Abstract
This study explores the influence of teaching methods, task complexity, and user characteristics on perceptions of teachable robots. Analysis of responses from 138 participants reveals that both Teaching with Evaluative Feedback and Teaching through Preferences were perceived as equally user-friendly and easier to use compared to the non-interactive condition. Additionally, Teaching with Evaluative Feedback enhanced robot responsiveness, while Teaching with Preferences yielded results similar to the passive Download condition, suggesting that the degree of interactivity and human guidance in the former may not substantially impact user perceptions. Personality traits, particularly extraversion and intellect, shape teaching method preferences. Task complexity influenced the perceived anthropomorphism, control, and responsiveness of the robot. Notably, the classification task led to higher anthropomorphism, control, and responsiveness scores. Our findings emphasise the importance of task design and the need of tailoring teaching methods to the user’s personality to optimise human-robot interactions, particularly in educational contexts. Project website: https://sites.google.com/view/teachable-robots.
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Acknowledgements
We thank Dimitri Lacroix for his statistical insights during the initial design of the study. This research was supported by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 955778. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.
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Tarakli, I., Di Nuovo, A. (2024). User Perception of Teachable Robots: A Comparative Study of Teaching Strategies, Task Complexity and User Characteristics. In: Ali, A.A., et al. Social Robotics. ICSR 2023. Lecture Notes in Computer Science(), vol 14454. Springer, Singapore. https://doi.org/10.1007/978-981-99-8718-4_31
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DOI: https://doi.org/10.1007/978-981-99-8718-4_31
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