The growing presence of Artificial Intelligence (AI) in society increases the exposure of children and youth to these technologies. In response, recent research introduced educational programs that foster AI knowledge and competencies, collectively comprising AI literacy. This study presents a systematic review of 23 articles published up to 2023 describing AI literacy programs for children and youth. We examined: (1) motivations for teaching AI literacy, (2) conceptualizations of AI literacy that informed program design, and (3) learning theories and pedagogical methods employed. The analysis identified five motivational themes: workforce, informed users, purposeful creators, advocacy, and social good. Seventeen AI literacy frameworks and conceptual models were identified and grouped into four themes: competency-based, computational, sociotechnical, and practice-based. Application of a three-dimensional model of literacy (operational, sociocultural, and critical), shows that the operational dimension predominates in both frameworks and program designs, the sociocultural dimension is less accentuated, and the critical dimension is least evident. Cognitive constructivism emerged as the dominant learning theory guiding program design, often supported by hands-on activities and project-based learning methods. This systematic review advances understanding of the conceptual drivers shaping AI literacy programs for children and youth. The findings highlight the need for stronger conceptualizations of sociocultural and critical AI literacies and for their more balanced integration into educational programs. Addressing these gaps would better support broad motivations for teaching AI to children and youth, such as fostering social and ethical understanding and agency, and guide future research towards more comprehensive and critically informed frameworks.

Critical thinking (CT) is widely recognized to be among the most essential skills for university graduates to successfully compete in the 21st century global economy; yet, much obscurity remains regarding its practical role in higher education. In fact, there is a need for an accepted educational plan with pedagogical guidelines that university instructors can lean on for integrating CT into subject instruction. Accordingly, this study aimed to identify the instructional design components recommended by instructors and students for promoting CT in higher education academic courses. The study involved ten higher education instructors and 342 undergraduate students in science and engineering. In order to address the research goal, the study followed the grounded theory approach in which the data was analyzed from a qualitative perspective. The data was collected through two research tools: semi-structured interviews and an online survey. The current study identified eight instructional design components for promoting CT. These components include four pedagogical practices: explicit teaching of CT, integration of digital tools, collaborative learning and constructing questions, as well as four modes of engagement: critical engagement with information, comparing approaches to problem-solving, guided inquiry and experimentation and raising doubts and evaluating credibility. The study introduces an adaptive model incorporating CT-enhanced pedagogical practices, for policymakers and instructional designers, to foster CT in science and engineering courses.

In this study, we shed light on the extent to which intersectional action is a feature of youth activism in the UK. We employed a longitudinal thematic analysis, examining both text and visual components of young activists’ social media postings, and used an intersectional solidarity framework to interpret data. Our findings show that youth activists often adopt frame-bridging engagement strategies that broadly move toward intersectional forms of activism, depending on the activism issue and current events in international and local politics. For the study’s UK context, we found that the intersectional mobilizations of social media make possible the creation of a ‘common sphere of action’, in which youth activists connect, familiarize, interact, and support each other across different activism domains. These findings advance current understandings of youth activism by demonstrating how social media cultivates intersectional activism through enabling frame bridging and cross-movement solidarity, highlighting the potential for intersectional strategies to enhance collaboration among youth movements adapting to evolving political landscapes.

This study contributes to the growing body of work on integrating Making into science education by examining how an open-ended projectile-motion design challenge can foster meaningful disciplinary engagement in physics. We report on an intervention that integrated an extended Making project into the learning of physics at the advanced high school level in the 10th grade. The intervention (18 academic hours, spread over 4 weeks) was videotaped, and post-activity interviews with selected students (5 out of 18) were conducted. We use these data to discuss how instructional design and the interaction with the facilitating teachers fostered the emergence of students’ engagement with core physics practices that organically emerged from their engagement in Making. We highlight key design considerations, how the tradeoff between openness and structure was negotiated and resolved, the teachers’ facilitation strategies and their discursive moves. We also offer practical guidance for educators seeking to support authentic disciplinary learning in Makerspaces without sacrificing students’ creativity, freedom, and ownership.

This paper discusses an intervention that integrated an extended Making experience into the learning of physics at the advanced high school level in the 10th grade, while studying projectile motion. The Maker movement has been lauded for its role in democratizing knowledge in engineering and science, for the way its open-ended, playful nature fosters creativity, collaboration, the development of adaptive expertise, and for offering alternative pathways to STEM learning [1–5]. However, while Making has been widely explored in informal education and elementary STEM learning, its integration into formal high school physics remains mainly underexplored [6, 7].

This chapter explores the educational potential of engaging students with citizen science data, positioning it as a gateway to novel scientific frontiers and data-driven practices. It outlines the unique challenges and opportunities inherent in handling large, complex datasets generated by public participation. By integrating theoretical and practical insights, the chapter proposes a framework that highlights three dimensions of data engagement: classical, scientific, and secondary data analysis. Emphasizing the bidirectional relationship between theory and data, it advocates for pedagogical strategies that help students appreciate the interplay between existing scientific theories and data collection, analysis, and interpretation. Activities designed to support each dimension aim to develop students’ data literacy and scientific reasoning, enhancing their ability to contribute meaningfully to scientific inquiries. The chapter underscores the importance of carefully structured educational interventions to maximize the pedagogical benefits of citizen science data to biology education, provides various recommendations for designing such interventions, and provides an illustrative example for a fruitful extended educational design.

This chapter presents two examples of citizen science initiatives in schools that took place in the outdoor environment and were developed from scratch by middle school science teachers. The teachers created their own research environments, research topics, and recruited scientific collaborators. They developed innovative curricula for teaching ecology that incorporates citizen science and conducted studies to assess the impact of their programs on their students.

The teacher of the 8th-grade class aimed to diversify the learning environment for his low achieving students, provide them with novel experiences, encourage them to get to know their surroundings, and cultivate their interest in science. The teacher of the honors 9th grade science class aimed to teach through inquiry using an authentic research experience that was relevant to her students and could contribute meaningfully to science. These two examples demonstrate the potential of citizen science as a teaching strategy that provides students with opportunities to learn about science through authentic research experiences, develop scientific practices, and contribute to student scientific literacy.

This chapter also discusses the design principles of citizen science in schools that took part in these projects which were developed by the Center for Citizen Science in Schools (Taking Citizen Science to Schools—TCSS). These principles can help teachers plan and implement citizen science projects that promote student learning effectively.

Today, the public is exposed to vast amounts of information from innumerable formal and informal scientific and social sources with almost as wide a range of reliability. To gauge reliability, individuals, both young and old, must harness three key types of reasoning: statistical (reasoning with data), scientific (relating the data to scientific theories), and understanding the Nature of Science (how science operates). Cultivating and integrating these forms of reasoning is a priority in contemporary science education but remains under-addressed. This instrumental case study examined how citizen science can foster these critical reasoning skills among 15–16-year-old biology students. Content analysis of the four-part learning sequence revealed that the citizen science pedagogical framework provides opportunities for students’ engagement and cultivation of the three types of reasoning.

Delivering therapeutic agents to the brain, particularly to neurons, is crucial for advancing treatments for neurological diseases. In this study, we introduce a non-invasive approach using music to enhance the delivery of lipid nanoparticles (LNPs) to the brain, potentially improving drug uptake. While LNPs have demonstrated significant potential in systemic delivery, various strategies have been explored to enhance their brain delivery. Here, we investigate the potential of audible sound waves, i.e., music, to enhance nanoparticle neuronal uptake and brain penetration. We tested different sounds across frequency ranges: low (10–250 Hz), mid (160–3800 Hz), high (1250–22,000 Hz) and a complex soundtrack of Pink Floyd “Another Brick in the Wall, Part 1” (128–5600 Hz). Low-frequency sound or soundtracks containing low frequencies in their spectrum (such as Pink Floyd) achieved significantly higher nanoparticle uptake in primary cortical neurons. Specifically, exposure of primary cortical neurons to low-frequency sound (10–250 Hz) enhanced LNP uptake and transfection in neuronal cultures, resulting in a 10-fold increase in gene expression. Low frequencies are often used for mechanical cellular stimulation, which may explain the effect of specific frequency ranges to achieve higher uptake and expression. In vivo, we found that mice that were administered with mRNA-LNP intravenously and exposed to audible low-frequency sounds had higher mRNA expression in the brain compared to non-exposed mice, with gene expression localized in the midbrain and thalamus - key regions involved in sound processing and emotional regulation. Mass spectrometry analysis of mice plasma showed that sound stimulation modulates the abundance of proteins involved in cytoskeletal dynamics and cellular uptake. Additionally, low-frequency sound exposure modulated the immune responses in mice, reducing neutrophil count in the blood and potentially lowering inflammation markers following intravenous LNP administration. In healthy human volunteers, functional MRI demonstrated that exposure to low-frequency audible sound elicited frontal, temporal, and occipital brain activation, in addition to the activation of classical auditory brain regions. This work explores a non-invasive neuro-stimulatory method for therapeutic brain delivery, potentially opening new possibilities for precision medicine, where sound waves are tailored to activate specific brain regions, for targeted treatment of neurological disorders.

This study investigates the order of examples and problems in example-based learning, a pedagogical approach that provides learners with step-by-step solutions to new problems early in the learning process. Worked examples have been found to be very effective when combined with open problems, mostly in well-structured topics. Despite extensive research in this area, questions persist about the optimal sequencing of examples and problems. The study investigates the effect of different sequences of examples and problems on middle-school students learning of inquiry competencies. One hundred and fifty-six students were randomly assigned to one of three conditions: example first, problem first, or simultaneous presentation of examples and problems. The study evaluates students’ knowledge application and transfer of learning in the topics of experimental design and graphing, as well as cognitive load during the learning process. Results show that while prior knowledge and student attributes are associated with learning outcomes, the order of examples and problems had no significant effect on learning nor on cognitive load. Findings are analyzed and discussed in the context of related (often inconclusive) studies, and potential reasons for the lack of effect are identified. The study highlights the complexity of example-based learning and suggests that additional factors, such as the type of knowledge being taught and contextual factors, may mediate its effectiveness.

Background

The rapid advancement of artificial intelligence (AI) has raised significant ethical concerns, prompting higher education institutions to reconsider how they prepare future STEM professionals to navigate such concerns responsibly. Despite growing efforts to integrate AI ethics into higher education, a lack of consensus and standardized approaches has led to inconsistent ethics education and disparities in graduates' preparedness for ethical issues related to AI. This study examined the role of higher education institutions in fostering ethical awareness in AI, focusing on institutional responsibilities and strategies as perceived by 95 science and engineering graduate students. Participants engaged in a case-based AI ethics activity, and their responses to open-ended questions were analyzed using inductive content analysis.

Results

The analysis identified three key themes regarding students’ views on institutional responsibility about AI and ethics. Most students indicated the essential responsibility of universities, citing their social, professional, educational, and reputational obligations. Others advocated for a shared responsibility, emphasizing the importance of collaboration between academia, industry, and society. A smaller group endorsed a constrained view of responsibility, questioning the feasibility and relevance of AI ethics education within academic settings. Students proposed various strategies for fostering ethical awareness, with standalone ethics courses being the most frequently discussed. Additional recommendations included interactive learning approaches, embedding ethics into curricula, and strengthening institutional leadership.

Conclusions

This study underscores the central role of higher education institutions in fostering ethical awareness in AI for science and engineering graduates, with most students emphasizing universities' societal and professional responsibilities. It highlights the need to align ethics education with technical training and professional trajectories in STEM subjects, offering actionable insights for higher education institutions to better prepare graduates for the ethical complexities of AI use and development.