Parents of under-fives in England are to be offered official advice on how long their children should spend watching TV or looking at computer screens. The government says it will publish its first guidance on screen time for the age group in April. It comes as government research was published showing that about 98% of children under two were watching screens on a daily basis - with parents, teachers and nursery staff saying youngsters were finding it harder to hold conversations or concentrate on learning. Children with the highest screen time - around five hours a day - reportedly could say significantly fewer words than those at the other end of the scale who watched for around 44 minutes. A national working group led by Children's Commissioner for England Dame Rachel de Souza and Department for Education scientific adviser Professor Russell Viner will formulate the guidance after speaking to parents, children and early years practitioners.

Children will be taught how to budget and how mortgages work as the government seeks to modernise the national curriculum in England's schools. They will also be taught how to spot fake news and disinformation, including AI-generated content, following the first review of what is taught in schools in over a decade. Education Secretary Bridget Phillipson said the government wanted to revitalise the curriculum but keep a firm foundation in basics like English, maths and reading. Head teachers said the review's recommendations were sensible but would require sufficient funding and teachers. The government commissioned a review of the national curriculum and assessments in England last year, in the hope of developing a cutting edge curriculum that would narrow attainment gaps between the most disadvantaged students and their classmates.

AI tools will soon be in use in classrooms across England, but the education secretary, Bridget Phillipson, has one big question she wants answered: will they save time? Attending a Department for Education-sponsored hackathon in central London last week, Phillipson listened as developers explained how their tools could compile pupil reports, improve writing samples and even assess the quality of soldering done by trainee electrical engineers. After listening to one developer extol their AI writing analysis tool as "superhuman", able to aggregate all the writing a pupil had ever done, Phillipson asked bluntly: "Do you know how much time it will have saved?" That will be our next step, the developer admitted, less confidently. In an interview with the Guardian, Phillipson said her interest in AI was less futuristic and more practical.

The rapid digitalisation of contemporary society has profoundly impacted various facets of our lives, including healthcare, communication, business, and education. The ability to engage with new technologies and solve problems has become crucial, making CT skills, such as pattern recognition, decomposition, and algorithm design, essential competencies. In response, Switzerland is conducting research and initiatives to integrate CT into its educational system. This study aims to develop a comprehensive framework for large-scale assessment of CT skills, particularly focusing on AT, the ability to design algorithms. To achieve this, we first developed a competence model capturing the situated and developmental nature of CT, guiding the design of activities tailored to cognitive abilities, age, and context. This framework clarifies how activity characteristics influence CT development and how to assess these competencies. Additionally, we developed an activity for large-scale assessment of AT skills, offered in two variants: one based on non-digital artefacts (unplugged) and manual expert assessment, and the other based on digital artefacts (virtual) and automatic assessment. To provide a more comprehensive evaluation of students' competencies, we developed an IAS based on BNs with noisy gates, which offers real-time probabilistic assessment for each skill rather than a single overall score. The results indicate that the proposed instrument can measure AT competencies across different age groups and educational contexts in Switzerland, demonstrating its applicability for large-scale use. AT competencies exhibit a progressive development, with no overall gender differences, though variations are observed at the school level, significantly influenced by the artefact-based environment and its context, underscoring the importance of creating accessible and adaptable assessment tools.

Relationships among teachers are known to influence their teaching-related perceptions. We study whether and how teachers' advising relationships (networks) are related to their perceptions of satisfaction, students, and influence over educational policies, recorded as their responses to a questionnaire (item responses). We propose a novel joint model of network and item responses (JNIRM) with correlated latent variables to understand these co-varying ties. This methodology allows the analyst to test and interpret the dependence between a network and item responses. Using JNIRM, we discover that teachers' advising relationships contribute to their perceptions of satisfaction and students more often than their perceptions of influence over educational policies. In addition, we observe that the complementarity principle applies in certain schools, where teachers tend to seek advice from those who are different from them. JNIRM shows superior parameter estimation and model fit over separately modeling the network and item responses with latent variable models.

Modern Education is not Modern without AI. However, AI's complex nature makes understanding and fixing problems challenging. Research worldwide shows that a parent's income greatly influences a child's education. This led us to explore how AI, especially complex models, makes important decisions using Explainable AI tools. Our research uncovered many complexities linked to parental income and offered reasonable explanations for these decisions. However, we also found biases in AI that go against what we want from AI in education: clear transparency and equal access for everyone. These biases can impact families and children's schooling, highlighting the need for better AI solutions that offer fair opportunities to all. This chapter tries to shed light on the complex ways AI operates, especially concerning biases. These are the foundational steps towards better educational policies, which include using AI in ways that are more reliable, accountable, and beneficial for everyone involved.

In the last decade, the widespread advances in computer science and its growing presence into the organization of everyday life have established a strong interest in its inclusion in the school curriculum. The recent mass dissemination of generative artificial intelligence (AI) tools has only strengthened this interest worldwide. In Latin America, for example, the Omar Dengo Foundation and the Ministry of Education in Costa Rica have led the design and implementation of the National Computing Program in schools. Other countries, such as Uruguay or Chile, are taking steps forward through different initiatives.4 In Argentina, a public ICT institution called the Sadosky Foundationa launched the Program.AR Initiative in 2013 and has since developed a comprehensive policy for the inclusion of computer science in the formal schooling system of Argentina.

More than half of undergraduates say they consult artificial intelligence programmes to help with their essays, while schools are trialling its use in the classroom. A survey of more than 1,000 UK undergraduates, conducted by the Higher Education Policy Institute (Hepi), found 53% were using AI to generate material for work they would be marked on. One in four are using applications such as Google Bard or ChatGPT to suggest topics and one in eight are using them to create content. Just 5% admitted to copying and pasting unedited AI-generated text into their assessments. Teachers are also seeking to use AI to streamline their work, with the Education Endowment Foundation (EEF) signing up secondary schools for a new research project into the use of AI to generate lesson plans and teaching materials as well as exams and model answers.

Harvard University offers a behemoth of courses that teach its students topics including "Queering Education," "Black Radicalism" and sexual fetishes. However, its course catalog – while offering many topics some would consider strongly critical of America – shows it does not offer significant courses focusing on American patriotism in depth despite taking in hundreds of millions of taxpayer dollars every year. In 2021, Harvard received 625 million from American taxpayers, all the while the Ivy League boasts over 50 billion in its endowment. Some companies and prospective students are starting to question their interest in Harvard, particularly after scandals relating to alleged pervasive antisemitism and pro-Hamas sentiment on its campus – prompting legal action and a civil rights investigation from the U.S. Department of Education. Harvard's education department for prospective K-12 teachers elaborates on how one can bring queerness and transgenderism into schools.

Obtaining stakeholders' diverse experiences and opinions about current policy in a timely manner is crucial for policymakers to identify strengths and gaps in resource allocation, thereby supporting effective policy design and implementation. However, manually coding even moderately sized interview texts or open-ended survey responses from stakeholders can often be labor-intensive and time-consuming. This study explores the integration of Large Language Models (LLMs)--like GPT-4--with human expertise to enhance text analysis of stakeholder interviews regarding K-12 education policy within one U.S. state. Employing a mixed-methods approach, human experts developed a codebook and coding processes as informed by domain knowledge and unsupervised topic modeling results. They then designed prompts to guide GPT-4 analysis and iteratively evaluate different prompts' performances. This combined human-computer method enabled nuanced thematic and sentiment analysis. Results reveal that while GPT-4 thematic coding aligned with human coding by 77.89% at specific themes, expanding to broader themes increased congruence to 96.02%, surpassing traditional Natural Language Processing (NLP) methods by over 25%. Additionally, GPT-4 is more closely matched to expert sentiment analysis than lexicon-based methods. Findings from quantitative measures and qualitative reviews underscore the complementary roles of human domain expertise and automated analysis as LLMs offer new perspectives and coding consistency. The human-computer interactive approach enhances efficiency, validity, and interpretability of educational policy research.