Social media have become an integral part of our lives, expanding our interlinking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats to society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues (e.g., body stereotyping). The impact of social media—both at an individual and societal level—is characterized by the complex interplay between the users' interactions and the intelligent components of the platform. Thus, users' understanding of social media mechanisms plays a determinant role. We thus propose a theoretical framework based on an adaptive “Social Media Virtual Companion” for educating and supporting an entire community, teenage students, to interact in social media environments in order to achieve desirable conditions, defined in terms of a community-specific and participatory designed measure of Collective Well-Being (CWB). This Companion combines automatic processing with expert intervention and guidance. The virtual Companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. We put an emphasis on experts and educators in the educationally managed social media community of the Companion. They play five key roles: (a) use the Companion in classroom-based educational activities; (b) guide the definition of the CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical reinforcement learning; (d) act as moderators of direct conflicts between the members of the community; and, finally, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.

Computer-Supported Collaborative Learning (CSCL) scripts aim to structure the process of collaboration creating opportunities for productive social interaction and learning. Despite CSCL research has shown these benefits for some scripts in particular contexts, more evidence is needed about to what extent learning gains are actually achieved for more families of scripts and in different conditions of implementation. This paper studies how three CSCL scripts based on the Pyramid collaborative learning flow pattern facilitate students learning in online classes. Learning gains are measured in terms of precision and confusion assessment criteria. Students' behaviour in the learning process, regarding agreement in the knowledge exchange, is also analysed in relation to the learning gains. Results bring out several factors, and implications for the design of fruitful Pyramid scripts implementation, that related to the pedagogical envelope, the type of tasks, pyramid design elements, the need for epistemic orchestration, and debriefing.

Social media has become an important part of adolescents' lives, with an increasing number of teenagers spending a great part of their time creating, sharing, and socializing with online content. Although the popularity of social media keeps growing, different studies identified threats and dangers that exist in such networks. From harmful content to negative behaviors, users can fall victim to negative social media phenomena that can affect their mental health and wellbeing. Several media literacy initiatives have been designed to promote social media awareness amongst the youth using traditional approaches to teaching about social media risks and threats. However, these approaches are limited in enabling deep reflection about the dangers behind their social media interactions and empowering their empathy, perspective-taking, critical thinking, digital and self-protection skills. This demo paper introduces a perspective in this context proposing the integration of educational opportunities within social media. The proposed approach is designed as a social media simulated learning platform where embedded learning activities follow a novel "narrative scripting" approach, in which Computer Supported Collaborative Learning script mechanisms are combined with counter-narratives strategies.

Social media have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats for society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues (e.g. body stereotyping). The full impact of current social media platform design -both at an individual and societal level -asks for a more holistic approach to tackle the problems conceptually. The way forward we see is to extend measures of Collective Well-Being (CWB) to social media communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting an entire community, teenage students, to interact with social media. This companion combines automatic processing with expert intervention and guidance. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize 1

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