This module provides an introduction to the core principles and concepts of AI for non-technical students. This course covers the historical evolution of AI, its interdisciplinary nature, various applications in everyday life, and the basic technical foundations of generative AI. Students will develop a high-level understanding of fundamental AI topics such as machine learning, natural language processing, and computer vision. Through interactive lectures, case studies, and discussions, participants will gain a comprehensive understanding of AI's capabilities and limitations, preparing them to critically evaluate and engage with AI technologies in diverse professional contexts.

1. Explain the fundamental concepts of artificial intelligence, and discuss the history and evolution of the field.
2. Discuss the use of key AI technologies, including machine learning, natural language processing, computer vision and robotics.
3. Examine applications of AI across a range of industries.
4. Select, justify, and contrast AI techniques for solving real-world problems.
5. Assess opportunities and challenges presented by AI in their own professional domain.
6. Use AI to identify opportunities and lead data‑driven workplace improvements.

This module provides learners with a foundational understanding of how data underpins modern AI systems. Learners explore key concepts such as data types, data sources, data quality, and governance. The module develops the ability to evaluate whether organisational data is suitable for AI applications and to understand the implications of data quality, compliance, and privacy requirements. Emphasis is placed on practical workplace relevance and effective communication with technical teams.

1. Explain core data concepts and describe the role of data in AI systems.
2. Differentiate between common data types and sources found in organisations.
3. Assess data quality, including issues such as bias, inconsistency, missingness, and representativeness.
4. Interpret basic data summaries and dashboards to support AI‑assisted decision‑making.
5. Describe key principles of data governance, including GDPR, privacy, ethical use, and organisational policy.
6. Communicate data requirements and risks effectively to technical and non‑technical stakeholders.

This module provides learners with a practical, structured understanding of generative AI and the principles of prompt engineering. It introduces text‑based, image‑based, and workflow‑oriented generative AI systems and focusses on how these tools can be safely and effectively applied within workplace contexts. Learners develop the ability to design clear, repeatable prompts, evaluate AI‑generated content, and integrate generative AI into real work processes. The module emphasises reliability, safe use, human oversight, and aligned organisational practice.

1. Explain the principles of generative AI, including how models produce text, images, and other outputs.
2. Design structured, consistent prompts to achieve reliable results for workplace tasks.
3. Evaluate AI‑generated outputs for accuracy, relevance, consistency, bias, and appropriateness.
4. Apply generative AI tools to enhance common workplace workflows, such as writing, summarisation, document handling, ideation, and analysis.
5. Identify risks and limitations associated with generative AI, including hallucination, over‑confidence, bias, privacy, and compliance.
6. Implement human‑in‑the‑loop practices to ensure safe and responsible use of generative AI in organisational settings.

This module builds on the Generative AI & Prompt Engineering module, providing learners with practical, hands‑on experience using modern AI tools to enhance workplace productivity, communication, and decision‑making. It focuses on text‑based assistants, workplace automation tools, generative AI applications, and workflow‑based problem solving. The module emphasises safe, responsible, and context‑appropriate use of AI tools within professional settings. No programming or technical background is required.

1. Utilise a range of AI tools to support workplace tasks such as writing, summarisation, organisation, analysis, and communication.
2. Construct multi‑step AI‑assisted workflows using no‑code or low‑code platforms to enhance efficiency and consistency.
3. Evaluate AI‑generated content for accuracy, appropriateness, and reliability within professional contexts.
4. Identify suitable opportunities for AI‑supported process improvements within their own organisation or team.
5. Apply safe and responsible use practices, including privacy, confidentiality, and ethical considerations.
6. Explain the limitations and risks of AI‑assisted automation and justify the need for human oversight.

This module introduces the principles, frameworks, and regulatory considerations that underpin the responsible and compliant use of AI in modern workplaces. Learners explore ethical considerations, fairness and bias, organisational governance practices, and emerging regulatory structures including the EU AI Act and data protection legislation. The module focusses on building the knowledge and judgement required to deploy and supervise AI tools safely, with appropriate human oversight and accountability.

1. Explain key concepts in responsible AI, including fairness, transparency, accountability, and human oversight.
2. Identify ethical and societal risks associated with AI systems and generative models.
3. Describe core regulatory frameworks relevant to AI use, such as GDPR, the EU AI Act, and organisational AI governance policies.
4. Assess the risk level of an AI‑enabled workflow and recommend appropriate controls and safeguards.
5. Interpret organisational governance guidelines and apply them to real‑world workplace scenarios.
6. Develop safe‑use recommendations for AI systems, including escalation pathways, documentation, and human‑in‑the‑loop safeguards.

In this module, learners will complete a capstone project in which they identify and evaluate an AI use case or improvement opportunity within their own workplace or a selected SME.

1. Select and justify the selection of AI tools for a real workplace task or challenge.
2. Apply AI tools to produce or support a practical output, such as summaries, document workflows, analytic support, or automation.
3. Evaluate the effectiveness, limitations, and associated of the implemented solution.
4. Document data, risk, and governance considerations.
5. Present findings to technical and non‑technical audiences.