The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.
Growing Growth Mindset with a Social Robot Peer
Open accessAbstract
Mindset has been shown to have a large impact on people's academic, social, and work achievements. A growth mindset, i.e., the belief that success comes from effort and perseverance, is a better indicator of higher achievements as compared to a fixed mindset, i.e., the belief that things are set and cannot be changed. Interventions aimed at promoting a growth mindset in children range from teaching about the brain's ability to learn and change, to playing computer games that grant brain points for effort rather than success. This work explores a novel paradigm to foster a growth mindset in young children where they play a puzzle solving game with a peer-like social robot. The social robot is fully autonomous and programmed with behaviors suggestive of it having either a growth mindset or a neutral mindset as it plays puzzle games with the child. We measure the mindset of children before and after interacting with the peer-like robot, in addition to measuring their problem solving behavior when faced with a challenging puzzle. We found that children who played with a growth-mindset robot 1) self-reported having a stronger growth mindset and 2) tried harder during a challenging task, as compared to children who played with the neutral-mindset robot. These results suggest that interacting with peer-like social robot with a growth mindset can promote the same mindset in children.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Previously, social robots have been shown to promote curiosity and engagement in children (Gordon et al., 2015; Park et al., 2017), and it would be valuable to explore such functionality with older adults.”
From Personal Narratives in Technology Design: The Value of Sharing Older Adults’ Stories in the Design of Social Robots (2021) · cites this paper as Park et al., 2017
Experimental — accuracy not yet measuredIn the citing paper
“Nonetheless, outside the settings of expressive arts therapy, a substantial body of studies focus on supporting storytelling and storymaking for children and families [23, 83, 96, 134].”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [83]
In the citing paper
“This encourages continued creative self-explorations and self-expression to take place after the therapeutic interventions [83].”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [83]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · Cited by 8,729
Mindset: The New Psychology of Success
(2006) · Choice Reviews Online · doi:10.5860/choice.44-2397 · Cited by 7,804
Grit: perseverance and passion for long-term goals
Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.92.6.1087 · Cited by 6,911
Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007) · Child Development · doi:10.1111/j.1467-8624.2007.00995.x · Cited by 3,784
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
Yeager, D. S., & Dweck, C. S. (2012) · Educational Psychologist · doi:10.1080/00461520.2012.722805 · Cited by 2,334
Aronson, J., Fried, C. B., & Good, C. (2002) · Journal of Experimental Social Psychology · doi:10.1006/jesp.2001.1491 · Cited by 1,889
Praise for intelligence can undermine children's motivation and performance.
Mueller, C. M., & Dweck, C. S. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.75.1.33 · Cited by 1,709 · cited 4×
“Both laboratory and classroom studies have shown that mindset can be changed through careful intervention, often involving social interaction [8, 16, 4, 22, 19].”
BackgroundInfluentialGood, C., Aronson, J., & Inzlicht, M. (2003) · Journal of Applied Developmental Psychology · doi:10.1016/j.appdev.2003.09.002 · Cited by 1,096
Person versus process praise and criticism: Implications for contingent self-worth and coping.
Kamins, M. L., & Dweck, C. S. (1999) · Developmental Psychology · doi:10.1037/0012-1649.35.3.835 · Cited by 659 · cited 3×
“Both laboratory and classroom studies have shown that mindset can be changed through careful intervention, often involving social interaction [8, 16, 4, 22, 19].”
Background
Flagged first, then by citations
2 of the 36 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- 10.5555/3016387.3016461 · DOI
- J. Jirout . 'i try to learn as much as i can' vs. 'i try to get the best grade i can': A study of children's ideas about learning and school . In Cognitive Development Society Meeting , 2015 . J. Jirout. 'i try to learn as much as i can' vs. 'i try to get the best grade i can': A study of children's ideas about learning and school. In Cognitive Development Society Meeting, 2015.
- 10.5555/2906831.2906979 · DOI
Cited by
Sorted by citations
Items 101–140 of 140
Effectiveness of Robotic Technology in Vocational Education: A Meta-Analysis
Sukardi, Setyawan, H., Risfendra, Usmeldi, & Yanto, D. T. P. (2024) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2024.14.4.2073 · Cited by 22
Braga, J., Regateiro, F. S., Stiubiener, I., & Braga, J. C. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/wexjr · Cited by 5
Lee, J.-H. (2017) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2017.8279359 · Cited by 3
Review on Artificial Intelligence and Robots in STEAM Education for Early Childhood Development
Mohana, M., Nandhini, K., & Subashini, P. (2022) · Advances in early childhood and K-12 education · doi:10.4018/978-1-7998-8649-5.ch019 · Cited by 3
User Interface for Child-Robot Interaction in Education: Perspective and Challenges
Mustar, M. Y., Hartanto, R., & Santosa, P. I. (2022) · International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) · doi:10.1109/ice3is56585.2022.10010156 · Cited by 3
Fostering Growth Mindset Principles in the Prevention of Type 2 Diabetes Through a Narrative Game
Raffali, A., & Asraf, R. M. (2023) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.22.2.14 · Cited by 3
Implications of Social Neuroscience for Learning Technology Research and Development
Law, N. W. Y., & Tsang, H. W. C. (2018) · Educational communications and technology: issues and innovations · doi:10.1007/978-3-030-02631-8_9 · Cited by 3
Kosmyna, N., Scheirer, C., & Maes, P. (2021) · CHI Extended Abstracts · doi:10.1145/3411763.3451732 · Cited by 4
Exploring the Effects of Growth Mind-Set Usernames in STEM Games
Kao, D. (2019) · Proceedings of the 2019 AERA Annual Meeting · doi:10.3102/1427645 · Cited by 3
Jafaritazehjani, S., Christensen, V., & Özgür, A. G. (2025) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3765766.3765798 · Cited by 1
Güneyli, A., Korkmaz, S., Karabacak, E., & Altıntuğ, F. A. (2026) · AI · doi:10.3390/ai7030100 · Cited by 2
Yi, Z., Capello, S. S., Gómez, R., & Šabanović, S. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3757279.3785570 · Cited by 1
Scatigna, S., Pecini, C., Pugi, L., Adelucci, E., Cavallo, F., Scrocco, A., Bruni, A., Benigni, M., Mei, S., Lieto, M. C. D., et al. (2026) · International Journal of Social Robotics · doi:10.1007/s12369-026-01398-8 · Cited by 1
Tolksdorf, N. F., & Schaffrath, S. (2026) · Springer eBooks · doi:10.1007/978-3-658-49118-5_12 · Cited by 1
EEG-Based Robot Alert System for Improving User Concentration
Raja, K., Laohakangvalvit, T., Sripian, P., & Sugaya, M. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06388-6_27 · Cited by 1
Deng, S., Zihan, L. I. N., Lu, Y., & Li, X. (2023) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2023.02319 · Cited by 1
Exploring the Design of Social Robot User Interfaces for Presenting Data-Driven Stories
Herath, A., Sallam, S., Sakamoto, Y., Gómez, R., & Irani, P. (2023) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3626705.3627781 · Cited by 1
Can Robots Enhance the Learning Experience by Making Music More Fun?
Tisza, G., Song, H., Markopoulos, P., Barakova, E. I., & Ham, J. R. C. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731440 · Cited by 1
Robot Primals: Exploring World Beliefs as a Source for Robot Behavior Design
Sullivan, D., White, N. T., Hu, Y., Clifton, J. D. W., & Mutlu, B. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3749997 · Cited by 1
Human Communicative Behavior While Solving Tangram Puzzles for Subsequent Transfer to a Robot
Zinina, A., Zaidelman, L., Arinkin, N. A., & Kotov, A. A. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-71637-0_74 · Cited by 1
A proposal to improve research in AI algorithm and data governancePreprint
Braga, J., Regateiro, F. S., Stiubiener, I., & Braga, J. C. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/sr7kt · Cited by 1
Huang, J. (2024) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/175025 · Cited by 2
Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue
Das, R., & Pon-Barry, H. (2018) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w18-5013
A semantics based computational model for word learning
Grover, I. (2018) · DSpace@MIT (Massachusetts Institute of Technology)
Mutual Reinforcement LearningPreprint
Roy, S., Kieson, E., Abramson, C. I., & Crick, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06725
Designing Games for Enabling Co-creation with Social AgentsPreprint
Ali, S., Devasia, N., & Breazeal, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00201 · Cited by 3
Self-citationShi, Z., Cao, M., Pei, S., Qiao, X., Groechel, T. R., & Matarić, M. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.15256
(2022) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-7998-8653-2.ch003
Strain-based Pinch Force Control for Soft Object Manipulation
INOUE, T., & MIZUTANI, Y. (2022) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim52237.2022.9863360
Uma proposta para impulsionar as pesquisas em governança de algoritmos de IA e dadosPreprint
Braga, J., Regateiro, F. S., Stiubiener, I., & Braga, J. C. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/xcpsd
Imitating Human Strategy for Social Robot in Real-Time Two-Player Games
Zheng, C., Wang, H., Zhang, L., Hao, J., Gómez, R., Nakamura, K., & Li, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-24670-8_38
Social Robots That Promote Soft Skills in Students
Gordon, G. (2023) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3611681
Self-citationGubenko, A. (2024) · Possibility Studies & Society · doi:10.1177/27538699241273734
Yang, Y.-T. T., Hu, Y. H., & Dautenhahn, K. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-3522-1_29
(2025) · International Journal of Humanities and Educational Research · doi:10.47832/2757-5403.31.18
The Effects of Digital Games on Pre-K-12 Students’ Growth Mindset: A Systematic Review
Rahimi, S., Esmaeiligoujar, S., Gao, R., Ercan, D., & Keawphonkrang, S. (2025) · Proceedings. · doi:10.22318/icls2025.104591
TutorBotz: A GenAI Tool for Teachers to Program Social Robots as Teacher Assistants
Botzer, B. H., Gordon, G., & Gordon-Kiwkowitz, M. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3776734.3794386
Self-citationDeveloping an Integrated Behavioral Framework for Social Robots Promoting Children’s Soft Skills
Yi, Z., & Gordon, G. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3776734.3794356
Self-citationRobotic Feedback for Enhancing Problem-Solving Experiences
Katz, R., Saadon, N. H., Grishko, A., & Erel, H. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3776734.3794347
Encouraging Growth in Programming Education: Does Growth Mindset Feedback Influence Learning?
Koch, N. N., Kapfenstein, A.-K., & Wirzberger, M. (2026) · Technology Knowledge and Learning · doi:10.1007/s10758-026-09996-1
Metadata
- Authors
- Park, H. W., Rosenberg-Kima, R., Rosenberg, M., Gordon, G., & Breazeal, C.
- Affiliations
- MIT Media Lab (US)
- Tel Aviv University, Israel, Tel Aviv, Israel
- Tel Aviv University (IL)
- Venue
- Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
- Publisher
- ACM
- Published
- 2017-03-06
- Volume
- 2017
- Pages
- 137-145
- PMC
- PMC6347975
- OpenAlex
- W2594091522
- Cited by
- 140
- References
- 36
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 12 · 2023: 15 · 2022: 25 · 2021: 25 · 2020: 16 · 2019: 17 · 2018: 9 · 2017: 3
- FWCI
- 19.95
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Institutes of Health (5R01HD086899-02)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Education › Educational Robotics and Engineering
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Keywords
- growth mindset, fixed mindset, social robots, children, puzzle solving, effort, problem-solving behavior, autonomous robot behavior
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
