Social media and education: reconceptualizing the boundaries of formal and informal learning

© 2015 Taylor & Francis. It is argued that social media has the potential to bridge formal and informal learning through participatory digital cultures. Exemplars of sophisticated use by young people support this claim, although the majority of young people adopt the role of consumers rather than full participants. Scholars have suggested the potential of social media for integrating formal and informal learning, yet this work is commonly under-theorized. We propose a model theorizing social media as a space for learning with varying attributes of formality and informality. Through two contrasting case studies, we apply our model together with social constructivism and connectivism as theoretical lenses through which to tease out the complexities of learning in various settings. We conclude that our model could reveal new understandings of social media in education, and outline future research directions

The impact of generative artificial intelligence on socioeconomic inequalities and policy making

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI

The impact of generative artificial intelligence on socioeconomic inequalities and policy making.

Funder: Hewlett Foundation; DOI: https://doi.org/10.13039/100004439Funder: NSF; DOI: https://doi.org/10.13039/100000001Funder: Schmidt SciencesFunder: Google; DOI: https://doi.org/10.13039/100006785Funder: Sloan Foundation; DOI: https://doi.org/10.13039/100000879Funder: Smith Richardson Foundation; DOI: https://doi.org/10.13039/100001314Funder: Sloan SchoolFunder: MIT; DOI: https://doi.org/10.13039/100006919Funder: Ammodo science awardFunder: Royal Netherlands Academy of Arts and Sciences; DOI: https://doi.org/10.13039/501100001722Funder: Google JigsawFunder: Center for Conflict and CooperationGenerative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI

The impact of generative artificial intelligence on socioeconomic inequalities and policy making.

Funder: Hewlett Foundation; doi: https://doi.org/10.13039/100004439Funder: NSF; doi: https://doi.org/10.13039/100000001Funder: Schmidt SciencesFunder: Google; doi: https://doi.org/10.13039/100006785Funder: Sloan Foundation; doi: https://doi.org/10.13039/100000879Funder: Smith Richardson Foundation; doi: https://doi.org/10.13039/100001314Funder: Sloan SchoolFunder: MIT; doi: https://doi.org/10.13039/100006919Funder: Ammodo science awardFunder: Royal Netherlands Academy of Arts and Sciences; doi: https://doi.org/10.13039/501100001722Funder: Google JigsawFunder: Center for Conflict and CooperationGenerative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI

¿Son los usuarios frecuentes de las redes sociales evaluadores competentes? Un estudio de las habilidades de los adolescentes para identificar, evaluar y hacer uso de las fuentes

International audienceAre frequent users of social network sites good information evaluators? An investigation of adolescents' sourcing abilities (¿Son los usuarios frecuentes de las redes sociales evaluadores competentes? Un estudio de las habilidades de los adolescentes para identificar, evaluar y hacer uso de las fuentes). Infancia y Aprendizaje / Journal for the Study of Education and Development, 43(1), 101-138.Rouet (2019): Are frequent users of social network sites good information evaluators? An investigation of adolescents' sourcing abilities / ¿Son los usuarios frecuentes de las redes sociales evaluadores competentes? Un estudio de las habilidades de los adolescentes para identificar, evaluar y hacer uso de las fuentes, Infancia y Aprendizaje