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This chapter reports on a study that examined the impact of COVID-19 within a context of poverty and existing emotional vulnerabilities amongst girls in an informal settlement in South Africa. Findings highlight the young people's resilience, hope and determination to stand together and draw upon each other's strengths through extremely difficult experiences. Data were collected through a survey with 19 girls aged between 12 and 17 years and analysed using Maslow's theory of human motivation (1943) and Brammer's crisis theory (1985). Living conditions and socio-economic status influenced the girls' experiences of the COVID-19 lockdowns. Social distancing, in particular, was found to be a challenge in their living conditions. Despite this, the study found the girls to be sophisticated caring agents; they were resourceful, supportive of each other and resolute in their plans for the future, despite the impact of the pandemic. Recommendations are made for further research with female adolescents to inform future strategies and interventions in South Africa's informal settlements.

The paper aims to develop and validate an instrument to measure users’ perceptions of online personalized advertising.

First, we identified 12 different aspects of online personalized advertisement and formulated candidate items through a literature review. A card sorting study and expert review were conducted to generate the initial scale items. We then conducted one survey (n = 308) to create a reliable measurement instrument and another (n = 296) to validate the instrument. Finally, we tested how the dimensions of the OPAD-Perception Framework can be used to differentiate between different levels of ad sensitivity, control/no control over the ad personalization process, and different levels of granularity of ad explanation.

The resulting OPAD-Perception Framework contains 49 Likert-formatted questions measuring ten distinct dimensions of online personalized advertising: reliability, usefulness, transparency, interactivity, targeting accuracy, accountability, creepiness, willingness to rely on, self-actualization, and persuasion.

The OPAD-Perception Framework can serve as a powerful tool to measure users’ attitudes toward online personalized advertising. This will enable advertisers and social media platforms to better support users’ privacy expectations and provide user-friendly interfaces for controlling the ad personalization process.

There is a need for precollege learning designs that empower youth to be epistemic agents in contexts that intersect burgeoning areas of computing, big data and social media. The purpose of this study is to explore how “sandbox” or open-inquiry data science with social media supports learning.

This paper offers vignettes from an illustrative youth study case that highlights the pedagogical prospects and obstacles tied to designing for open-ended inquiry with computational data science to access or “scrape” Twitter/X. The youth case showcases how social media can be taken up productively and in ways that facilitate epistemological agency, an approach where individuals actively shape understanding and knowledge-creation processes, highlighting the potentially transformative impact this approach might have in empowering learners to engage productively.

The authors identify three key affordances for learning that emerged from the illustrative case: (1) flexible opportunities for content-specific domain mastery, (2) situated inquiry that embodies next-generation science practices and (3) embedded computational skill development. The authors discuss these findings in relation to contemporary education needs to broaden participation in data science and computing.

To address challenges in current data science education associated with supporting sustained and productive engagement in computing-based data science, the authors leverage a “sandbox” approach – an original pedagogical framework to support open inquiry with precollege groups. The authors demonstrate how “big data” drawn from social media with high school-aged youth supports learning designs and outcomes by emphasizing learner interests and authentic practice.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

The Children's Hearings System is a Scottish welfare-based tribunal-based system in which decisions are made about the care and protection of children in conflict with the law and/or in need of additional care and protection. The Covid-19 pandemic resulted in the rapid implementation of a virtual Children's Hearings System. This system, which operated as the sole mechanism through which decisions were made between March and July 2020, continued to be used alongside in-person and hybrid Hearing formats for the duration of the pandemic. Early research into the use of virtual Hearings identified that their use presented significant barriers to participation, particularly in relation to the impacts of digital literacy and digital poverty. However, much of this research focused upon the experiences of adult participants in Hearings and failed to capture the experiences of children. In this chapter, we present findings from a qualitative study designed to explore the impact of virtual Hearings upon the participation and rights of children. In doing so, we demonstrate that virtual Hearings acted as both a barrier and facilitator of children's participation.

This chapter presents an international review of the experiences of children and parents regarding care during the COVID-19 pandemic. The pandemic profoundly impacted children and families, magnifying the influence of governmental policies, socio-economic disparities and cultural contexts on children's experiences and exacerbating global inequalities. Vulnerable families faced increased challenges affecting children's rights and well-being, while the transition to digital learning highlighted the critical need for equitable access to technology. Despite extensive documentation of these challenges, research focusing on the pandemic's impact on young children's development, well-being, socialization and learning opportunities, as well as the experiences of parents/carers, remains limited. This scarcity stems from the pandemic's constraints on research activities, requiring reliance on online methods and the increased burdens on parents/carers, making participation in research more challenging.

Employing the PRISMA 2020 method for a literature review, this chapter aggregates international research findings on the subject, examining the impacts of COVID-19 on health and well-being, knowledge of the pandemic, effects on learning, educational strategies, online activity engagement and collaboration with Early Childhood Education and Care (ECEC) services. It concludes with a synthesis of insights and recommendations drawn from the reviewed literature.

The chapter contributes to a comprehensive framework for understanding the pandemic's impact on young children and their families, emphasising the importance of targeted interventions, equitable resource distribution and ongoing support for the ECEC sector to address the challenges and opportunities presented by the COVID-19 pandemic and future crises.

The evolution of Artificial Intelligence (AI) in higher education marks a paradigm shift, driving significant changes in pedagogical approaches and learning methodologies. With the rise of generative AI and artificial general intelligence (AGI), institutions have witnessed a transformative era where traditional content creation and delivery are being redefined. Start-ups like OpenAI and Anthropic have been at the forefront, offering tools like ChatGPT and Claude-3, which reshape natural language processing and forecast a future where AI integrations are seamless and pervasive. This chapter provides a critical overview of the current AI-driven applications enhancing personalized learning, content generation, and remote learning. Tools such as Mainstay, CourseGenie, and AIDES demonstrate AI's capacity to improve student engagement and success rates, while Degreed and Gnowbe showcase the broadening horizons of AI in skills building and microlearning experiences. Furthermore, platforms like Elicit and Research Rabbit exemplify the transformation in research and academic writing, albeit not without raising ethical concerns. In conclusion, AI's permanence in the educational landscape is unquestionable, calling for strategic frameworks that empower educators and students to harness its benefits effectively. The imminent expansion of the AI tool ecosystem necessitates preparedness for substantial shifts in educational practices, where ethical considerations and value-based strategies become paramount. Higher education institutions must align with this technological momentum, ensuring AI's potential is maximized in an ethical, inclusive, and impactful manner.

Traditional purchasing best practices primarily follow a commercial logic and may not necessarily be applicable for social enterprises (SEs) supplier selection. This study examines how SEs focused on poverty alleviation select suppliers amidst competing institutional logics to achieve both social impact and economic performance.

A grounded theory methodology is applied to guide semi-structured interviews with 18 fair trade verified SEs. Constant comparison methods aided in determining the point of data saturation was reached.

The results of this study indicate that SEs select marginalized suppliers based on implicit criteria that is initially based on social-welfare logic and then through a blend of commercial and social-welfare logic based on company structure.

This study is the first to reveal that SEs addressing social issues do not follow the traditional criteria for supplier selection but have their own unique selection criteria when selecting suppliers.