Personalization and adaptation are key aspects of designing and developing effective and acceptable social robot tutors. They allow to tailor interactions towards individual needs and preferences, improve engagement and sense of familiarity over time, and facilitate trust between the user and the robot. To foster the development of autonomous adaptive social robots, we present a tutoring framework that recognizes new or previously met pupils and adapts the training experience through feedback

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... out real-time performance and the tailoring of exercises and interaction based on users' past encounters. The framework is suitable for multiparty scenarios, allowing for deployment in real-world tutoring contexts unfolding in groups. A preliminary evaluation of the framework during pilot studies and demonstration events in yoga-based training and game scenarios showed that our framework could be adapted to different contexts and populations, including children and adolescents. The robot's ability to recognize people and personalize its behavior based on the performance of previous sessions was appreciated by participants, who reported the feeling of being followed and cared for by the robot. Overall, the framework can support autonomous robot-led training by allowing monitoring of both daily performance and improvements over multiple encounters. It also lends itself to further expansion to more complex behaviors, with the organic and modular inclusion of more advanced social capabilities, such as redirecting the robot's attention to different learners or estimating participant engagement.