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The latest data on teacher recruitment and retention in England shows that teacher supply is in a critical state, representing a substantial risk to the quality of education. Last year's initial teacher training (ITT) recruitment was below target in almost every secondary subject, while this year's applications data suggests significant improvements for the current cycle are unlikely. Meanwhile, little progress has been made on reducing high teacher workload since the pandemic, which has a strong impact on retention. This affirms that actions that are ambitious and radical, yet costeffective are urgently needed. The aim of the National Foundation for Educational Research's (NFER) annual series of Teacher Labour Market reports is to monitor the progress the school system in England is making towards meeting the teacher supply challenge. The report summarises new research and key trends in teacher recruitment, retention and working conditions and points towards policy actions that are likely to have the greatest impact on addressing the challenges. [ABSTRACT FROM AUTHOR]

Artificial intelligence is being adopted across industries at an unprecedented pace. Alongside its posited benefits, AI also presents serious risks to society, making the implementation of normative frameworks to reduce these risks a global imperative. The UNESCO Recommendation on the Ethics of AI asserts that "AI actors should make all reasonable efforts to minimize and avoid reinforcing or perpetuating discriminatory or biased applications and outcomes throughout the life cycle of the AI system to ensure fairness of such systems". To date however, AI-based systems often perpetuate (and even scale and amplify) human, structural and social biases. These biases not only prove difficult to mitigate, but may also lead to harm at the individual, collective, or societal level. This study explores biases in three significant large language models (LLMs): OpenAI's GPT-2 and ChatGPT, along with Meta's Llama 2, highlighting their role in both advanced decision-making systems and as user-facing conversational agents. Across multiple studies, the brief reveals how biases emerge in the text generated by LLMs, through gendered word associations, positive or negative regard for gendered subjects, or diversity in text generated by gender and culture. The research uncovers persistent social biases within these state-of-the-art language models, despite ongoing efforts to mitigate such issues. The findings underscore the critical need for continuous research and policy intervention to address the biases that exacerbate as these technologies are integrated across diverse societal and cultural landscapes. The emphasis on GPT-2 and Llama 2 being open-source foundational models is particularly noteworthy, as their widespread adoption underlines the urgent need for scalable, objective methods to assess and correct biases, ensuring fairness in AI systems globally. [ABSTRACT FROM AUTHOR]