An AI tutorial for speech and language therapists: Translating concepts from the AI literature into accessible knowledge and clinically relevant applications

Background: Artificial Intelligence (AI) is increasingly discussed as a tool that can support speech and language therapy (SLT). However, clinical adoption of AI requires improved AI literacy among clinicians. AI is a rapidly evolving and often inconsistently defined field that can be difficult to navigate. Despite the definition provided by the EU AI Act, AI terminology can feel abstract for non-technical readers. Aims: To provide a foundational understanding of AI tailored for SLTs, by translating complex concepts into accessible language and organising them across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI can support SLT). Methods: This tutorial is informed by foundational AI literature, established AI taxonomies, relevant SLT literature and regulatory and ethical guidelines. Clinical analogies are used to explain technical concepts, with additional technical detail signposted where relevant. Existing and conceptual examples illustrate the relevance of AI across paediatric SLT practice. Main contribution: This tutorial provides: (i) a clinician-focussed interpretation of the EU AI Act definition; (ii) an organisation of key AI concepts into techniques, capabilities and clinical applications; (iii) a production-line model for mapping clinical needs to AI design choices and (iv) a practice-focussed discussion of ethical and regulatory considerations. Conclusion: AI is best understood as a set of techniques that enable specific capabilities, which in turn support clinical applications. This tutorial promotes the safe, ethical and accountable use of AI as a tool that can support rather than replace clinicians. WHAT THIS PAPER ADDS: What is already known on this subject Current Artificial Intelligence (AI) literature is typically designed for technical audiences, making it difficult for clinicians to interpret. This can hinder the effective and responsible integration of AI into clinical practice. What this paper adds to the existing knowledge This tutorial provides a clinician-focussed explanation of AI, structured across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI supports practice) in paediatric speech and language therapy. It also addresses key challenges, ethical considerations and regulatory requirements relevant to clinical contexts. What are the potential or actual clinical implications of this work? This tutorial lays the groundwork for informed engagement with emerging AI tools. It prepares clinicians to evaluate how different AI techniques and capabilities may support core clinical tasks (e.g., assessment, therapy planning and delivery).