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Designing a Socially Assistive Robot for Education Through a Participatory Design Approach: Pivotal Principles for the Developers

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Abstract

Designing socially assistive robots (SARs) for educational purposes can be a challenging task for developers. Developers need to identify the combination of a particular set of features to include in the design of a SAR. Participatory design approaches can be a promising solution since stakeholders can suggest, through their involvement, the requirements that could meet their needs and expectations. Still, such approaches for designing a SAR for education are scattered and bewildering, focusing on aspects of the robot such as the role or the appearance. The current study aimed to map stakeholders’ requirements regarding the design of a SAR exploited for educational purposes as well as to provide a set of guiding design principles for developers. A qualitative focus group discussion took place, and the participants were 127 (65 were female) stakeholders from five European countries, representing various affiliations in the field of education. A deductive qualitative content analysis approach revealed 121 themes of analysis, which fitted into 11 theory-driven categories regarding the use of the SARs in the class settings, their appearance, and their voice commands. Additionally, 46 themes of analysis were classified under five new categories following an inductive approach. The results of the deductive and inductive content analysis were further exploited in two Two-Step Cluster Analyses. The analyses revealed five tentative combinations of the dimensions exploited for the design of a SAR sketched by education stakeholders. The findings of the current study are discussed, providing pivotal guiding principles for the developers of SARs for education.

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References

  1. Hong ZW, Huang YM, Hsu M, Shen WW (2016) Authoring robot-assisted instructional materials for improving learning performance and motivation in EFL classrooms. J Educ Techno Soc 19:337–349

    Google Scholar 

  2. Janssen JB, van der Wal CC, Neerincx MA, Looije R (2011) Motivating children to learn arithmetic with an adaptive robot game. In: Mutlu B, Bartneck C, Ham J, Evers V, Kanda T (eds) International conference on social robotics. Springer, Berlin, pp 153–162

    Chapter  Google Scholar 

  3. Baroni I, Nalin M, Zelati MC, Oleari E, Sanna A (2014). Designing motivational robot: How robots might motivate children to eat fruits and vegetables. In: Vargas PA, Aylett R, Amirabdollahian F (eds) The 23rd IEEE international symposium on robot and human interactive communication IEEE, Scotland, pp 796–801. https://doi.org/10.1109/ROMAN.2014.6926350

  4. Looije R, Neerincx MA, Hindriks KV (2017) Specifying and testing the design rationale of social robots for behaviour change in children. Cogn Syst Res 43:250–265. https://doi.org/10.1016/j.cogsys.2016.07.002

    Article  Google Scholar 

  5. Belpaeme T, Kennedy J, Ramachandran A, Scassellati B, Tanaka F (2018) Social robots for education: a review. Sci Robot 3:1–9. https://doi.org/10.1126/scirobotics.aat5954

    Article  Google Scholar 

  6. Blancas M, Vouloutsi V, Fernando S, Sánchez-Fibla M, Zucca R, Prescott TJ, Mura A, Verschure PF (2017) Analysing children’s expectations from robotic companions in educational settings. In: 2017 IEEE-RAS 17th international conference on humanoid robotics (humanoids). IEEE, UK, pp 749–755

  7. Cheng YW, Sun PC, Chen NS (2018) The essential applications of educational robot: requirement analysis from the perspectives of experts, researchers and instructors. Comput Educ 126:399–416. https://doi.org/10.1016/j.compedu.2018.07.020

    Article  Google Scholar 

  8. Obaid M, Baykal GE, Yantaç AE, Barendregt W (2018) Developing a prototyping method for involving children in the design of classroom robots. Int J Soc Robot 10:279–291

    Article  Google Scholar 

  9. Reich-Stiebert N, Eyssel F, Hohnemann C (2019) Involve the user! Changing attitudes toward robots by user participation in a robot prototyping process. Comput Human Behav 91:290–296

    Article  Google Scholar 

  10. Reich-Stiebert N, Eyssel F, Hohnemann C (2019) Exploring university students’ preferences for educational robot design by means of a user-centered design approach. Int J Soc Robot. https://doi.org/10.1007/s12369-019-00554-7

    Article  Google Scholar 

  11. Pnevmatikos D, Christodoulou P, Fachantidis N (2020) Stakeholders’ involvement in participatory design approaches of learning environments: a systematic review. In: EDULEARN20 proceedings, pp 5543–5552. https://doi.org/10.21125/edulearn.2020.1454

  12. Cober R, Tan E, Slotta J, So HJ, Könings KD (2015) Teachers as participatory designers: two case studies with technology-enhanced learning environments. Instr Sci 43:203–228. https://doi.org/10.1007/s11251-014-9339-0

    Article  Google Scholar 

  13. Kalyanaraman S, Sundar SS (2006) The psychological appeal of personalized content in web portals: does customisation affect attitudes and behaviour? J Commun 56:110–132

    Article  Google Scholar 

  14. Könings KD, Seidel T, van Merriënboer JJ (2014) Participatory design of learning environments: integrating perspectives of students, teachers, and designers. Instr Sci 42:1–9. https://doi.org/10.1007/s11251-013-9305-2

    Article  Google Scholar 

  15. Bovill C (2014) An investigation of co-created curricula within higher education in the UK, Ireland and the USA. Innov Educ Teach Int 51:15–25. https://doi.org/10.1080/14703297.2013.770264

    Article  Google Scholar 

  16. Hurst A, Tobias J (2011) Empowering individuals with do-it-yourself assistive technology. In: McCoy KF, Yesilada Y (eds) Proceedings of the 13th international ACM SIGACCESS conference on computers and accessibility. ACM, Scotland, pp 11–18

  17. Phillips B, Zhao H (1993) Predictors of assistive technology abandonment. Assist Technol 5:36–45

    Article  Google Scholar 

  18. Dautenhahn K, Billard A (1999) Bringing up robots or—the psychology of socially intelligent robots: from theory to implementation. In: Etzioni O, Müller JP, Bradshaw JM (eds) Proceedings of the third annual conference on Autonomous Agents, USA, Washington, Seattle, pp 366–367

  19. Duffy B, Rooney CFB, O’Hare GMP, O’ Donoghue RPS (1999) What is a social robot? In: 10th Irish conference on artificial intelligence and cognitive science, Ireland. http://hdl.handle.net/10197/4412

  20. Fong T, Nourbakhsh I, Dautenhahn K (2003) A survey of socially interactive robots. Robot Auton Sys 42:143–166. https://doi.org/10.1016/S0921-8890(02)00372-X

    Article  MATH  Google Scholar 

  21. Breazeal C (2003) Towards sociable robots. Robot Auton Syst 42:167–175

    Article  MATH  Google Scholar 

  22. Feil-Seifer D, Mataric M J (2005) Defining socially assistive robotics. In: 9th international conference on rehabilitation robotics. IEEE, pp 465–468

  23. Burns HL, Capps CG (2013) Foundations of intelligent tutoring systems: an introduction. In: Polson MC, Richardson JJ (eds) Foundations of intelligent tutoring systems. Psychology Press, London, pp 1–20

    Google Scholar 

  24. Bayat B, Bermejo-Alonso J, Carbonera J, Facchinetti T, Fiorini S, Goncalves P, Jorge VAM, Habib M, Khamis A, Melo K, Nguyen B, Olszewska JI, Paull L, Prestes E, Ragavan V, Saeedi S, Sanz R, Seto M, Spencer B, Vosughi A, Li H (2016) Requirements for building an ontology for autonomous robots. Ind Robot 43:469–480

    Article  Google Scholar 

  25. DiSalvo C, Gemperle F, Forlizzi J, Kiesler S (2002) All robots are not equal: the design and perception of humanoid robot heads. In: Proceedings of the 4th conference on designing interactive systems: processes, practices, methods, and techniques, England, London, pp 321–326

  26. Scheeff M, Pinto J, Rahardja K, Snibbe S, Tow R (2000) Experiences with Sparky: a social robot. In: Proceedings of the workshop interactive robot entertainment, Springer, Boston, pp 173–180

  27. Schulte J, Rosenberg C, Thrun S (1999) Spontaneous, short-term interaction with mobile robots. In: Proceedings of the IEEE international conference on robotics and automation, USA, Detroit, pp 658–663

  28. Werry I, Dautenhahn K, Ogden B, Harwin W (2001) Can social interaction skills be taught by a social agent? The role of a robotic mediator in autism therapy. In: Beynon M, Nehaniv C L, Dautenhahn K (eds) Cognitive technology: instruments of mind. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, pp 57–74

  29. Zlatev J (1999) The epigenesis of meaning in human beings, and possibly in robots. Mind Mach 2:155–195

    MATH  Google Scholar 

  30. Calzado J, Lindsay A, Chen C, Samuels G, Olszewska JI (2018) SAMI: interactive, multi-sense robot architecture. In: 2018 IEEE 22nd international conference on intelligent engineering systems (INES), Las Palmas de Gran Canaria, Spain, pp 317–322

  31. Mubin O, Stevens CJ, Shahid S, Al Mahmud A, Dong JJ (2013) A review of the applicability of robots in education. JoTLT 1:1–7. https://doi.org/10.2316/Journal.209.2013.1.209-0015

    Article  Google Scholar 

  32. Olszewska JI, Houghtaling M, Goncalves PJ, Fabiano N, Haidegger T, Carbonera JL, Remington Patterson W, Ragavan VS, Fiorini SR, Prestes E (2019) Robotic standard development life cycle in action. J Intell Robot Syst 98:119–131

    Article  Google Scholar 

  33. Serholt S, Barendregt W, Leite I, Hastie H, Jones A, Paiva A, Vasalou Α, Castellano G (2014) Teachers’ views on the use of empathic robotic tutors in the classroom. In: The 23rd IEEE international symposium on robot and human interactive communication. IEEE, Edinburg, pp 955–960

  34. Orlikowski WJ, Gash DC (1994) Technological frames: making sense of information technology in organisations. ACM Trans Inf Syst (TOIS) 12:174–207

    Article  Google Scholar 

  35. Shin N, Kim S (2007) Learning about, from, and with robots: students’ perspectives. In: The 16th IEEE international symposium on robot and human interactive communication. IEEE, USA, pp 1040–1045

  36. Sciutti A, Rea F, Sandini G (2014) When you are young, (robot’s) looks matter. Developmental changes in the desired properties of a robot friend. In: The 23rd IEEE international symposium on robot and human interactive communication. IEEE, Edinburg, pp 567–573

  37. Obaid M, Barendregt W, Alves-Oliveira P, Paiva A, Fjeld M (2015) Designing robotic teaching assistants: interaction design students’ and children’s views. In: Tapus A, André E, Martin JC, Ferland F, Ammi M (eds) International conference on social robotics. Springer, Cham, pp 502–511

    Chapter  Google Scholar 

  38. Woods S (2006) Exploring the design space of robots: children’s perspectives. Interact Comput 18:1390–1418

    Article  Google Scholar 

  39. Krueger R, Casey M (2000) Focus groups: a practical guide for applied research. Sage, California

    Google Scholar 

  40. Cohen L, Manion L, Morrison K (2011) Research methods in education, 7th edn. Routledge, New York

    Google Scholar 

  41. Georgiadou T, Fotakopoulou O, Pnevmatikos D (2018) Exploring bioethical reasoning in children and adolescents using focus groups methodology. SAGE Res Methods Cases. https://doi.org/10.4135/9781526445025

    Article  Google Scholar 

  42. Finch H, Jewis J (2003) Focus groups. In: Ritchie J, Lewis J (eds) Qualitative research practice: a guide for social science students and researchers. Sage Publications, California, pp 170–198

    Google Scholar 

  43. Morgan DL, Krueger RA (1998) Developing questions for focus groups. Sage Publications, California

    Google Scholar 

  44. Stewart DW, Shamdasi PM (1990) Focus groups: theory and practice. Sage Publications, California

    Google Scholar 

  45. Lee KM, Jung Y, Kim J, Kim SR (2006) Are physically embodied social agents better than disembodied social agents?: The effects of physical embodiment, tactile interaction, and people’s loneliness in human–robot interaction. Int J Hum Comput St 64:962–973

    Article  Google Scholar 

  46. Mavridis N, Bourlai T, Ognibene D (2012) The human–robot cloud: situated collective intelligence on demand. In: 2012 IEEE international conference on cyber technology in automation, control, and intelligent systems, pp 360–365

  47. Mavridis N, Datta C, Emami S, Tanoto A, BenAbdelkader C, Rabie T (2009) FaceBots: robots utilising and publishing social information in facebook. In: 2009 4th ACM/IEEE international conference on human–robot interaction, pp 273–274

  48. Strauss A, Corbin J (1998) Basics of qualitative research techniques. Sage Publications, California

    Google Scholar 

  49. Etikan I, Musa SA, Alkassim RS (2016) Comparison of convenience sampling and purposive sampling. Am J Theor Appl Stat 5:1–4

    Article  Google Scholar 

  50. David M, Sutton C (2011) Social research: an introduction, 2nd edn. Sage Publications, London

    Google Scholar 

  51. Krueger RA, Casey MA (2014) Focus groups: a practical guide for applied research. Sage Publications, California

    Google Scholar 

  52. Ritchie J, Lewis J, Elam G (2003) Designing and selecting samples. In: Ritchie J, Lewis J (eds) Qualitative research practise: a guide for social science students and researchers. Sage Publications, California, pp 111–142

    Google Scholar 

  53. Patton MQ (2002) Qualitative research and evaluations methods. Sage Publications, California

    Google Scholar 

  54. Lewis J (2003) Design Issues. In: Ritchie J, Lewis J (eds) Qualitative research practise: a guide for social science students and researchers. Sage Publications, California, pp 47–76

    Google Scholar 

  55. Bryman A (2016) Social research methods, 5th edn. Oxford University Press, Oxford

    Google Scholar 

  56. Hwang S (2008) Utilising qualitative data analysis software: a review of Atlas. ti. Soc Sci Comput Rev 26:519–527

    Article  Google Scholar 

  57. Gill P, Stewart K, Treasure E, Chadwick B (2008) Methods of data collection in qualitative research: interviews and focus groups. Br Dent J 204:291–295

    Article  Google Scholar 

  58. Elo S, Kääriäinen M, Kanste O, Pölkki T, Utriainen K, Kyngäs H (2014) Qualitative content analysis: a focus on trustworthiness. SAGE Open 4:1–10. https://doi.org/10.1177/2158244014522633

    Article  Google Scholar 

  59. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46

    Article  Google Scholar 

  60. Mayer JD (2007) Asserting the definition of personality. The online newsletter for personality science 1: 1–4. https://www.personality-arp.org/html/newsletter01/jdm.pdf

  61. Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. Wiley, New Jersey

    MATH  Google Scholar 

  62. Satish SM, Bharadhwaj S (2010) Information search behavior among new car buyers: a two-step cluster analysis. IIMB Manag Rev 22:5–15

    Article  Google Scholar 

  63. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33:261–304. https://doi.org/10.1177/0049124104268644

    Article  MathSciNet  Google Scholar 

  64. Claeskens G, Jansen M (2015) Model selection and model averaging. In: Wright JD (ed) International encyclopedia of the social and behavioral sciences, 2nd edn. Elsevier, pp 647–652. 10.1016/B978-0-08-097086-8.42057-X

    Chapter  Google Scholar 

  65. Van de Schoot R, Kaplan D, Denissen J, Asendorpf JB, Neyer FJ, Van Aken MA (2014) A gentle introduction to Bayesian analysis: applications to developmental research. Child Dev 85:842–860

    Article  Google Scholar 

  66. Fachantidis N, Dimitriou AG, Pliasa S, Dagdilelis V, Pnevmatikos D, Perlantidis P, Papadimitriou A (2017) Android OS mobile technologies meets robotics for expandable, exchangeable, reconfigurable, educational, STEM-enhancing, socializing robot. In: Interactive mobile communication, technologies and learning. Springer, Cham, pp 487–497

  67. Gregor S (2006) The nature of theory in information systems. MIS Q 30:611–642

    Article  Google Scholar 

  68. Cagan J, Cagan JM, Vogel CM (2002) Creating breakthrough products: innovation from product planning to program approval. Ft Press, New York

    Google Scholar 

  69. Takala R, Keinonen T, Mantere J (2006) Processes of product concepting. In: Keinonen T, Takala R (eds) Product concept design. Springer, Berlin, pp 57–90. https://doi.org/10.1007/978-1-84628-126-6_3

    Chapter  Google Scholar 

  70. Spyrtou A, Lavonen J, Zoupidis A, Loukomies A, Pnevmatikos D, Juuti K, Kariotoglou P (2018) Transferring a teaching learning sequence between two different educational contexts: the case of Greece and Finland. Int J Sci Math Educ 16:443–463

    Article  Google Scholar 

  71. Mavridis N, Katsaiti MS, Naef S, Falasi A, Nuaimi A, Araifi H, Kitbi A (2012) Opinions and attitudes toward humanoid robots in the Middle East. AI Soc 27:517–534

    Article  Google Scholar 

  72. LeTendre GK, Baker DP, Akiba M, Goesling B, Wiseman A (2001) Teachers’ work: Institutional isomorphism and cultural variation in the US, Germany, and Japan. Educ Res 30:3–15

    Article  Google Scholar 

  73. Howard A, Borenstein J (2018) The ugly truth about ourselves and our robot creations: the problem of bias and social inequity. Sci Eng Ethics 24:1521–1536

    Article  Google Scholar 

  74. Davison DP, Wijnen FM, van der Meij J, Reidsma D, Evers V (2019) Designing a social robot to support children’s inquiry learning: a contextual analysis of children working together at school. Int J Soc Robot 12:883–907. https://doi.org/10.1007/s12369-019-00555-6

    Article  Google Scholar 

  75. Feil-Seifer D, Matarić MJ (2009) Toward socially assistive robotics for augmenting interventions for children with autism spectrum disorders. In: Khatib O, Kumar V, Pappas GJ (eds) Experimental robotics. Springer Tracts in Advanced Robotics, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00196-3_24

    Chapter  Google Scholar 

  76. Kujala S, Kauppinen M, Rekola S (2001) Bridging the gap between user needs and user requirements. In: Advances in human–computer interaction I (proceedings of the panhellenic conference with international participation in human–computer interaction PC-HCI 2001). Typorama Publications, pp 45–50

  77. Mayer R, Mayer RE (2005) The Cambridge handbook of multimedia learning. Cambridge University Press, New York

    Book  Google Scholar 

  78. Fitter NT, Strait M, Bisbee E, Mataric MJ, Takayama L (2021) You’re wigging me out! Is personalization of telepresence robots strictly positive?. In: Proceedings of the 2021 ACM/IEEE international conference on human–robot interaction, ACM, New York, pp 168–176

  79. Sharkey AJ (2016) Should we welcome robot teachers? Ethics Inf Technol 18:283–297. https://doi.org/10.1007/s10676-016-9387-z

    Article  Google Scholar 

  80. Sharkey NE, Sharkey AJC (2010) The crying shame of robot nannies: an ethical appraisal. Interact Stud 11:161–190. https://doi.org/10.1075/is.11.2.01sha

    Article  Google Scholar 

  81. Sparrow R, Sparrow L (2006) In the hands of machines? The future of aged care. Mind Mach 16:141–161. https://doi.org/10.1007/s11023-006-9030-6

    Article  Google Scholar 

  82. Tzafestas SG (2016) Socialised roboethics. In: Tzafestas SG (ed) Roboethics. Springer, Cham, pp 107–137

    Chapter  Google Scholar 

  83. Wallach W, Allen C (2009) Moral machines: teaching robots right from wrong. Oxford University Press, New York

    Book  Google Scholar 

  84. Kulyukin VA (2006) On natural language dialogue with assistive robots. In: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human–robot interaction. ACM, New York, pp 164–171

  85. Robertson LJ, Abbas R, Alici G, Munoz A, Michael K (2019) Engineering-based design methodology for embedding ethics in autonomous robots. In: Proceedings of the IEEE, pp 582–599. https://doi.org/10.1109/JPROC.2018.2889678

  86. Woolf B, Burleson W, Arroyo I, Dragon T, Cooper D, Picard R (2009) Affect-aware tutors: recognising and responding to student affect. Int J Learn Technol 4:129–164

    Article  Google Scholar 

  87. Costa S, Brunete A, Bae BC, Mavridis N (2018) Emotional storytelling using virtual and robotic agents. Int J Humanoid Robot 15:1850006

    Article  Google Scholar 

  88. Deuerlein C, Langer M, Seßner J, Heß P, Franke J (2021) Human–robot-interaction using cloud-based speech recognition systems. In: Makris S (ed) 8th CIRP conference of assembly technology and systems, vol 97. pp 130–135. https://doi.org/10.1016/j.procir.2020.05.214

  89. Huijnen CA, Lexis MA, Jansens R, de Witte LP (2016) Mapping robots to therapy and educational objectives for children with autism spectrum disorder. J Autism Dev Disord 46:2100–2114. https://doi.org/10.1007/s10803-016-2740-6

    Article  Google Scholar 

  90. Lee KM, Peng W, Jin SA, Yan C (2006) Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction. J Commun 56:754–772

    Article  Google Scholar 

  91. Jones A, Bull S, Castellano G (2018) “I know that now, i’m going to learn this next” promoting self-regulated learning with a robotic tutor. Int J Soc Robot 10:439–454

    Article  Google Scholar 

  92. Pnevmatikos D, Christodoulou P, Fachantidis N (2018) Promoting critical thinking dispositions in children and adolescents through human–robot interaction with socially assistive robots. In: Tsitouridou M, Diniz J, Mikropoulos T (eds) Technology and innovation in learning, teaching and education. TECH-EDU 2018. Communications in computer and information science, Springer, Cham, pp 153–165

  93. Cho J, Trent A (2006) Validity in qualitative research. Qual Res 6:319–340. https://doi.org/10.1177/1468794106065006

    Article  Google Scholar 

  94. Koelsch LE (2013) Reconceptualizing the member check interview. Int J Qual Methods 12:168–179. https://doi.org/10.1177/160940691301200105

    Article  Google Scholar 

  95. Christodoulou P, May Reid AA, Pnevmatikos D, Rioja del Rio C, Fachantidis N (2020) Students participate and evaluate the design and development of a social robot. In: Proceedings of the 29th IEEE international symposium on robot and human interactive communication. IEEE, pp 739–744

  96. Peffers K, Marcus R, Tuure T, Reza V (2012). Design science research evaluation. In: Peffers K, Rothenberger M, Kuechler B (eds) Design science research in information systems. advances in theory and practice. DESRIST 2012. Lecture Notes in Computer Science, Springer, pp 398–410. https://doi.org/10.1007/978-3-642-29863-9_29

  97. Hevner AR (2007) A three cycle view of design science research. Scand J Inf Syst 19:87–92

    Google Scholar 

  98. Hevner AR, Salvatore TM, Jinsoo P, Sudha R (2004) Design science in information systems research. MIS Q 28:75–105

    Article  Google Scholar 

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Acknowledgement

We acknowledge the effort the colleagues Manal Assaad, Sviatlana Astapchuk, Christina Haaf, Hanna Immonen, Tiina Mäkelä, Juho Mäkiö, Eduard Pavlysh, Alecia A. M Reid., Noemi Serrano Diaz, and Evgeniia Surkova, who, except the authors, contributed to the study by collecting the data from the focus groups.

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Funding was provided by EU-HORIZON2020 (709515).

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  1. University of Western Macedonia, Kozani, Greece

    Dimitris Pnevmatikos & Panagiota Christodoulou

  2. University of Macedonia, Thessaloniki, Greece

    Nikolaos Fachantidis

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  1. Dimitris Pnevmatikos
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  2. Panagiota Christodoulou
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  3. Nikolaos Fachantidis
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Correspondence to Dimitris Pnevmatikos.

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Appendix

Appendix

See Tables 3, 4 and

Table 3 The subcategories and categories emerged from participants’ wishes regarding the assisting role of the SAR in the class
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Table 4 Categories and Subcategories that emerged for the question of what the SAR would look like
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Table 5 The categories and subcategories related to participants’ wishes regarding the voice commands of the SAR
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Pnevmatikos, D., Christodoulou, P. & Fachantidis, N. Designing a Socially Assistive Robot for Education Through a Participatory Design Approach: Pivotal Principles for the Developers. Int J of Soc Robotics 14, 763–788 (2022). https://doi.org/10.1007/s12369-021-00826-1

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