Skip to main content

Design and Implementation of a Robotic Architecture for Adaptive Teaching: a Case Study on Iranian Sign Language

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.


Social robots may soon be able to play an important role in expanding communication with the deaf. Based on the literature, adaptive user interfaces lead to greater user acceptance and increased teaching efficiency compared to non-adaptive ones. In this paper, we build a robotic architecture able to simultaneously adjust a robot’s teaching parameters according to both the user’s past and present performance, adapt the content of the training, and then implement it on the RASA robot to teach sign language based on these parameters in a manner similar to a human teacher. To do this, a word to teach in sign language, repetition, speed, and emotional valence were chosen to be adaptive using a fuzzy logic mechanism. Then, two groups of participants were recruited. For the first group, the robot teaches without the adaptive architecture, while for the second group, the teaching is done with the adaptive architecture. The assessment phase was conducted with 8 users in person and 48 users virtually. A standard UTAUT questionnaire was selected to assess the effectiveness of this methodology by comparing different items from the two groups of users. Statistical analysis of the T-test and Cohen’s d effect size found that the second group felt the robot’s adaptability significantly more than the first group, indicating that the methodology used in this study was effective and that the robot’s ability to adapt was felt by users. In addition, the results of the two groups were significantly different in several other items, revealing the effects of the adaptive architecture.

This is a preview of subscription content, access via your institution.


  1. Martins, G.S., Santos, L., Dias, J.: User-adaptive interaction in social robots: a survey focusing on non-physical interaction. Int. J. Soc. Robot. 11(1), 185–205 (2018)

    Article  Google Scholar 

  2. Zakipour, M., Meghdari, A., Alem, M.: RASA: a Low-Cost Upper-Torso Social Robot Acting as a Sign Language Teaching Assistant," Presented at the International Conference on Social Robotics (2016)

  3. C. Courtin, "The impact of sign language on the cognitive development of deaf children: the case of theories of mind," J. Deaf. Stud. Deaf. Educ., vol. 5, no. 3, pp. 266–276, Summer 2000

  4. Norcio, A.F., Stanley, J.: Adaptive human-computer interfaces: a literature survey and perspective. IEEE Trans. Syst. Man. Cybernetics. 19(2), 399–408 (1989)

    Article  Google Scholar 

  5. McTear, M.F.: User modelling for adaptive computer systems: a survey of recent developments. Artif. Intell. Rev. 7(3–4), 157–184 (1993)

    Article  Google Scholar 

  6. Rossi, S., Ferland, F., Tapus, A.: User profiling and behavioral adaptation for HRI: a survey. Pattern Recogn. Lett. 99, 3–12 (2017)

    Article  Google Scholar 

  7. Karami, A.B., Mouaddib, A.I.: A decision model of adaptive interaction selection for a robot companion, pp. 83–88. ECMR (2011) Corpus ID: 15367459

  8. Karami, A.-B., Sehaba, K., Encelle, B.: Adaptive and personalised robots - learning from users' feedback. In: IEEE 25th International Conference on Tools with Artificial Intelligence, Washington DC, United States (Nov 2013).

  9. Karami, A.-B., Sehaba, K., Encelle, B.: Adaptive artificial companions learning from users' feedback. Adaptive Behavior, SAGE Publications. 24(2), 69–86 (2016).⟩

  10. Grosinger. J., Pecora, F., Saffiotti, A.: Making robots proactive through equilibrium maintenance. In: 25th International Joint Conference on Artificial Intelligence [Internet]. 2016. Available from: (March 2021)

  11. Amirabdollahian, F., et al.: Accompany: Acceptable robotiCs COMPanions for AgeiNG Years — Multidimensional aspects of human-system interactions. In: 2013 6th International Conference on Human System Interactions (HSI), pp. 570–577 (2013).

  12. Aly, A., Tapus, A.: Towards enhancing human-robot relationship: customized robot's behavior to human's profile. In: The AAAI Fall Symposium Series (AI-HRI), Arlington, Virginia, United States (Nov 2014) ⟨(hal-01265965⟩

  13. Westlunda, J.K., Gordona, G., Spauldinga, S., Leea, J.J., Plummera, L., Martinezb, M., Dasb, M., Breazeal, C.: Learning a second language with a socially assistive robot. Corpus ID. 14461956 (2015)

  14. Gordon, G., Breazeal, C.: Bayesian Active Learning-Based Robot Tutor for Children’s Word-Reading Skills," Presented at the National Conference on Artificial Intelligence (2015)

  15. Gordon, G., Spaulding, S., Westlund, J.K., Lee, J.J., Plummer, L., Martinez, M., Das, M., Breazeal, C.: Affective personalization of a social robot tutor for children's second language skills. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16), pp. 3951–3957. AAAI Press (2016)

  16. Aylett, R., et al.: I Know how that Feels – an Empathic Robot Tutor, in eChallenges e-2015 Conference Proceedings. Vilnius, Lithuania (2015)

    Google Scholar 

  17. Tozadore, D.: Project r-Castle: Robotic-Cognitive Adaptive System for Teaching and Learning (2017)

  18. Almeida, O., de Magalhaes Netto, J.F.: Adaptive educational resource model to promote robotic teaching in STEM Courses. In: 2019 IEEE Frontiers in Education Conference (FIE), pp. 1–8 (2019).

  19. Li, X., O'Regan, D., Akca, H.: Global exponential stabilization of impulsive neural networks with unbounded continuously distributed delays. IMA J. Appl. Math. 80(1), 85–99 (2013)

    Article  MathSciNet  Google Scholar 

  20. Zhang, X., He, S., Stojanovic, V., et al.: Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems. Sci. China Inf. Sci. 64, 152206 (2021).

  21. Stojanovic, V., He, S., Zhang, B.: State and parameter joint estimation of linear stochastic systems in presence of faults and non-Gaussian noises. Intl. J. Robust Nonlinear Control. 30(16), 6683–6700 (2020)

    Article  MathSciNet  Google Scholar 

  22. Zhou, L., Tao, H., Paszke, W., Stojanovic, V., Yang, H.: PD-Type Iterative Learning Control for Uncertain Spatially Interconnected Systems," Mathematics, vol. 8, no. 9, (2020)

  23. Chen, Z., Zhang, B., Stojanovic, V., Zhang, Y., Zhang, Z.: Event-based fuzzy control for T-S fuzzy networked systems with various data missing. Neurocomputing. 417, 322–332 (2020)

    Article  Google Scholar 

  24. Zadeh, L.A.: Fuzzy sets. Inf. Control. 8(3), 338–353 (1965)

    Article  Google Scholar 

  25. Carinena, P., Regueiro, C. V., Otero, A., Bugarin, A. J., Barro, S.: Landmark detection in mobile robotics using fuzzy temporal rules," vol. 12, no. 4%J Trans. Fuz Sys., pp. 423–435 (2004)

  26. Huq, R., Mann, G. K. I., Gosine, R. G.: Behavior-modulation technique in mobile robotics using fuzzy discrete event system. IEEE Transactions on Robotics, vol. 22, no. 5, (2006)

  27. Shaukat, A., Burroughes, G., Gao, Y.: Self-Reconfigurable Robotics Architecture Utilising Fuzzy and Deliberative Reasoning," Presented at the 2015 SAI Intelligent Systems Conference (IntelliSys), London, UK (2015)

  28. Fateh, M.M., Khorashadizadeh, S.: Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty. Nonlinear Dynamics. 69(3), 1465–1477 (2012)

    Article  MathSciNet  Google Scholar 

  29. Wu, C., Liu, J., Jing, X., Li, H., Wu, L.: Adaptive fuzzy control for nonlinear networked control systems. IEEE Trans. Syst. Man Cybernet.: Syst. 47(8), 2420–2430 (2017)

    Article  Google Scholar 

  30. Pelletier, F.J.: Petr Hájek. Metamathematics of fuzzy logic. Trends in logic, vol. 4. Kluwer academic publishers, Dordrecht, 1998, viii + 297 pp," Bull Symbolic Logic, vol. 6, no. 3, pp. 342–346, 2014

  31. Zadeh, L.A.: Fuzzy sets. Inf Control. 8(3), 338–353 (1965).

  32. Belpaeme, T., Vogt, P., van den Berghe, R., Bergmann, K., Göksun, T., de Haas, M., Kanero, J., Kennedy, J., Küntay, A.C., Oudgenoeg-Paz, O., Papadopoulos, F., Schodde, T., Verhagen, J., Wallbridge, C.D., Willemsen, B., de Wit, J., Geçkin, V., Hoffmann, L., Kopp, S., Krahmer, E., Mamus, E., Montanier, J.M., Oranç, C., Pandey, A.K.: Guidelines for designing social robots as second language tutors. Int. J. Soc. Robot. 10(3), 325–341 (2018)

    Article  Google Scholar 

  33. Paléologue, V., Martin, J., Pandey, A.K., Coninx, A., Chetouani, M.: Semantic-based interaction for teaching robot behavior compositions. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 50–55 (2017).

  34. Meghdari, A., Alemi, M., Zakipour, M., Kashanian, S.A.: Design and realization of a sign language educational humanoid robot. J. Intell. Robotic. Syst. 95(1), 3–17 (2018)

    Article  Google Scholar 

  35. Krägeloh, C.U., Bharatharaj, J., Sasthan Kutty, S.K., Nirmala, P.R., Huang, L.: Questionnaires to measure acceptability of social robots: a critical review. Robotics. 8, 88 (2019).

  36. Williams, M.D., Rana, N.P., Dwivedi, Y.K.: The unified theory of acceptance and use of technology (UTAUT): a literature review. J. Enterp. Inf. Manag. 28(3), 443–488 (2015)

    Article  Google Scholar 

  37. Heerink, M., Kröse, B., Evers, V., Wielinga, B.: Assessing acceptance of assistive social agent technology by older adults: the Almere model. Int. J. Soc. Robot. 2(4), 361–375 (2010)

    Article  Google Scholar 

  38. Meghdari, A., Alemi, M.: STEM teaching-learning communication strategies for deaf students, Proc. 17th international RAIS conference on social sciences and humanities, pp. 47–55. Princeton University, USA (2020)

    Google Scholar 

  39. Alemi, M., Meghdari, A.: Science and engineering education strategies along with the deaf-sign language. Iranian J. Eng. Educ. 22(85), 69–84, Spring (2020)

    Google Scholar 

  40. Alemi, M., Taheri, A., Shariati, A., Meghdari, A.: Social Robotics, Education, and Religion in the Islamic World: An Iranian Perspective. J. Sci. Eng. Ethics, vol. 26, no. 3, (2020)

Download references


This research was supported by the Iranian National Science Foundation (INSF) ( The complementary and continues support of the Social & Cognitive Robotics Laboratory by Dr. Ali Akbar Siassi Memorial Grant is also greatly appreciated.

Availability of Data and Material (Data Transparency)

All data from this project (videos of the sessions, results of the questionnaires, scores of performances, etc.) are available in the archive of the Social & Cognitive Robotics Laboratory.

Code Availability

All of the codes are available in the archive of the Social & Cognitive Robotics Laboratory. In case the readers need the codes, they may contact the corresponding author.


This research was funded by the Iranian National Science Foundation (INSF) ( (Grant No. 98025100)

Author information

Authors and Affiliations



All authors contributed to the study conception and design, material preparation, data collection and analysis were performed by Salar Basiri and Alireza Taheri. The first draft of the manuscript was written by Salar Basiri and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Alireza Taheri.

Ethics declarations

Conflicts of Interest/Competing Interests

Author Alireza Taheri has received research grants from the Iranian National Science Foundation (INSF). The authors Salar Basiri, Ali Meghdari, and Minoo Alemi declare that they have no conflict of interest.

Ethics Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Ethical approval for the protocol of this study was provided by the Iran University of Medical Sciences (#IR.IUMS.REC.1395.95301469).

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Consent for Publication

The authors affirm that human research participants provided informed consent for publication of the image in Fig. 7. All of the participants have consented to the submission of the results of this study to the journal.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Basiri, S., Taheri, A., Meghdari, A. et al. Design and Implementation of a Robotic Architecture for Adaptive Teaching: a Case Study on Iranian Sign Language. J Intell Robot Syst 102, 48 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: