Article Sidebar

Submitted
Mar 4, 2024
Accepted
Apr 13, 2024
Published
Apr 29, 2024
Download
PDF
Statistic
Read Counter : 126 Download : 44

Main Article Content

Abstract

Interaction between social robots and children occurs today in a variety of environments, including schools, hospitals, and homes. This review aims to highlight studies that delve into this interaction in the educational settings, exploring the characteristics of the social robot NAO and how its features influence its relationship with children. A search was conducted on July 1st, 2023 in Scopus and PsychInfo. Inclusion criteria pertained to (1) typical development; (2) age range 4-12 years; (3) educational setting; (4) type of robot (NAO); (5) type of publication: peer-reviewed journal; (6) language: English; (7) research studies. Of the 116 results that emerged from the search, 92 were excluded, yielding 24 valid results. We classified the records into two categories, namely 17 results were included in the “NAO as an informational and educational tool” category and 7 in the “NAO as a relational agent” category. The first category considers all studies where social robots were used as tools for educational and informational support; these studies delve into topics related to the teaching of school subjects and personalized learning, with a specific focus on emotional education. In the second category, we encounter studies that explore the relationships between children and robots, with a primary emphasis on the phenomenon of anthropomorphism, the attribution of mental states, touch interaction, and the robot's caregiving abilities. Based on the present review, social robots like NAO emerge as potential resources to implement new forms of teaching and interaction within the educational context; however, more research is needed to design...

Keywords

NAO Children Interaction Learning Educational Robotics

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The author declares that the submitted to Journal of e-Learning and Knowledge Society (Je-LKS) is original and that is has neither been published previously nor is currently being considered for publication elsewhere.
The author agrees that SIe-L (Italian Society of e-Learning) has the right to publish the material sent for inclusion in the journal Je-LKS.
The author agree that articles may be published in digital format (on the Internet or on any digital support and media) and in printed format, including future re-editions, in any language and in any license including proprietary licenses, creative commons license or open access license. SIe-L may also use parts of the work to advertise and promote the publication.
The author declares s/he has all the necessary rights to authorize the editor and SIe-L to publish the work.
The author assures that the publication of the work in no way infringes the rights of third parties, nor violates any penal norms and absolves SIe-L from all damages and costs which may result from publication.

The author declares further s/he has received written permission without limits of time, territory, or language from the rights holders for the free use of all images and parts of works still covered by copyright, without any cost or expenses to SIe-L.

For all the information please check the Ethical Code of Je-LKS, available at http://www.je-lks.org/index.php/ethical-code

How to Cite
Rossi , L., Orsenigo, E., Bisagno, E., & Cadamuro, A. (2024). From learning machines to teaching robots. Interaction for educational purposes between the Social Robot NAO and children: a systematic review. Journal of E-Learning and Knowledge Society, 20(1), 15-26. https://doi.org/10.20368/1971-8829/1135884

References

  1. Ahmad, M.I., Mubin, O., & Orlando, J. (2017). Adaptive Social Robot for Sustaining Social Engagement during Long-Term Children-Robot Interaction. International Journal of Human-Computer Interaction, 33(12), 943-962. https://doi.org/10.1080/10447318.2017.1300750
  2. Ahmad, M.I., Mubin, O., Shahid, S., & Orlando, J. (2019). Robot’s adaptive emotional feedback sustains children’s social engagement and promotes their vocabulary learning: a long-term child-robot interaction study. Adaptive Behavior, 27(4), 243-266. https://doi.org/10.1177/1059712319844182
  3. Alemi, M., A., Meghdari, A., & Ghazisaedy, M. (2015). The Impact of Social Robotics on L2 Learners Anxiety and Attitude in English Vocabulary Acquisition. Int. J. Soc. Robot, 7, 523535.
  4. Alhashmi, M., Mubin, O., & Baroud, R. (2021). Examining the use of Robots as Teacher Assistants in Uae Classrooms: Teacher and Student Perspectives. Journal of Information Technology Education: Research, 20, 245-261. https://doi.org/10.28945/4749
  5. Almousa, O., & Alghowinem, S. (2022). Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children. User Modeling and User-Adapted Interaction, 33, 261-291. https://doi.org/10.1007/s11257-022-09344-9
  6. Baddeley, A. (2007). Working Memory, Thought, and Action. Oxford University Press.
  7. Basori, A.H. (2020). Nao-Teach: Helping kids to learn societal and theoretical knowledge with friendly human-robot interaction. Indonesian Journal of Electrical Engineering and Computer Science, 17, 3, 1657-1664. https://doi:10.11591/IJEECS.V17.I3.PP1657-1664
  8. Baxter, P., & Belpaeme, T. (2016). A Cautionary Note on Personality (Extroversion) Assessments in Child-Robot Interaction Studies. University of Lincoln Institutional Repository. https://core.ac.uk/reader/163026580
  9. Baxter, P., Ashurst, E., Read, R., Kennedy, J., & Belpaeme, T. (2017). Robot Education Peers in a Situated Primary School Study: Personalisation Promotes Child Learning. PLoS ONE, 12(5), e0178126. https://journals.plos.org/plosone/article?id=10.1371journal.pone.0178126
  10. Bono, A., Augello, A., Pilato, G., Vella, F., & Gaglio, S. (2020). An ACT-R based humanoid social robot to manage storytelling activities. Robotics, 9, 25. https://doi:10.3390/robotics9020025
  11. Brown, L., Kerwin, R., & Howard, A.M. (2013). Applying Behavioral Strategies for Student Engagement Using a Robotic Educational Agent. In Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK, 13-16 October, 4360-4365.
  12. Chen, H., Park, H.W., & Breazeal, C. (2020). Teaching and learning with children: impact of reciprocal peer learning with a social robot on children’s learning and emotive engagement. Comput. Educ., 150, 103836.
  13. Crompton, H., Gregory, K., & Burke, D. (2018). Humanoid robots supporting children’s learning in an early childhood setting. British Journal of Educational Technology, 49(5), 911-927. https// doi:10.1111/bjet.12654
  14. De Souza Jeronimo, B., De Albuquerque Wheler, A.P., De Oliveira, J.P.G., Melo, R., Bastos-Filho, C.J.A. & Kelner, J. (2022). Comparing Social Robot Embodiment for Child Musical Education. Journal of Intelligent & Robotic Systems, 105, 28. https://doi:10.1007/s10846-022-01604-5
  15. Di Dio, C., Isernia, S., Ceolaro, C., Marchetti, A., & Massaro, D. (2018). Growing up Thinking of God’s Beliefs: Theory of Mind and Ontological Knowledge. Thousand Oaks, CA: Sage Open, 1-14. https://doi:10.1177/2158244018809874
  16. Di Dio, C., Manzi, F., Itakura, S., Kanda, T., Hishiguro, H., Massaro, D., & Marchetti, A. (2019). It does not matter who you are: fairness in pre-schoolers interacting with human and robotic parteners. Int. J. Soc. Robot, 1-15. https://doi:10.1007/s12369-019-00528-9
  17. Di Dio, C., Manzi, F., Peretti, G., Cangelosi, A., Harris, P.L., Massaro, D., & Marchetti, A. (2020a). Come i bambini pensano alla mente del robot. Il ruolo dell’attaccamento e della Teoria della mente nell’attribuzione di stati mentali ad un agente robotico. Sistemi Intelligenti, 32, 41-56. https://doi: 10.1422/96279
  18. Di Dio, C., Manzi, F., Peretti, G., Cangelosi, A., Harris, P.L., Massaro, D., & Marchetti, A. (2020b). Shall I trust you? From child human-robot interaction to trusting relationships. Frontiers in Psychology, 11, 469. https://doi:10.3389/fpsyg.2020.00469
  19. Feil-Seifer, D., & Mataric, M.J. (2005). Defining socially assistive robotics. In 9th International Conference on Rehabilitation Robotics, 465-468.
  20. Flanagan, T., Wong, G., & Kushnir, T. (2023). The minds of Machines: Children’s Belief About the Experiences, Thoughts, and Morals of Familiar Interactive Technologies. Developmental Psychology. https://dx.doi.org/10.1037/dev0001524
  21. Georgieva-Tsaneva, G., Andreeva, A., Tsvetkova, P., Lekova, A., Simonska, M., Stancheva-Popkostadinova, V., Dimitrov, G., Rasheva-Yordanova, K., & Kostadinova, I. (2023). Exploring the potential of social robots for speech and language therapy: a review and analysis of interactive scenarios. Machines, 11(7), 693, https://doi.org/10.3390/machines11070693
  22. Goh, H., & Aris, B. (2007). Using Robotics In Education: Lessons Learned And Learning Experiences. In Proceedings of the 1st International Malaysian Educational Technology Convention, Johor Bahru, Malaysia, 2-5 November.
  23. Imbernón Cuadrado L.-E., Manjarrés Riesco, Á., & De La Paz López, F. (2016). ARTIE: An Integrated Environment for the Development of Affective Robot Tutors. Frontiers in Computational Neuroscience, 10, 77. https://doi:10.3389/fncom.2016.00077
  24. Ioannou, A., Andreoui, E., & Christofi, M. (2015). Pre-schoolers’ interest and caring behaviour around a humanoid robot. Techtrends: Linking Research and Practice to Improve Learning, 59(2). https://doi.org/10.1007/s11528-015-0835-0
  25. Johal, W. (2020). Research Trends in Social Robots for Learning. Current Robotics Reports, 1-9. https://doi:10.1007/s43154-020-00008-3
  26. Levinson, L., Gvirsman, O., Gorodesky, I.M., Perez, A., Gonen, E., & Gordon, G. (2021). Learning in Summer Camp with Social Robots: a Morphological Study. International Journal of Social Robotics, 13(5), 999-1012. https://doi.org/10.1007/s12369-020-00689-y
  27. Lopez-Caudana, E., Ponce, P., Mazon, N., & Baltazar, G. (2022). Improving the attention span of elementary school children for physical education through an NAO robotics platform in developed countries. International Journal on Interactive Design and Manufacturing, 16(2), 657-675. https://doi.org/10.1007/s12008-022-00851-y
  28. Manzi, F., Massaro, D., Kanda, T., Tomita, K., Itakura, S., & Marchetti, A. (2017). Teoria della Mente, bambini e robot: l’attribuzione di stati mentali. In Proceedings of the Abstract de XXX Congresso Nazionale-Messina, Associazione Italiana di Psicologia, Sezione di Psicologia dello sviluppo e dell’Educazione, 14-16 September.
  29. Manzi, F., Peretti, G., Di Dio, C., Cangelosi, A., Itakura, S., Kanda, T., Ishiguro, H., Massaro, D., & Marchetti, A. (2020). A robot Is Not Worth Another: Exploring Children’s Mental State Attribution to Different Humanoid Robots. Frontiers in Psychology, 11, 2011. https://doi:10.3389/fpsyg.2020.02011
  30. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed1000097
  31. Molenaar, B., Soliño Fernández, B., Polimeno, A., Barakova, E., & Chen, A. (2021). Pitch It Right: Using Prosodic Entrainment to Improve Robot-Assisted Foreign Language Learning in School-Age Children. Multimodal Technology Interaction, 5, 76. https://doi.org/10.3390/ mti5120076
  32. National Association for the Education of Young Children. (2012). Technology and interactive media as tools in early childhood programs serving children from birth through age 8. Joint position statement issued by the National Association for the Education of Young Children and the Fred Rogers Center for Early Learning and Children’s Media at Saint Vincent College. Retrieved from https://www.naeyc.org/ content/technology-and-young-children.
  33. Neumann, M.M., Neumann, D.L., & Koch, L.-C. (2023). Young children’s interactions with a social robot during a drawing task. European Early Childhood Education Research Journal, 31(3), 421-436. https://doi.org/10.1080/1350293X.2022.2116653
  34. Obaid, M., Aylett, R., Barendregt, W., Basedow, C., Corrigan, L.J., Hall, L., Jones, A., Kappas, A., Kuster, D., Paiva, A. et al. (2018). Endowing a robotic tutor with empathic qualities: design and pilot evaluation. Int. J. Human. Rob., 15(6), 1850025.
  35. Okanda, M., & Itakura, S. (2010). When do children exhibit a “yes” bias?. Child Development, 81, 568-580. https://doi.org/10.1111/j.1467-8624.2009.01416.x
  36. Okanda, M. & Itakura, S. (2011). Do young and old preschoolers exhibit response bias due to different mechanisms? Investigating children’s response time. Journal of Experimental Child Psychology, 110, 453-460. https://doi.org/10.1016/j.jecp.2011.04.012
  37. Okanda, M, Somogyi, E., & Itakura, S. (2012). Differences in response bias among younger and older preschoolers: investigating Japanese and Hungarian preschoolers. Journal of Cross-Cultural Psychology, 43, 1325-1338. https://doi.org/10.1177/00220022112440145
  38. Okanda, M., Taniguchi, K., & Itakura, S. (2019). The role of animism tendencies and empathy in adult evaluations of robot. In Proceedings of the 7th International Conference on Human-Agent Interaction, 51–58. https://doi: 10.1145/3349537.3351891
  39. Okanda, M., & Taniguchi, K. (2022). How do children answer questions from a physically present humanoid robot?. Inf Child Dev., 31, e2300. https:// doi.org/10.1002/icd.2300
  40. Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hròbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A., Stewart, L.A., Thomas, J., Tricco, A.C., Welch, V.A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71. https://doi:10.1136/bmj.n71
  41. Park, I.-W., & Han, J. (2016). Teachers Views On The Use Of Robots And Cloud Services In Education For Sustainable Development. Cluster Computing, 19, 987999.
  42. Peretti, G., Manzi, F., Di Dio, C., Cangelosi, A., Harris, P.L., Massaro, D., & Marchetti, A. (2023). Can a robot lie? Young children’s understanding of intentionality beneath false statements. Inf Child Dev, 32, e2398. https://doi.org/10.1002/icd.2398
  43. Ponce, P., López-Orozco, C.F., Reyes, G.E.B., Lopez-Caudana, E., Parra, N.M., & Molina, A. (2022). Use of Robotic Platforms as a Tool to Support STEM and Physical Education in Developed Countries: A Descriptive Analysis. Sensors, 22, 1037. https://doi.org/10.3390/ s22031037
  44. Ramachandran, A., Huang, C.M., & Scassellati, B. (2017). Give me a break!: personalized timing strategies to promote learning in robot-child tutoring. In ACM/IEEE International Conference on Humna-Robot Interaction. https://doi.org/10.1145/2909824.3020209
  45. Riggs, N.R., Greenberg, M.T., Kusché, C.A., & Pentz, M.A. (2006). The mediational role of neurocognition in the behavioral outcomes of a social-emotional prevention program in elementary students: effects of the PATHS curriculum. Prev. Sci., 7, 91-102, https://doi:10.1007/s11121-005-0022.1
  46. Rosi, A., Dall’Asta, M., Brighenti, F., Del Rio, D., Volta, E., Baroni, I., Nalin, M., Coti Zelati, M., Sanna, A., & Scazzina, F. (2016). The use of new technologies for nutritional education in primary schools: a pilot study. Public Health, 140, 50-55. https://doi.org/10.1016/j.puhe.2016.08.021
  47. Rossi, S., Dell’Aquila, E., & Bucci, B. (2019). Evaluating the emotional valence of affective sounds for child-robot interaction. In Springer-International Conference on Social Robotics, 505-514.
  48. Shaffer, D.R., & Kipp, K. (2009). Developmental psychology: Childhood and adolescence (8th ed.). Belmont, CA: Wadsworth.
  49. Sherwood, L. (2015). Human Physiology: From Cells to Systems. Boston: Cengage Learning, 157-162.
  50. So, S., & Lee, N. (2023). Pedagogical exploration and technological development of a humanoid robotic system for teaching to and learning in young children. Cogent Education. https://doi:10.1080/2331186X.2023.2179181
  51. Stower, R., Abdelghani, R., Tschopp, M., Evangelista, K., Chetouani, M., & Kappas, A. (2022). Exploring space for robot mistakes in child robot interactions. Interaction Studies, 23(2), 243-288. https://doi.org/10.1075/is.21034.sto
  52. Tanaka, G., Cicourel, A., & Movellan, J. (2007). Socialization between toddlers and robots at an early childhood education center. In Proceedings of the National Academy of Sciences of the United States of America, 104(46), 17954-17958.
  53. Van Straten, C.L., Peter, J., Kühne, R., & Barco, A. (2020). Transparency about a robot’s lack of human psychological capacities: Effects on child-robot perception and relationship formation. Transactions on Human-Robot Interaction, 9(2), 11. https://doi.org/10.1145/3365668
  54. Van Straten, C.L., Peter, J., Kühne, R., & Barco, A. (2022). On sharing and caring: Investigating the effects of a robot’s self-disclosure and question-asking on children’s robot perceptions and child-robot relationship formation. Computer in Human Behavior, 129, 107135. https://doi.org/10.1016/j.chb.2021.107135
  55. Verhagen, J., van den Berghe, R., & Oudgenoeg-Paz, O., Kuntay, A., & Leseman, P. (2019). Children’s Reliance on the non-Verbal Cues of a Robot Versus a Human. PLoS ONE, 14(12), e0217833. https://doi:10.1371/journal.pone.0217833
  56. Woods, S., Dautenhahn, K., & Schulz, J. (2004). The design space of robots: Investigating children’s views. IEEE Xplore, https://doi:10.1109/ROMAN.2004.1374728