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Endowing a Robotic Tutor with Empathic Qualities: Design and Pilot Evaluation

    https://doi.org/10.1142/S0219843618500251Cited by:20 (Source: Crossref)

    As increasingly more research efforts are geared towards creating robots that can teach and interact with children in educational contexts, it has been speculated that endowing robots with artificial empathy may facilitate learning. In this paper, we provide a background to the concept of empathy, and how it factors into learning. We then present our approach to equipping a robotic tutor with several empathic qualities, describing the technical architecture and its components, a map-reading learning scenario developed for an interactive multitouch table, as well as the pedagogical and empathic strategies devised for the robot. We also describe the results of a pilot study comparing the robotic tutor with these empathic qualities against a version of the tutor without them. The pilot study was performed with 26 school children aged 10–11 at their school. Results revealed that children in the test condition indeed rated the robot as more empathic than children in the control condition. Moreover, we explored several related measures, such as relational status and learning effect, yet no other significant differences were found. We further discuss these results and provide insights into future directions.

    References

    • 1. P. Alves-Oliveira, D. Küster, A. Kappas and A. Paiva, Psychological science in hri: Striving for a more integrated field of research, in 2016 AAAI Fall Symp. Series, 2016. Google Scholar
    • 2. J. Anghileri, Scaffolding practices that enhance mathematics learning, J. Math. Teach. Edu. 9 (1) (2006) 33–52. Google Scholar
    • 3. C. Bartneck, Interacting with an embodied emotional character, in Proc. 2003 Int. Conf. Designing Pleasurable Products and Interfaces, DPPI ’03, ACM, New York, USA, 2003, pp. 55–60. Google Scholar
    • 4. C. Daniel Batson, These Things Called Empathy: Eight Related but Distinct Phenomena (MIT Press, 2009). Google Scholar
    • 5. P. Baxter, R. Wood and T. Belpaeme, A touchscreen-based ‘sandtray’ to facilitate, mediate and contextualise human-robot social interaction, in Proc. Seventh Annual ACM/IEEE Int. Conf. Human-Robot Interaction (HRI ’12), ACM, New York, USA, 2012, pp. 105–106. Google Scholar
    • 6. T. Belpaeme, P. Baxter, J. De Greeff, J. Kennedy, R. Read, R. Looije, M. Neerincx, I. Baroni and M. C. Zelati, Child-robot interaction: Perspectives and challenges, in Int. Conf. Social Robotics (Springer, 2013), pp. 452–459. Google Scholar
    • 7. F. B. V. Benitti, Exploring the educational potential of robotics in schools: A systematic review, Comput. Edu. 58 (3) (2012) 978–988. ISIGoogle Scholar
    • 8. W. Boucsein, Electrodermal Activity (Springer Science & Business Media, 2012). Google Scholar
    • 9. J. T. Cacioppo and W. Patrick, Loneliness: Human Nature and the Need for Social Connection (WW Norton & Company, 2008). Google Scholar
    • 10. G. Castellano, I. Leite and A. Paiva, Detecting perceived quality of interaction with a robot using contextual features, Auton. Robots (2016), pp. 1–17. ISIGoogle Scholar
    • 11. G. Castellano, I. Leite, A. Pereira, C. Martinho, A. Paiva and P. W. McOwan, Affect recognition for interactive companions: Challenges and design in real world scenarios, J. Multimodal User Interf. 3 (1) (2010) 89–98. ISIGoogle Scholar
    • 12. G. Castellano, I. Leite, A. Pereira, C. Martinho, A. Paiva and P. W. Mcowan, Multimodal affect modeling and recognition for empathic robot companions, Int. J. Humanoid Robot. 10 (1) (2013) 1350010. Link, ISIGoogle Scholar
    • 13. G. Castellano, I. Leite, A. Pereira, C. Martinho, A. Paiva and P. W. Mcowan, Context-sensitive affect recognition for a robotic game companion, ACM Trans. Interact. Intell. Syst. 4 (2) (2014) 10:1–10:25. Google Scholar
    • 14. G. Castellano, A. Paiva, A. Kappas, R. Aylett, H. Hastie, W. Barendregt, F. Nabais and S. Bull, Towards empathic virtual and robotic tutors, in Int. Conf. Artificial Intelligence in Education (Springer, 2013), pp. 733–736. Google Scholar
    • 15. G. Castellano and C. Peters, Socially perceptive robots: Challenges and concerns, Interact. Stud. 11 (2) (2010) 201. ISIGoogle Scholar
    • 16. C. Clabaugh, G. Ragusa, F. Sha and M. Matarić, Designing a socially assistive robot for personalized number concepts learning in preschool children, in 2015 Joint IEEE Int. Conf. Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (IEEE, 2015), pp. 314–319. Google Scholar
    • 17. M. Coeckelbergh, Artificial companions: Empathy and vulnerability mirroring in human-robot relations, Stud. Ethics, Law, and Technol. 4 (3) (2010) (17 pages). Google Scholar
    • 18. L. J. Corrigan, C. Peters, D. Küster and G. Castellano, Engagement Perception and Generation for Social Robots and Virtual Agents (Springer International Publishing, Cham, 2016), pp. 29–51. Google Scholar
    • 19. S. Costa, A. Brunete, B.-C. Bae and N. Mavridis, Emotional storytelling using virtual and robotic agents, Int. J. Humanoid Robot. 15 (3) (2018) 1850006. Link, ISIGoogle Scholar
    • 20. N. Dahlbäck, A. Jönsson and L. Ahrenberg, Wizard of oz studies - why and how, Knowl.-Based Syst. 6 (4) (1993) 258–266. ISIGoogle Scholar
    • 21. M. H. Davis, Measuring individual differences in empathy: Evidence for a multidimensional approach, J. Pers. Social Psychol. 44 (1) (1983) 113–126. ISIGoogle Scholar
    • 22. M. H. Davis, Empathy: A Social Psychological Approach (Westview Press, 1994). Google Scholar
    • 23. J. Decety and M. Meyer, From emotion resonance to empathic understanding: A social developmental neuroscience account, Dev. Psychopathol. 20 (4) (2008) 1053–1080. ISIGoogle Scholar
    • 24. E. L. Deci, R. J. Vallerand, L. G. Pelletier and R. M. Ryan, Motivation and education: The self-determination perspective, Educ. Psychol. 26 (3–4) (1991) 325–346. Google Scholar
    • 25. F. Faul, E. Erdfelder, A.-G. Lang and A. Buchner, G*power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences, 39(2) 175–191. Google Scholar
    • 26. N. D. Feshbach and S. Feshbach, Empathy and education, in The Social Neuroscience of Empathy (MIT Press, 2009), pp. 85–98. Google Scholar
    • 27. T. Fong, I. Nourbakhsh and K. Dautenhahn, A survey of socially interactive robots, Robot. Auton. Syst. 42 (3) (2003) 143–166. ISIGoogle Scholar
    • 28. M. Fridin, Storytelling by a kindergarten social assistive robot: A tool for constructive learning in preschool education, Comput. Educ. 70 (2014) 53–64. ISIGoogle Scholar
    • 29. J. N. Fuertes, T. I. Stracuzzi, J. Bennett, J. Scheinholtz, A. Mislowack, M. Hersh and D. Cheng, Therapist multicultural competency: A study of therapy dyads, Psychother. Theor. Res. Prac. Train. 43 (4) (2006) 480. Google Scholar
    • 30. G. Gordon, S. Spaulding, J. K. Westlund, J. J. Lee, L. Plummer, M. Martinez, M. Das and C. Breazeal, Affective personalization of a social robot tutor for children’s second language skills, in Proc. Thirtieth AAAI Conf. Artificial Intelligence (AAAI’16) (AAAI Press, 2016), pp. 3951–3957. Google Scholar
    • 31. A. C. Graesser, K. Wiemer-Hastings, P. Wiemer-Hastings and R. Kreuz, Autotutor: A simulation of a human tutor, Cognitive Syst. Res. 1 (1) (1999) 35–51. Google Scholar
    • 32. L. S. Greenberg, J. C. Watson, R. Elliot and A. C. Bohart, Empathy, Psychother. Theor. Res. Prac. Train. 38 (4) (2001) 380. Google Scholar
    • 33. L. Hall, C. Hume and S. Tazzyman, Five degrees of happiness: Effective smiley face likert scales for evaluating with children, in Proc. 15th Int. Conf. Interaction Design and Children (ACM, 2016), pp. 311–321. Google Scholar
    • 34. H. Hastie, P. Dente, D. Küster and A. Kappas, Sound emblems for affective multimodal output of a robotic tutor: A perception study, in Proc. 18th ACM Int. Conf. Multimodal Interaction (ACM, 2016), pp. 256–260. Google Scholar
    • 35. E. Hatfield, J. T. Cacioppo and R. L. Rapson, Emotional contagion, Current Direct. Psychol. Sci. 2 (3) (1993) 96–100. Google Scholar
    • 36. J. Hattie and H. Timperley, The power of feedback, Rev. Educ. Res. 77 (1) (2007) 81–112. ISIGoogle Scholar
    • 37. U. Hess and A. Fischer, Emotional mimicry as social regulation, Personality Soc. Psychol. Rev. 17 (2) (2013) 142–157. ISIGoogle Scholar
    • 38. S. Higgins, E. Mercier, L. Burd and A. Joyce-Gibbons, Multi-touch tables and collaborative learning, Br. J. Educ. Technol. 43 (6) (2012) 1041–1054. ISIGoogle Scholar
    • 39. S. E. Higgins, E. Mercier, E. Burd and A. Hatch, Multi-touch tables and the relationship with collaborative classroom pedagogies: A synthetic review, Int. J. Comput. Supp. Collab. Learn. 6 (4) (2011) 515–538. ISIGoogle Scholar
    • 40. S. Janarthanam, H. Hastie, A. Deshmukh, R. Aylett and M. E. Foster, A reusable interaction management module: Use case for empathic robotic tutoring, in Proc. Sem-DIAL 2015, SEM-Dial, 2015, pp. 182–183. Google Scholar
    • 41. W. Lewis Johnson, Jeff W. Rickel and James C. Lester, Animated pedagogical agents: Face-to-face interaction in interactive learning environments, Int. J. Artif. Intell. Educ. 11 (1) (2000) 47–78. Google Scholar
    • 42. W. Lewis Johnson, P. Rizzo, W. Bosma, S. Kole, M. Ghijsen and H. van Welbergen, Generating Socially Appropriate Tutorial Dialog (Springer, Berlin, Heidelberg, 2004), pp. 254–264. Google Scholar
    • 43. A. Jones, S. Bull and G. Castellano, Personalising robot tutors’ self-regulated learning scaffolding with an open learner model, in Proc. WONDER (International Workshop on Educational Robots) Workshop, Int. Conf. Social Robotics 2015 (ICSR15), 2015, Paris, France. Google Scholar
    • 44. A. Jones, D. Küster, C. A. Basedow, P. Alves-Oliveira, S. Serholt, H. Hastie, L. J. Corrigan, W. Barendregt, A. Kappas, A. Paiva and G. Castellano, Empathic robotic tutors for personalized learning: A multidisciplinary approach, 2015. Google Scholar
    • 45. A. Kappas, E. Krumhuber and D. Küster, Facial behavior, in Handbook of Communication Science: Nonverbal Communication, eds. J. A. Hall and M. L. Knapp (Berlin, Germany: Mouton de Gruyter, 2013), pp. 131–166. Google Scholar
    • 46. A. Kappas, D. Küster, P. Dente and C. Basedow, Simply the best! creation and validation of the bremen emotional sounds toolkit, in Poster presented at the 1st Int. Convention of Psychological Science, Amsterdam, the Netherlands, 2015. Google Scholar
    • 47. J. Kennedy, P. Baxter and T. Belpaeme, The robot who tried too hard: Social behaviour of a robot tutor can negatively affect child learning, in Proc. Tenth Annual ACM/IEEE Int. Conf. Human-Robot Interaction (ACM, 2015), pp. 67–74. Google Scholar
    • 48. J. Kennedy, P. Baxter, E. Senft and T. Belpaeme, Social robot tutoring for child second language learning, in The Eleventh ACM/IEEE Int. Conf. Human Robot Interaction (HRI ’16) (IEEE Press, Piscataway, NJ, USA, 2016), pp. 231–238. Google Scholar
    • 49. D. Küster and A. Kappas, Measuring emotions online: Expression and physiology, in Cyberemotions (Springer, 2017), pp. 71–93. Google Scholar
    • 50. J. Kdzierski, P. Kaczmarek, M. Dziergwa and K. Tcho, Design for a robotic companion, Int. J. Humanoid Robot. 12 (1) (2015) 1550007. Link, ISIGoogle Scholar
    • 51. I. Leite, G. Castellano, A. Pereira, C. Martinho and A. Paiva, Long-term interactions with empathic robots: Evaluating perceived support in children, in Proc. Int. Conf. Social Robotics (Springer, 2012), pp. 298–307. Google Scholar
    • 52. I. Leite, R. Henriques, C. Martinho and A. Paiva, Sensors in the wild: Exploring electrodermal activity in child-robot interaction, in Proc. 8th ACM/IEEE Int. Conf. Human-robot Interaction (HRI ’13) (IEEE Press, Piscataway, NJ, USA, 2013), pp. 41–48. Google Scholar
    • 53. I. Leite, C. Martinho and A. Paiva, Social robots for long-term interaction: A survey, Int. J. Social Robot. 5 (2) (2013) 291–308. ISIGoogle Scholar
    • 54. D. Leyzberg, S. Spaulding and B. Scassellati, Personalizing robot tutors to individuals’ learning differences, in Proc. 2014 ACM/IEEE Int. Conf. Human-robot Interaction (ACM, 2014), pp. 423–430. Google Scholar
    • 55. J. Li, The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents, Int. J. Human-Comput. Stud. 77 (2015) 23–37. ISIGoogle Scholar
    • 56. C. Liu, K. Conn, N. Sarkar and W. Stone, Online affect detection and robot behavior adaptation for intervention of children with autism, IEEE Trans. Robot. 24 (4) (2008) 883–896. ISIGoogle Scholar
    • 57. M. M. Lusk and R. K. Atkinson, Animated pedagogical agents: Does their degree of embodiment impact learning from static or animated worked examples? Appl. Cognitive Psychol. 21 (6) (2007) 747–764. ISIGoogle Scholar
    • 58. I. B. Mauss and M. D. Robinson, Measures of emotion: A review, Cognit. Emotion 23 (2) (2009) 209–237. ISIGoogle Scholar
    • 59. A. Meghdari, A. Shariati, M. Alemi, A. A. Nobaveh, M. Khamooshi and B. Mozaffari, Design performance characteristics of a social robot companion arash for pediatric hospitals, Int. J. Humanoid Robot. 15(5) (2018) 1850019 (27 pages). Link, ISIGoogle Scholar
    • 60. R. Moreno, R. E. Mayer, H. A. Spires and J. C. Lester, The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition Instruct. 19 (2) (2001) 177–213. ISIGoogle Scholar
    • 61. O. Mubin, C. J. Stevens, S. Shahid, A. Al Mahmud and J.-J. Dong, A review of the applicability of robots in education, J. Technol. Edu. Learn. 1 (2013) 209–0015. Google Scholar
    • 62. F. Papadopoulos, D. Kster, L. J. Corrigan, A. Kappas and G. Castellano, Do relative positions and proxemics affect the engagement in a human-robot collaborative scenario? Inter. Stud. 17(3) (2016) 321–347. ISIGoogle Scholar
    • 63. M. L. Parson, Focus student attention with verbal cues, Strategies 11 (3) (1998) 30–33. Google Scholar
    • 64. A. Pereira, C. Martinho, I. Leite and A. Paiva, iCat, the chess player: The influence of embodiment in the enjoyment of a game, in Proc. 7th Int. Joint Conf. Autonomous Agents and Multiagent Systems (AAMAS ’08), Vol. 3 (Richland, SC, 2008), pp. 1253–1256. Google Scholar
    • 65. A. Powers, S. Kiesler, S. Fussell and C. Torrey, Comparing a computer agent with a humanoid robot, in Proc. ACM/IEEE Int. Conf. Human-robot Interaction (HRI ’07), (ACM, New York, USA, 2007), pp. 145–152. Google Scholar
    • 66. A. Ramachandran, C.-M. Huang and B. Scassellati, Give me a break!: Personalized timing strategies to promote learning in robot-child tutoring, in Proc. 2017 ACM/IEEE Int. Conf. Human-Robot Interaction (HRI ’17), (ACM, New York, USA, 2017), pp. 146–155. Google Scholar
    • 67. T. Ribeiro, A. Pereira, E. D. Tullio, P. Alves-Oliveira and A. Paiva, From thalamus to skene: High-level behaviour planning and managing for mixed-reality characters, in Proc. IVA 2014 Workshop on Architectures and Standards for IVAs (2014). Google Scholar
    • 68. C. Rich, B. Ponsler, A. Holroyd and C. L. Sidner, Recognizing engagement in human-robot interaction, in Proc. 5th ACM/IEEE Int. Conf. Human-Robot Interaction (HRI) (IEEE, 2010), pp. 375–382. Google Scholar
    • 69. J. L. Robison, S. W. Mcquiggan and J. C. Lester, Modeling task-based vs. affect-based feedback behavior in pedagogical agents: An inductive approach, in AIED (2009), pp. 25–32. Google Scholar
    • 70. J. A. Russell, Emotion, core affect, and psychological construction, Cognit. Emot. 23 (7) (2009) 1259–1283. ISIGoogle Scholar
    • 71. S. Šabanović, S. Reeder and B. Kechavarzi, Designing robots in the wild: In situ prototype evaluation for a break management robot, J. Hum. Robot. Inter. 3 (1) (2014) 70–88. Google Scholar
    • 72. M. Saerbeck, T. Schut, C. Bartneck and M. Janse, Expressive robots in education - varying the degree of social supportive behavior of a robotic tutor, in 28th ACM Conf. Human Factors in Computing Systems (CHI2010) (ACM, New York, 2010), pp. 1613–1622. Google Scholar
    • 73. S. Serholt, W. Barendregt, A. Vasalou, A.-O. Patricia, A. Jones, S. Petisca and A. Paiva, The case of classroom robots: Teachers’ deliberations on the ethical tensions, AI & SOCIETY 32 (2017) 613. https://doi.org/10.1007/s00146-016-0667-2. Google Scholar
    • 74. S. Serholt, C. Basedow, W. Barendregt and M. Obaid, Comparing a humanoid tutor to a human tutor delivering an instructional task to children, in Proc. 2014 IEEE-RAS Int. Conf. Humanoid Robots (2014), pp. 1134–1141. Google Scholar
    • 75. A. J. C. Sharkey, Should we welcome robot teachers? Ethics Inf. Technol. 18 (4) (2016) 283–297. ISIGoogle Scholar
    • 76. N. Sharkey and A. Sharkey, The crying shame of robot nannies: An ethical appraisal, Inter. Stud. 11 (2) (2010) 161–190. ISIGoogle Scholar
    • 77. T. Singer and C. Lamm, The social neuroscience of empathy, Ann. New York Acad. Sci. 1156 (1) (2009) 81–96. ISIGoogle Scholar
    • 78. K. Stueber, Empathy, in The Stanford Encyclopedia of Philosophy, ed. Edward N. Zalta (Metaphysics Research Lab, Stanford University, Spring 2017 edition, 2017). Google Scholar
    • 79. F. Tanaka and S. Matsuzoe, Children teach a care-receiving robot to promote their learning: Field experiments in a classroom for vocabulary learning, J. Human-Robot Interact. 1 (1) (2012) 78–95. Google Scholar
    • 80. S. Thill, C. A. Pop, T. Belpaeme, T. Ziemke and B. Vanderborght, Robot-assisted therapy for autism spectrum disorders with (partially) autonomous control: Challenges and outlook, Paladyn 3 (4) (2012) 209–217. Google Scholar
    • 81. D. Traum and S. Larsson, The information state approach to dialogue management, in Current and New Directions in Discourse and Dialogue (2003), pp. 325–353. Google Scholar
    • 82. I. Verenikina, Scaffolding and learning: Its role in nurturing new learners, in Learning and the learner: Exploring Learning for New Times, eds. P. KellW. VialleD. KonzaG. Vogl (University of Wollongong, 2008), pp. 161–180. Google Scholar
    • 83. A.-L. Vollmer, R. Read, D. Trippas and T. Belpaeme, Children conform, adults resist: A robot group induced peer pressure on normative social conformity, Science Robotics 3(21) (2018), https://doi.org/10.1126/scirobotics.aat7111. ISIGoogle Scholar
    • 84. C. A. Warren, Empathic interaction: White female teachers and their Black male students, PhD thesis, The Ohio State University (2012). Google Scholar
    • 85. K. Westlund, M. Jacqueline, S. Jeong, H. W. Park, S. Ronfard, A. Adhikari, P. L. Harris, D. DeSteno and C. L. Breazeal, Flat vs. expressive storytelling: Young childrens learning and retention of a social robots narrative. Front. Human Neurosci. 11 (2017) 295. ISIGoogle Scholar
    • 86. D. Wood and H. Wood, Vygotsky, tutoring and learning, Oxford Rev. Educ. 22 (1) (1996) 5–16. ISIGoogle Scholar
    • 87. M. Yik, J. A. Russell and J. H. Steiger, A 12-point circumplex structure of core affect, Emotion 11 (4) (2011) 705–731. ISIGoogle Scholar
    • 88. R. Yılmaz and K.-Ç. Ebru, Educational interface agents as social models to influence learner achievement, attitude and retention of learning, Comput. Educ. 59 (2) (2012) 828–838. ISIGoogle Scholar
    • 89. Z. Zeng, M. Pantic, G. I. Roisman and T. S. Huang, A survey of affect recognition methods: Audio, visual, and spontaneous expressions, IEEE Trans. Pattern Anal. Mach. Intell. 31 (1) (2009) 39–58. ISIGoogle Scholar
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