The government wants the majority of attenders at the boot camp to gain an apprenticeship. The government wants the majority of attenders at the boot camp to gain an apprenticeship. Pilot scheme will provide three weeks of training as part of UK government's latest attempt to address Neets crisis Young people out of work or at risk of unemployment in the UK are to join "AI boot camps" where they harness the technology to get a foothold in the workplace. The government's latest attempt to address the crisis in Neets - young people not in work or education - involves turning to a technology that many view as a potential threat to employment. A pilot in north-west England will provide up to 70 people aged 16 to 21 with three weeks of AI training, including learning to build AI tools, understanding how businesses use AI and learning to use the technology responsibly.

The government says some 50,000 young people are expected to benefit from a programme to expand apprenticeships as it looks to tackle youth unemployment. The £725 million package, which was earmarked in the Budget and covers the next three years, will be used to create apprenticeships in sectors including AI, hospitality and engineering. Apprenticeships for people under the age of 25 at small and medium-sized businesses will be fully funded as part of the package, removing the 5% that they currently have to pay. The government is aiming to reverse a decline in the number of young people starting apprenticeships, which has fallen by almost 40% in the past decade. The funding also includes £140m for a pilot that the Department for Work and Pensions says will allow local mayors to connect young people with employers and apprenticeship opportunities, although it is unclear exactly how the money will be used.

Training and education in human-centered fields require authentic practice, yet realistic simulations of human behavior have remained limited. We present a multi-agent psychological simulation system that models internal cognitive-affective processes to generate believable human behaviors. In contrast to black-box neural models, this system is grounded in established psychological theories (e.g., self-efficacy, mindset, social constructivism) and explicitly simulates an ``inner parliament'' of agents corresponding to key psychological factors. These agents deliberate and interact to determine the system's output behavior, enabling unprecedented transparency and alignment with human psychology. We describe the system's architecture and theoretical foundations, illustrate its use in teacher training and research, and discuss how it embodies principles of social learning, cognitive apprenticeship, deliberate practice, and meta-cognition.

We're glad that R3 likes the idea of the paper and believes that the design process and methods are sound.

We present theoretical and empirical results for a framework that combines the benefits of apprenticeship and autonomous reinforcement learning. Our approach modifies an existing apprenticeship learning framework that relies on teacher demonstrations and does not necessarily explore the environment. The first change is replacing previously used Mistake Bound model learners with a recently proposed framework that melds the KWIK and Mistake Bound supervised learning protocols. The second change is introducing a communication of expected utility from the student to the teacher. The resulting system only uses teacher traces when the agent needs to learn concepts it cannot efficiently learn on its own.