Social robots exist in various forms but are still not spreading widely to the societal contexts they are envisioned for, such as educational settings. There appears to be a gap between research and practice: A lot of research already demonstrates promising results for using social robots in various domains, but this promise has yet to be realized in “the real world”. The aim of this paper is to form a systematic understanding of the potential and challenges of social robots in the domain of education and learning, which is an intensively researched application domain for social robots. We conducted a meta-review of recent literature reviews (published in 2018-2022), using the PRISMA method. We analyzed 12 review papers that met the defined inclusion criteria by extracting the potential benefits and challenges presented in these reviews. We identify six benefits, five challenges, and six recommendations for future research on social robots in education and learning. These findings emphasize the potential and developments needed to realize the potential of social robots in educational contexts.

Civic technologies are aimed at supporting citizens to participate in democratic processes. Civic robots - social robots that are designed to support people in civic participation - have potential to lower the barriers of participation especially for youth who face obstacles in participating through formal channels. We conducted an exploratory study with adolescents to investigate their perceptions of possible roles for civic robots. In the study, we asked participants of a youth event ($\mathrm{n}=24$, age range 14–21) to ideate civic participation related purposes and concepts for social robots. The findings suggest that scenarios in which a robot serves as social facilitator or provides decision support for civic activities seem promising. In addition, civic robots could indirectly increase participation ability by providing emotional or social support.

OBJECTIVE: Crowdsourcing dietary ratings for food photographs, which uses the input of several users to provide feedback, has potential to assist with dietary self-monitoring. MATERIALS AND METHODS: This study assessed how closely crowdsourced ratings of foods and beverages contained in 450 pictures from the Eatery mobile app as rated by peer users (fellow Eatery app users) (n = 5006 peers, mean 18.4 peer ratings/photo) using a simple 'healthiness' scale were related to the ratings of the same pictures by trained observers (raters). In addition, the foods and beverages present in each picture were categorized and the impact on the peer rating scale by food/beverage category was examined. Raters were trained to provide a 'healthiness' score using criteria from the 2010 US Dietary Guidelines. RESULTS: The average of all three raters' scores was highly correlated with the peer healthiness score for all photos (r = 0.88, p<0.001). Using a multivariate linear model (R(2) = 0.73) to examine the association of peer healthiness scores with foods and beverages present in photos, peer ratings were in the hypothesized direction for both foods/beverages to increase and ones to limit. Photos with fruit, vegetables, whole grains, and legumes, nuts, and seeds (borderline at p = 0.06) were all associated with higher peer healthiness scores, and processed foods (borderline at p = 0.06), food from fast food restaurants, refined grains, red meat, cheese, savory snacks, sweets/desserts, and sugar-sweetened beverages were associated with lower peer healthiness scores. CONCLUSIONS: The findings suggest that crowdsourcing holds potential to provide basic feedback on overall diet quality to users utilizing a low burden approach.

Personal well-being is influenced by small daily decisions such as what or when to eat or whether to go jogging. The health consequences of these decisions accumulate over time. Mobile applications can be designed to support people in everyday decisions and thus help to improve well-being real-time. In this paper we propose a framework to study the influential factors for users to download and use applications from the wide selection currently available in App stores. The framework includes attractiveness, value, ease-of-use, trust, social support, diffusiveness, as well as fun and excitement. We illustrated how the framework works in practise by applying it to an online survey to assess 12 mobile Apps for well-being. The results showed that these influential factors did match the decisions on users' attitudes toward taking Apps into use. User feedback explained how people assessed the influential factors before actually using the applications.