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Research Trends in Social Robots for Learning

  • Service and Interactive Robotics (A Tapus, Section Editor)
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Abstract

Purpose of Review

With the growth in the number of market-available social robots, there is an increasing interest in research on the usage of social robots in education. This paper proposes a summary of trends highlighting current research directions and potential research gaps for social robots in education. We are interested in design aspects and instructional setups used to evaluate social robotics system in an educational setting.

Recent Findings

The literature demonstrates that as the field grows, setup, methodology, and demographics targeted by social robotics applications seem to settle and standardize—a tutoring Nao robot with a tablet in front of a child seems the stereotypical social educational robotics setup.

Summary

An updated review on social robots in education is presented here. We propose, first, an analysis of the pioneering works in the field. Secondly, we explore the potential for education to be the ideal context to investigate central human-robot interaction research questions. A trend analysis is then proposed demonstrating the potential for educational context to nest impactful research from human-robot interaction.

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References

  1. Angel-Fernandez JM, Vincze M. Towards a formal definition of educational robotics. In: Austrian Robotics Workshop 2018. 2018 [cited 2020 Mar 30]. Available from: https://doi.org/10.15203/3187-22-1-08

  2. Kanero J, Geçkin V, Oranç C, Mamus E, Küntay AC, Göksun T. Social robots for early language learning: current evidence and future directions. Child Dev Perspect. 2018;12(3):146–51.

    Google Scholar 

  3. van den Berghe R, Verhagen J, Oudgenoeg-Paz O, van der Ven S, Leseman P. Social robots for language learning: a review. Rev Educ Res. 2019 Apr 1;89(2):259–95.

    Google Scholar 

  4. Randall N. A survey of robot-assisted language learning (RALL). ACM Trans Hum-Robot Interact. 2019;9(1):7 1–7:36.

    MathSciNet  Google Scholar 

  5. de Wit J, Krahmer E, Vogt P. Social robots as language tutors: challenges and opportunities. In: Proceedings of the Workshop on the Challenges of Working on Social Robots that Collaborate with People, ACMCHI Conference on Human Factors in Computing Systems (CHI2019 SIRCHI Workshop). 2019 [cited 2020 Apr 18]. Available from: https://research.tilburguniversity.edu/en/publications/social-robots-as-language-tutors-challenges-and-opportunities.

  6. Neumann MM. Social robots and young children’s early language and literacy learning. Early Child Educ J. 2020 Mar 1;48(2):157–70.

    Google Scholar 

  7. Yang J, Zhang B. Artificial intelligence in intelligent tutoring robots: a systematic review and design guidelines. Appl Sci. 2019;9(10):2078.

    Google Scholar 

  8. Jamet F, Masson O, Jacquet B, Stilgenbauer J-L, Baratgin J. Learning by teaching with humanoid robot: a new powerful experimental tool to improve children’s learning ability. J Robot. 2018 [cited 2020 Mar 19]. Available from: https://www.hindawi.com/journals/jr/2018/4578762/.

  9. Tuna G, Tuna A, Ahmetoglu E, Kuscu H. A survey on the use of humanoid robots in primary education: prospects, research challenges and future research directions. Cypriot J Educ Sci. 2019;14(3):361–73.

    Google Scholar 

  10. Kaburlasos VG, Vrochidou E. Social robots for pedagogical rehabilitation: trends and novel modeling principles. Cyber-Physical Systems for Social Applications. IGI Global; 2019 [cited 2020 Mar 18]. p. 1–21. Available from: www.igi-global.com/chapter/social-robots-for-pedagogical-rehabilitation/224413.

  11. Belpaeme T, Kennedy J, Ramachandran A, Scassellati B, Tanaka F. Social robots for education: a review. Sci Robot. 2018 [cited 2020 Apr 1];3(21). Available from: https://robotics.sciencemag.org/content/3/21/eaat5954.

  12. Rosanda V, Istenic SA. The robot in the classroom: a review of a robot role. In: Popescu E, Hao T, Hsu T-C, Xie H, Temperini M, Chen W, editors. Emerging technologies for education. Cham: Springer International Publishing; 2020. p. 347–57. (Lecture Notes in Computer Science).

    Google Scholar 

  13. Chamberlain S. sckott/habanero. 2020 [cited 2020 Apr 23]. Available from: https://github.com/sckott/habanero.

  14. Johal W. WafaJohal/SocialRobotUpdate. 2020 [cited 2020 Apr 23]. Available from: https://github.com/WafaJohal/SocialRobotUpdate.

  15. You are Crossref - Crossref. [cited 2020 Apr 23]. Available from: https://www.crossref.org/.

  16. Tanaka F, Matsuzoe S. Children teach a care-receiving robot to promote their learning: field experiments in a classroom for vocabulary learning. J Hum-Robot Interact. 2012;1(1):78–95.

    Google Scholar 

  17. Fasola J, Matarić MJ. A socially assistive robot exercise coach for the elderly. J Hum-Robot Interact. 2013;2(2):3–32.

    Google Scholar 

  18. Saerbeck M, Schut T, Bartneck C, Janse MD. Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Atlanta, Georgia, USA: Association for Computing Machinery; 2010 [cited 2020 Feb 26]. p. 1613–1622. (Chi ‘10). Available from: https://doi.org/10.1145/1753326.1753567.

  19. Szafir D, Mutlu B. Pay attention! designing adaptive agents that monitor and improve user engagement. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Austin, Texas, USA: Association for Computing Machinery; 2012 [cited 2020 Feb 26]. p. 11–20. (CHI ‘12). Available from: https://doi.org/10.1145/2207676.2207679.

  20. Fridin M. Storytelling by a kindergarten social assistive robot: a tool for constructive learning in preschool education. Comput Educ. 2014;70:53–64.

    Google Scholar 

  21. Han J-H, Jo M-H, Jones V, Jo J-H. Comparative study on the educational use of home robots for children. J Inf Process Syst. 2008;4(4):159–68.

    Google Scholar 

  22. Kennedy J, Baxter P, Belpaeme T. The robot who tried too hard: social behaviour of a robot tutor can negatively affect child learning. In: 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 2015. p. 67–74.

  23. Leyzberg D, Spaulding S, Scassellati B. Personalizing robot tutors to individuals’ learning differences. In: Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction [Internet]. Bielefeld, Germany: Association for Computing Machinery; 2014 [cited 2020 Mar 18]. p. 423–430. (HRI ‘14). Available from: https://doi.org/10.1145/2559636.2559671.

  24. Lemaignan S, Jacq A, Hood D, Garcia F, Paiva A, Dillenbourg P. Learning by teaching a robot: the case of handwriting. IEEE Robot Autom Mag. 2016 Jun;23(2):56–66.

    Google Scholar 

  25. Yadollahi E, Johal W, Paiva A, Dillenbourg P. When deictic gestures in a robot can harm child-robot collaboration. In: Proceedings of the 17th ACM Conference on Interaction Design and Children. Trondheim, Norway: Association for Computing Machinery; 2018 [cited 2020 Feb 26]. p. 195–206. (IDC ‘18). Available from: https://doi.org/10.1145/3202185.3202743.

  26. Wade E, Parnandi A, Mead R, Matarić M. Socially assistive robotics for guiding motor task practice. Paladyn. 2011;2(4):218–27.

    Google Scholar 

  27. Park HW, Grover I, Spaulding S, Gomez L, Breazeal C. A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education. Proc AAAI Conf Artif Intell. 2019;33(01):687–94.

    Google Scholar 

  28. Clabaugh C, Ragusa G, Sha F, Matarić M. Designing a socially assistive robot for personalized number concepts learning in preschool children. In: 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 2015. p. 314–9.

  29. de Haas M, Smeekens I, Njeri E, Haselager P, Buitelaar J, Lourens T, et al. Personalizing educational game play with a robot partner. In: Merdan M, Lepuschitz W, Koppensteiner G, Balogh R, editors. Robotics in Education. Cham: Springer International Publishing; 2017. p. 259–70. (Advances in Intelligent Systems and Computing).

  30. Gao Y, Barendregt W, Obaid M, Castellano G. When robot personalisation does not help: insights from a robot-supported learning study. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). 2018. p. 705–12.

  31. Gordon G, Spaulding S, Westlund JK, Lee JJ, Plummer L, Martinez M, et al. Affective personalization of a social robot tutor for children’s second language skills. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Phoenix: AAAI Press; 2016. p. 3951–7. (AAAI’16)

    Google Scholar 

  32. Ramachandran A, Sebo SS, Scassellati B. Personalized robot tutoring using the assistive tutor POMDP (AT-POMDP). Proc AAAI Conf Artif Intell. 2019;33(01):8050–7.

    Google Scholar 

  33. Ramachandran A, Huang C-M, Scassellati B. Give me a break! Personalized timing strategies to promote learning in robot-child tutoring. In: Proceedings of the 2017 ACM/IEEE International Conference on Human-rRobot Interaction [Internet]. Vienna, Austria: Association for Computing Machinery; 2017 [cited 2020 Feb 26]. p. 146–155. (HRI ‘17). Available from: https://doi.org/10.1145/2909824.3020209.

  34. Clabaugh C, Matarić M. Escaping Oz: autonomy in socially assistive robotics. Annu Rev Control Robot Auton Syst. 2019;2(1):33–61.

    Google Scholar 

  35. Davison DP, Wijnen FM, Charisi V, van der Meij J, Evers V, Reidsma D. Working with a social robot in school: a long-term real-world unsupervised deployment. In: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. Cambridge, United Kingdom: Association for Computing Machinery; 2020 [cited 2020 mMar 16]. p. 63–72. (HRI ‘20). Available from: https://doi.org/10.1145/3319502.3374803.

  36. Charisi V, Gomez E, Mier G, Merino L, Gomez R. Child-robot collaborative problem-solving and the importance of child’s voluntary interaction: a developmental perspective. Front Robot AI. 2020 [cited 2020 Mar 26];7. Available from: https://www.frontiersin.org/articles/10.3389/frobt.2020.00015/full

  37. Chandra S, Alves-Oliveira P, Lemaignan S, Sequeira P, Paiva A, Dillenbourg P. Children’s peer assessment and self-disclosure in the presence of an educational robot. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). 2016. p. 539–44.

  38. Michaelis JE, Mutlu B. Reading socially: transforming the in-home reading experience with a learning-companion robot. Sci Robot. 2018 [cited 2020 Mar 19];3(21). Available from: https://robotics.sciencemag.org/content/3/21/eaat5999

  39. Reich-Stiebert N, Eyssel F. (Ir)relevance of gender? On the influence of gender stereotypes on learning with a robot. In: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. Vienna, Austria: Association for Computing Machinery; 2017 [cited 2020 Mar 18]. p. 166–176. (HRI ‘17). Available from: https://doi.org/10.1145/2909824.3020242.

  40. Ali S, Moroso T, Breazeal C. Can children learn creativity from a social robot? In: Proceedings of the 2019 on Creativity and Cognition [Internet]. San Diego, CA, USA: Association for Computing Machinery; 2019 [cited 2020 Mar 17]. p. 359–368. (C&C ‘19). Available from: https://doi.org/10.1145/3325480.3325499.

  41. Kose H, Akalin N, Uluer P. Socially interactive robotic platforms as sign language tutors. Int J Humanoid Robot. 2014;11(01):1450003.

    Google Scholar 

  42. Park HW, Coogle RA, Howard A. Using a shared tablet workspace for interactive demonstrations during human-robot learning scenarios. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). 2014. p. 2713–9.

  43. Kennedy J, Lemaignan S, Montassier C, Lavalade P, Irfan B, Papadopoulos F, et al. Child speech recognition in human-robot interaction: evaluations and recommendations. In: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. Vienna, Austria: Association for Computing Machinery; 2017 [cited 2020 Apr 16]. p. 82–90. (HRI ‘17). Available from: https://doi.org/10.1145/2909824.3020229.

  44. Thomaz A, Hoffman G, Cakmak M. Computational human-robot interaction. Found Trends Robot. 2016;4(2–3):104–223.

    Google Scholar 

  45. Clow D. The learning analytics cycle: closing the loop effectively. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. Vancouver, British Columbia, Canada: Association for Computing Machinery; 2012 [cited 2020 Apr 17]. p. 134–138. (LAK ‘12). Available from: https://doi.org/10.1145/2330601.2330636.

  46. Siemens G. Learning analytics: the emergence of a discipline. Am Behav Sci. 2013;57(10):1380–400.

    Google Scholar 

  47. Papamitsiou Z, Economides AA. Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J Educ Technol Soc. 2014;17(4):49–64.

    Google Scholar 

  48. Nasir J, Norman U, Johal W, Olsen JK, Shahmoradi S, Dillenbourg P. Robot analytics: what do human-robot interaction traces tell us about learning? In: 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE; 2019. p. 1–7.

  49. Westlund JMK, Dickens L, Jeong S, Harris PL, DeSteno D, Breazeal CL. Children use non-verbal cues to learn new words from robots as well as people. Int J Child-Comput Interact. 2017;13:1–9.

    Google Scholar 

  50. chili-epfl/cowriter_letter_learning. CHILI Lab @ EPFL; 2019 [cited 2020 Apr 23]. Available from: https://github.com/chili-epfl/cowriter_letter_learning.

  51. Jacq A, Lemaignan S, Garcia F, Dillenbourg P, Paiva A. Building successful long child-robot interactions in a learning context. In: 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 2016. p. 239–46.

  52. Chandra S, Paradeda R, Yin H, Dillenbourg P, Prada R, Paiva A. Do children perceive whether a robotic peer is learning or not? In: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. Chicago, IL, USA: Association for Computing Machinery; 2018 [cited 2020 Mar 18]. p. 41–49. (HRI ‘18). Available from: https://doi.org/10.1145/3171221.3171274.

  53. Johal W, Jacq A, Paiva A, Dillenbourg P. Child-robot spatial arrangement in a learning by teaching activity. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). 2016. p. 533–8.

  54. El Hamamsy L, Johal W, Asselborn T, Nasir J, Dillenbourg P. Learning by collaborative teaching: an engaging multi-party cowriter activity. In: 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE; 2019. p. 1–8.

  55. Sandygulova A, Johal W, Zhexenova Z, Tleubayev B, Zhanatkyzy A, Turarova A, et al. CoWriting Kazakh: learning a new script with a robot. In: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. Cambridge, United Kingdom: Association for Computing Machinery; 2020 [cited 2020 Mar 17]. p. 113–120. (HRI ‘20). Available from: https://doi.org/10.1145/3319502.3374813.

  56. Rosenberg-Kima RB, Koren Y, Gordon G. Robot-supported collaborative learning (RSCL): social robots as teaching assistants for higher education small group facilitation. Front Robot AI. 2020 [cited 2020 Mar 26];6. Available from: https://www.frontiersin.org/articles/10.3389/frobt.2019.00148/full.

  57. Begum M, Serna RW, Yanco HA. Are robots ready to deliver autism interventions? A comprehensive review. Int J Soc Robot. 2016;8(2):157–81.

    Google Scholar 

  58. Pennisi P, Tonacci A, Tartarisco G, Billeci L, Ruta L, Gangemi S, et al. Autism and social robotics: a systematic review. Autism Res. 2016;9(2):165–83.

    Google Scholar 

  59. Ismail LI, Verhoeven T, Dambre J, Wyffels F. Leveraging robotics research for children with autism: a review. Int J Soc Robot. 2019;11(3):389–410.

    Google Scholar 

  60. Savela N, Turja T, Oksanen A. Social acceptance of robots in different occupational fields: a systematic literature review. Int J Soc Robot. 2018;10(4):493–502.

    Google Scholar 

  61. Abdi J, Al-Hindawi A, Ng T, Vizcaychipi MP. Scoping review on the use of socially assistive robot technology in elderly care. BMJ Open. 2018;8(2):e018815.

    Google Scholar 

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  1. Faculty of Engineering, University of New South Wales, Sydney, Australia

    Wafa Johal

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Correspondence to Wafa Johal.

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Conflict of Interest

Wafa Johal reports grants and non-financial support from European Union Horizon 2020 research and innovation program under grant agreement no. 765955. Also, she reports that this work is incrementing on a previous review published in Science Robotics 2018 by Belpaeme et al., “Social robots for education: a review”.

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Johal, W. Research Trends in Social Robots for Learning. Curr Robot Rep 1, 75–83 (2020). https://doi.org/10.1007/s43154-020-00008-3

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