Social-emotional skills, such as recognizing, under-standing, and regulating one's emotions and those of others, along with cognitive skills like working memory, phonological aware-ness, and grapheme-phoneme correspondence, are highly valued in developing cognitive, social, and emotional competencies. During the past 10 years, there has been an increasing interest in research on social robots as teaching assistants, peers, animators, or even teachers, as numerous studies have demonstrated their beneficial impact on student social skills. The physical design and advanced capabilities of humanoid robots like SoftBank Robotics' NAO demonstrate that they represent cutting-edge technology with a wide range of functionalities, enhancing the learning experience during Child-Robot Interaction (CRI). In this work, we have designed and implemented fourteen (14) educational applications for the NAO robot, evaluating its pedagogical impact in real school settings. Specifically, nine (9) focused on emotional competencies, one (1) on social interaction and collaboration, three (3) on working memory, and one (1) on the development of phonological awareness and grapheme-phoneme correspondence. For validation and evaluation, qualitative research was carried out in kindergartens in Attica, involving 42 preschool-aged students (4–6 years) in pre- / post-test studies. Measurements were made through observation and discussion, using video recordings, evaluation / self-evaluation sheets, rubrics, and statistical analysis. Our results and findings show that the children exhibited high participation in communicating their emotions to NAO, or even imitating it, conveying various emotions through facial expres-sions, gestures and body language. The children adapted their emotional expression according to the activity guidelines, helping NAO by proposing solutions to emotional regulation (sadness) or collaborating to solve common problems. Finally, it appears that the NAO robot can enhance both working memory and phonological awareness. Our future steps involve more long-term research in a wider audience and “humanising” NAO through AI integration to provide more physical human interactions.

Human-like social robots (or humanoids) such as Softbank's NAO6, have been proven valuable assistants, able to advance State-of-the-Art of Technology in Education and Learning (TEL) as they are quite impressive “clones” of human behavior, and due to their relatable form, are often perceived as superior social companions. The rise of accessible Large Language Models and cloud computing, could transform robots like NAO6- a rather obsolete robot with quite low computing capacity (Pentium CPU, 2–4 GB RAM)- into a capable social agent, able to adopt A.I. behavior. In the current paper, we present a solution to enrich Softbank NAO6with A.I. capacity, in order to act as an LLM vessel. Specifically, we managed to connect an augmented AI chatbot to NAO6by deploying corresponding Python APIs. In our showcase, a NAO6acts as the ancient Greek Philosopher Plato that “guides the one who seeks wisdom” based on his theory. Our solution has been evaluated in real crowded settings as a proof-of-concept. Next steps involve to evaluate our solution in school classrooms.

During the last decade, social robots and humanoids have been reported as a valuable tool that advances State-of-the-Art of Technology in Education and Learning (TEL), both in General and Special Education. Social robots seem to hold the capacity to enhance both Academic and Socio-Emotional Learning (SEL) skills of young students; also they seem to promote learning through innovative applications. In the current paper, we present our attempt to evaluate the added pedagogical value of the humanoid Softbank NAO 6, and its impact on students' cognitive and SEL outcomes. We have designed and implemented five (5) applications addressing different skills (music motor, ecology awareness, cultural heritage, communication and social skills,). Furthermore, we have conducted five (5) different pilot research studies, as proof-of-concept, targeting K12 in real education settings (General & Special Education Schools, Dancing School). Totally over 115 students participated, validating our solutions. Our initial results support the positive impact of the NAO robot on both students' cognitive and emo-social benefits. Future steps prompt for a more systematic and scientific research approach to reach safer results. Furthermore, the integration of machine learning mechanisms would lead to a more ‘living robot’.

In the current study, we present how robot-based interventions can improve symbolic play and imitation skills in children with ASD (Autism Spectrum Disorder) by increasing their eye contact, attention, imitation skills, and engagement with a robot. Specifically, we deploy the humanoid NAO 6 (SoftBank Robotics) to enrich ASD students’ symbolic play skills through six education applications developed in Choregraphe software platform. The applications were evaluated in pilot studies with 5 students (2 girls and 3 boys, aged 7-10 years) in a Special Primary School for Children with Autism, for 3 months (Nov2020-Jan2021), in which they constantly interacted with the NAO. Initial results reveal improvements in eye contact, attention, and imitation skills of the children with ASD, prompting for a long-term study in our next steps.

The adoption of emotion awareness features in learning environments, has been put on the focus of the research agenda towards students' engagement and their authentic social interaction. In the current paper we provide a solution towards the provision of e-learning with emotion awareness features, which is based on self-reporting of emotions and affective response from the system. We also present research findings derived from two experiments with undergraduate students: in the University of Aegean (UoA) and the Open University of Catalonia (UoC). Finally, we set open questions for future research.

The enrichment of Computer Supported Collaborative (CSCL) Systems with emotion awareness features (detect emotion patterns and respond effectively) opens a window to the future of learner-to-computer interaction. In the current paper we present a system's design that attempts to evaluate the learner's affective state while he/she is taking part into collaborative tasks, in three different time-points: (i) before the task, (ii) in real-time (while collaborating) and (iii) retrospective (after the task). For the emotion assessment process, self-reporting and sentiment analysis are explored. Based on effective production rules that correlate emotions or emotion sequences to affective feedback techniques, the system will provide emotion scaffolds corresponding to user needs and feelings.