Artificial intelligence (AI) and automation practitioner — version 2.1
A comprehensive, AI-assisted curriculum resource pack for the ST1512 Level 4 AI & Automation Practitioner apprenticeship (Version 2.1).
Includes 121 published resource files organised across 12 progressive learning modules.
Resources include editable masterclass presentations, detailed tutor delivery notes, learner assignments, assessment guidance, practical activities, learner-facing PDFs and supplementary tutorials where provided.
The programme includes Knowledge, Skills and Behaviours (KSB) mappings to support curriculum planning and consistent delivery.
Designed for apprenticeship training providers seeking to reduce preparation time, standardise delivery and provide tutors with organised, adaptable teaching materials.
Resources are AI-assisted and should be reviewed, adapted and quality-assured by the purchasing provider before delivery.
Introduces the role and responsibilities of an AI and Automation Practitioner, including the organisational context for AI adoption, professional standards, responsible working practices and the ethical, legal and human considerations surrounding AI and automation. Learners explore opportunities and limitations of AI technologies, stakeholder needs, governance principles and the impact of automation on people and organisations, establishing the foundations for subsequent practical learning.
Explores the ethical, legal and human implications of adopting artificial intelligence and automation in the workplace. Learners examine UK data protection legislation, GDPR, employment and equality law, fairness, transparency, accountability and algorithmic bias. The module considers responsible automation, workforce impacts, human oversight, empathy and the importance of augmenting rather than replacing human capabilities. Through practical scenarios and ethical decision-making activities, learners develop the knowledge and professional judgement needed to support responsible, inclusive and legally compliant AI adoption.
Explores the capabilities, limitations and practical applications of artificial intelligence tools and intelligent technologies in the workplace. Learners develop effective prompting techniques, evaluate generative AI outputs for accuracy and bias, and apply iterative refinement and human oversight. The module examines conversational AI, intelligent assistants, cloud-based and on-premise solutions, third-party platforms and vendor selection. Through practical activities and comparisons, learners develop the skills to select and use appropriate AI technologies safely, responsibly and effectively within organisational contexts.
Develops the skills required to analyse existing business processes, identify inefficiencies and evaluate opportunities for AI and automation. Learners explore workflow mapping, process improvement, stakeholder engagement and techniques for identifying bottlenecks, duplication and waste. The module examines automation feasibility, including data quality, process maturity, organisational constraints, costs, risks and workforce impacts. Through practical process-mapping and evaluation activities, learners identify suitable automation opportunities, recommend incremental improvements and propose pilots that can be evaluated and scaled responsibly.
Develops the skills needed to gather and analyse stakeholder requirements, apply human-centred design principles and plan practical AI and automation solutions. Learners explore user needs, accessibility, user experience, solution feasibility, sustainability and organisational constraints. Through requirements-gathering and design activities, learners develop solution specifications, compare alternative approaches and produce structured pilot and delivery plans. The module emphasises collaborative working, human oversight, responsible design and alignment with organisational objectives.
Develops the knowledge and practical skills needed to prepare, analyse and manage data for AI and automation solutions. Learners explore data sources, quality, cleaning, transformation and analytical techniques, using appropriate tools to identify patterns, evaluate outcomes and support evidence-based decisions. The module examines data protection, information security, algorithmic bias, fairness and workforce equality, including methods for identifying and mitigating potential risks. Through practical data analysis and automation activities, learners apply responsible data handling, assess solution effectiveness and communicate findings to support organisational decision-making.
Develops practical skills in designing, building, integrating and improving AI and automation solutions using low-code and no-code platforms such as Microsoft Power Automate, Zapier and Make. Learners explore triggers, actions, conditions, branching, data handling, connectors and workflow integrations. Through hands-on activities, they build working automations, test functionality, identify errors and refine solutions to improve reliability, security, accessibility and performance. The module emphasises responsible development, human oversight, sustainable design and alignment with organisational requirements.
Develops advanced practical skills in integrating artificial intelligence and automation technologies using APIs, connectors, AI agents and intelligent workflows. Learners explore how systems exchange data, use external services and coordinate automated actions across platforms. The module introduces the design and configuration of AI agents, including prompting, tool integration, decision-making, limitations and human oversight. Through practical development activities, learners build integrated solutions, test reliability and security, manage errors and evaluate alternative technologies. Emphasis is placed on responsible implementation, scalability, governance and maintaining appropriate human control.
Develops the knowledge and practical skills required to test, evaluate and assure AI and automation solutions for reliability, security, accessibility and responsible operation. Learners explore testing methodologies, risk assessments, cyber security threats, algorithmic bias, model drift and human oversight. The module examines AI governance, assurance frameworks, auditability, documentation, transparency and accountability. Through practical testing and risk-management activities, learners identify vulnerabilities, evaluate solution performance, recommend improvements and produce evidence demonstrating compliance with organisational, legal and ethical requirements.
Develops the knowledge and practical skills required to support organisational change and successful adoption of AI and automation solutions. Learners explore change management principles, stakeholder engagement, inclusive communication, workforce impacts and strategies for supporting employees through technological change. The module examines approaches to developing accessible user guides, training materials and support resources for technical and non-technical audiences. Through practical activities, learners design communication and adoption plans, deliver training, gather feedback and evaluate the effectiveness of implementation. Emphasis is placed on empathy, collaboration, responsible leadership, employee wellbeing and sustainable organisational change.
Develops the skills required to evaluate the business value, productivity benefits and long-term effectiveness of AI and automation solutions. Learners explore project delivery, performance indicators, cost-benefit analysis, return on investment, benefits realisation and evidence-based reporting. The module examines methods for measuring efficiency improvements, identifying unintended consequences and assessing whether automation delivers meaningful organisational outcomes. Through practical evaluation and project-management activities, learners produce performance reports, communicate recommendations and develop continuous improvement plans. Emphasis is placed on sustainable solutions, stakeholder engagement, responsible decision-making and ongoing monitoring.
Develops the knowledge and practical skills required to govern, assure and monitor AI and automation solutions throughout their operational lifecycle. Learners explore responsible AI governance, algorithmic impact assessments, workforce equality monitoring, assurance frameworks, accountability and regulatory compliance. The module examines post-deployment risks including model drift, emerging bias, degraded performance, automation bias, over-reliance and security vulnerabilities. Through practical activities, learners produce governance and assurance documentation, assess the wider impacts of AI adoption and develop long-term monitoring and improvement plans. Emphasis is placed on transparency, fairness, human oversight, ethical decision-making and sustainable organisational outcomes.
ST1512 · Version 2.1 · Artificial Intelligence (AI) and Automation Practitioner – Level 4 (Version 2.1)
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Coverage uses explicit mappings on active, downloadable learning resources (slides, assignment tasks, learning content, handouts/workbooks, guided activities, independent study, reflection and stretch/challenge activities) in this pack. Tutor Notes and guidance Resources do not contribute. Curriculum coverage does not represent learner achievement, competence or an EPA outcome.
KSBs in this version
64
KSBs mapped
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Eligible Resources mapped
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Curriculum KSB mapping matrix
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Artificial Intelligence (AI) and Automation Practitioner – Level 4 (Version 2.1): curriculum coverage from eligible Resource mappings to current exact-Version KSBs.
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AI-assisted resources. Review and adapt them for your learners, delivery model and current requirements. No independent expert validation or guaranteed compliance is claimed.