This session presents an introduction to how generative AI is being used in social work, examining its current level of uptake, perceived benefits, and ethical considerations. 

• The different types of artificial intelligence

• The current role of artificial intelligence in professional practice

• Generative AI tools and how they support social worker teams

• Practitioners’ understanding of the ethical implications of its use, including data privacy and algorithmic bias.

This session explores the emerging role of AI technology in children’s and adult social services. It will explore how social care teams are currently using AI tools to support decision-making, manage caseloads, and access policy guidance more efficiently. A live demonstration of North Yorkshire’s Policy Buddy showcases a practical example of AI in action — an assistant designed to help social workers navigate and apply local policies quickly and accurately.

• Overview of current AI use in children’s and adult social services

• Live demonstration of North Yorkshire’s Policy Buddy AI tool

• Exploration of potential future uses of AI in social care

This session explores both formal and informal ways students and educators are engaging with AI tools and will present findings from empirical research showing that, despite its widespread use, AI adoption is often happening "underground" — with students and practitioners using AI unofficially due to a lack of clear guidance, support, or policies.

• How generative artificial intelligence is being used within social work education and learning

• Findings from empirical research which illustrate how AI use is being driven underground

• The need for AI strategies within social work learning, emphasizing the importance of ethical guidelines, transparent use, and the integration of AI literacy into professional training.

This panel discussion focuses on the ethical and equity-related challenges that arise when using AI in children's and adult social services. Panellists will explore issues such as data bias, transparency, accountability, and the potential for AI to either reduce or reinforce existing inequalities. Key themes include the importance of maintaining human oversight, ensuring diverse and inclusive data practices, safeguarding privacy, and building AI systems that support — rather than replace — professional judgment.