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.
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This research was supported by the Iranian National Science Foundation (INSF) (http://en.insf.org/). 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.
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) (http://en.insf.org/) (Grant No. 98025100)
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.
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).
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Informed consent was obtained from all individual participants included in the study.
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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). https://doi.org/10.1007/s10846-021-01413-2