited by 136 · see citing works
A review and synthesis of recent research in intelligent computer-assisted instruction
Dede, C. (1986) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(86)80050-5 · Cited by 110 · see citing works
Effects of Embodiment and Gestures on Social Interaction in Drumming Games with a Humanoid Robot
Köse, H., Ferrari, E., Dautenhahn, K., Syrdal, D. S., & Nehaniv, C. L. (2009) · Advanced Robotics · doi:10.1163/016918609x12518783330360 · Cited by 109 · see citing works
Multi-Armed Bandits for Intelligent Tutoring SystemsPreprint
Clement, B., Roy, D., Oudeyer, P., & Lopes, M. C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.3174 · Cited by 107 · see citing works
Faster Teaching via POMDP Planning
Rafferty, A. N., Brunskill, E., Griffiths, T. L., & Shafto, P. (2015) · Cognitive Science · doi:10.1111/cogs.12290 · Cited by 97 · see citing works
Skantze, G., & Moubayed, S. A. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388698 · Cited by 94 · see citing works
Algorithmic and Human Teaching of Sequential Decision Tasks
Çakmak, M., & Lopes, M. C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8333 · Cited by 77 · see citing works
Bayesian Active Learning-Based Robot Tutor for Children's Word-Reading Skills
Gordon, G., & Breazeal, C. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9376 · Cited by 75 · see citing works
Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks
Käser, T., Klingler, S., Schwing, A. G., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07221-0_23 · Cited by 66 · see citing works
Rowe, M. L., Silverman, R. D., & Mullan, B. E. (2012) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2012.12.001 · Cited by 64 · see citing works
A Virtual Agent as Vocabulary Trainer: Iconic Gestures Help to Improve Learners’ Memory Performance
Bergmann, K., & Macedonia, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40415-3_12 · Cited by 55 · see citing works
Self-citationHyun, E.-J., Kim, S., Jang, S.-K., et al. (2008) · IEEE RO-MAN · doi:10.1109/roman.2008.4600664 · Cited by 53 · see citing works
Neural Correlates of High Performance in Foreign Language Vocabulary Learning
Macedonia, M., Müller, K., & Friederici, A. D. (2010) · Mind Brain and Education · doi:10.1111/j.1751-228x.2010.01091.x · Cited by 38 · see citing works
Affect-Aware Student Models for Robot Tutors
Spaulding, S., Gordon, G., & Breazeal, C. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.5555/2936924.2937050 · Cited by 34 · see citing works
Developmental learning for Intelligent Tutoring Systems
Clement, B., Roy, D., Oudeyer, P., & Lopes, M. C. (2014) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2014.6983019 · Cited by 23 · see citing works
Storytelling by a kindergarten social assistive robot
FridinMarina (2014) · Computers in Education · doi:10.5555/2753872.2753893 · Cited by 9 · see citing works
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Social robots for education: a review.
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018) · Science Robotics · doi:10.1126/scirobotics.aat5954 · Cited by 1,558 · see citing works
Fostering children’s creativity through LLM-driven storytelling with a social robot
Elgarf, M., Salam, H., & Peters, C. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1457429 · Cited by 28 · see citing works
Ho, H.-R., Hubbard, E. M., & Mutlu, B. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642806 · Cited by 24 · see citing works
SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot
Ho, H.-R., Kargeti, N., Liu, Z., & Mutlu, B. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713330 · Cited by 24 · see citing works
Parents’ Views on Using Social Robots for Language Learning
Tolksdorf, N. F., & Rohlfing, K. J. (2020) · 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) · doi:10.1109/ro-man47096.2020.9223540 · Cited by 15 · see citing works
- Authors
- Schodde, T., Bergmann, K., & Kopp, S.
- Affiliations
- Bielefeld University (DE)
- ORCID
- Kopp, Stefan
- Venue
- Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
- Publisher
- ACM
- Published
- 2017-03-06
- Pages
- 128-136
- OpenAlex
- W2592042377
- Cited by
- 110
- References
- 29
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 19 · 2023: 13 · 2022: 16 · 2021: 12 · 2020: 7 · 2019: 11 · 2018: 16 · 2017: 3
- FWCI
- 4.52
- Type
- conference paper
- Language
- en
- Open access
- green
- pub.uni-bielefeld.de
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion, other-oa)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion, other-oa)
- Funders
- Horizon 2020 (688014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- child-robot interaction, Bayesian Knowledge Tracing, predictive decision-making, foreign language word learning
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