Personalizing robot tutors to individuals' learning differences
Abstract
In education research, there is a widely-cited result called "Bloom's two sigma" that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction. Tutored students scored in the 95th percentile, or two sigmas above the mean, on average, compared to students who received traditional classroom instruction. In human-robot interaction research, however, there is relatively little work exploring the potential benefits of personalizing a robot's actions to an individual's strengths and weaknesses. In this study, participants solved grid-based logic puzzles with the help of a personalized or non-personalized robot tutor. Participants' puzzle solving times were compared between two non-personalized control conditions and two personalized conditions (n=80). Although the robot's personalizations were less sophisticated than what a human tutor can do, we still witnessed a "one-sigma" improvement (68th percentile) in post-tests between treatment and control groups. We present these results as evidence that even relatively simple personalizations can yield significant benefits in educational or assistive human-robot interactions.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [46]
Cites
The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
BLOOM, B. S. (1984) · Educational Researcher · doi:10.3102/0013189x013006004 · Cited by 2,734 · cited 1× · see citing works
“In education research, there is a widely-cited result called “Bloom’s two sigma” that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction [1].”
BackgroundVanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323 · see citing works
Robots at home: Understanding long-term human-robot interaction
Kidd, C. D., & Breazeal, C. (2008) · 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2008.4651113 · Cited by 332 · see citing works
Interactive robots for communication-care: a case-study in autism therapy
Kozima, H., Nakagawa, C., & Yasuda, Y. L. (2006) · IEEE RO-MAN · doi:10.1109/roman.2005.1513802 · Cited by 317 · 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
Self-citationLee, M. K., Forlizzi, J., Kiesler, S. B., Rybski, P. E., Antanitis, J., & Savetsila, S. (2012) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2157689.2157804 · Cited by 169 · see citing works
Towards a humanoid museum guide robot that interacts with multiple persons
Bennewitz, M., Faber, F., Joho, D., Schreiber, M., & Behnke, S. (2005) · IEEE-RAS International Conference on Humanoid Robots · doi:10.1109/ichr.2005.1573603 · Cited by 140 · see citing works
Sung, J.-Y., Grinter, R. E., & Christensen, H. I. (2009) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/1518701.1518732 · Cited by 101 · see citing works
Long-Term Interactions with Empathic Robots: Evaluating Perceived Support in Children
Leite, I., Castellano, G., Pereira, A., Martinho, C., & Paiva, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34103-8_30 · Cited by 61 · see citing works
Robots that express emotion elicit better human teaching
Leyzberg, D., Avrunin, E., Liu, J., & Scassellati, B. M. (2011) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1957656.1957789 · Cited by 48 · see citing works
Self-citationMovellan, J. R., Tanaka, F., Fasel, I., Taylor, C., Ruvolo, P., & Eckhardt, M. (2007) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1228716.1228761 · Cited by 37 · see citing works
The design of a semi-autonomous robot avatar for family communication and education
Lee, J. K., Toscano, R. L., Stiehl, W. D., et al. (2008) · IEEE RO-MAN · doi:10.1109/roman.2008.4600661 · Cited by 33 · see citing works
Building Intelligent Tutoring Systems: An Overview
Nkambou, R., Bourdeau, J., & Psyché, V. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-14363-2_18 · Cited by 30 · see citing works
Relationships between user experiences and children's perceptions of the education robot
Hyun, E.-J., Yoon, H., & Son, S. (2010) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2010.5453197 · Cited by 20 · see citing works
An efficient algorithm for solving nonograms
Yu, C.-H., Lee, H.-L., & Chen, L. (2009) · Applied Intelligence · doi:10.1007/s10489-009-0200-0 · Cited by 18 · see citing works
Relationships between user experiences and children's perceptions of the education robot
Hyun, E.-J., Yoon, H., & Son, S. (2010) · doi:10.5555/1734454.1734542 · Cited by 17 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Self-citationEngagement in human-agent interaction: An overview
Oertel, C., Castellano, G., Chetouani, M., Nasir, J., Obaid, M., Pelachaud, C., & Peters, C. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00092 · Cited by 160 · see citing works
Park, H. W., Grover, I., Spaulding, S., Gomez, L., & Breazeal, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301687 · Cited by 146 · see citing works
Self-citationA Systematic Review of Ten Years of Research on Human Interaction with Social Robots
Lambert, A., Norouzi, N., Bruder, G., & Welch, G. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1801172 · Cited by 89 · 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
Louie, B., Björling, E. A., & Kuo, A. C. (2021) · Frontiers in Education · doi:10.3389/feduc.2021.566909 · Cited by 18 · see citing works
- Authors
- Leyzberg, D., Spaulding, S., & Scassellati, B.
- Affiliations
- Yale University (US)
- Massachusetts Institute of Technology (US)
- ORCID
- Scassellati, Brian
- Venue
- Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
- Publisher
- ACM
- Published
- 2014-03-03
- Pages
- 423-430
- OpenAlex
- W2144001787
- Cited by
- 223
- References
- 16
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 15 · 2023: 22 · 2022: 16 · 2021: 27 · 2020: 19 · 2019: 23 · 2018: 28 · 2017: 11 · 2016: 19 · 2015: 18 · 2014: 5
- FWCI
- 34.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Keywords
- robot tutors, personalized tutoring, human-robot interaction, one-on-one tutoring, logic puzzles, puzzle solving, learning differences, assistive robotics, educational robotics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
