In recent years, social robots have become part of a variety of human activities, especially in applications involving children, e.g., entertainment, education, companionship. The interest of this work lies in the interaction of social robots with children in the field of special education. This paper seeks to present a systematic review of the use of robots in special education, with the ultimate goal of highlighting the degree of integration of robots in this field worldwide. This work aims to explore the technologies of robots that are applied according to the impairment type of children. The study showed a large number of attempts to apply social robots to the special education of children with various impairments, especially in recent years, as well as a wide variety of social robots from the market involved in such activities. The main conclusion of this work is the finding that the specific field of application of social robots is at the first development step; however, it is expected to be of great concern to the research community in the coming years.

(1) Background: There has been significant recent interest in the potential role of social robots (SRs) in special education. Specific Learning Disorders (SpLDs) have a high prevalence in the student population, and early intervention with personalized special educational programs is crucial for optimal academic achievement. (2) Methods: We designed an intense special education intervention for children in the third and fourth years of elementary school with a diagnosis of a SpLD. Following confirmation of eligibility and informed consent, the participants were prospectively and randomly allocated to two groups: (a) the SR group, for which the intervention was delivered by the humanoid robot NAO with the assistance of a special education teacher and (b) the control group, for which the intervention was delivered by the special educator. All participants underwent pre- and post-intervention evaluation for outcome measures. (3) Results: 40 children (NAO = 19, control = 21, similar baseline characteristics) were included. Pre- and post-intervention evaluation showed comparable improvements in both groups in cognition skills (decoding, phonological awareness and reading comprehension), while between-group changes favored the NAO group only for some phonological awareness exercises. In total, no significant changes were found in any of the groups regarding the emotional/behavioral secondary outcomes. (4) Conclusion: NAO was efficient as a tutor for a human-supported intervention when compared to the gold-standard intervention for elementary school students with SpLDs.

The task of child engagement estimation when interacting with a social robot during a special educational procedure is studied. A multimodal machine learning-based methodology for estimating the engagement of the children with learning difficulties, participating in appropriate designed educational scenarios, is proposed. For this purpose, visual and audio data are gathered during the child-robot interaction and processed towards deciding an engaged state of the child or not. Six single and three ensemble machine learning models are examined for their accuracy in providing confident decisions on in-house developed data. The conducted experiments revealed that, using multimodal data and the AdaBoost Decision Tree ensemble model, the children’s engagement can be estimated with 93.33% accuracy. Moreover, an important outcome of this study is the need for explicitly defining the different engagement meanings for each scenario. The results are very promising and put ahead of the research for closed-loop human centric special education activities using social robots.

It is unclear whether prior antidiabetic treatment affects stroke severity and outcome. To evaluate this association, we prospectively studied all patients who were admitted in our Department with acute ischaemic stroke (n = 378, mean age = 78.8 ± 6.5 years). The severity of stroke was assessed at admission with the National Institutes of Health Stroke Scale. The outcome was assessed with the modified Rankin Scale at discharge and with in-hospital mortality. A total of 123 patients had type 2 diabetes mellitus. At admission, there was a trend for lower National Institutes of Health Stroke Scale in patients treated with dipeptidyl peptidase 4 inhibitors compared with patients treated with other antidiabetic agents (6.1 ± 7.5 vs 10.0 ± 9.2, respectively; p = 0.079). At discharge, patients treated with dipeptidyl peptidase 4 inhibitors had lower modified Rankin Scale than patients treated with other antidiabetic agents (2.1 ± 1.9 vs 3.2 ± 2.1, respectively; p < 0.05). Patients treated with dipeptidyl peptidase 4 inhibitors also had lower in-hospital mortality than patients treated with other antidiabetic agents (0.0% vs 15.1%, respectively; p < 0.05). In conclusion, prior treatment with dipeptidyl peptidase 4 inhibitors in patients with acute ischaemic stroke appears to be associated with better functional outcome and lower mortality risk.

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child’s behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child’s behavior.