AI in the Workplace

AI in the Workplace introduces the practical foundation skills employees, workers, supervisors and managers need to use common AI tools more effectively in a UK workplace context.

Across eight teaching Modules, learners explore what AI and generative AI can do, appropriate workplace uses, clear instructions and prompts, improving outputs through context and iteration, checking accuracy, data protection and cyber-security awareness, bias and fairness, and the importance of meaningful human oversight.

The course keeps the focus on practical workplace decisions rather than coding or machine-learning development. Learners are encouraged to use approved tools, protect workplace information, check important outputs and apply professional judgement before acting on AI-generated content.

The course also explains common limitations such as hallucinations and misleading confidence. It distinguishes useful AI assistance from situations that require human review, organisational approval or escalation to an appropriate specialist.

This is introductory awareness and skills training. It does not replace organisation-specific AI policies, specialist legal advice, data-protection advice, cyber-security controls or role-specific professional responsibilities

By completing this course, learners will be able to:

Describe common AI and generative-AI capabilities and limitations.

Identify practical workplace tasks that may be supported by AI.

Write clearer workplace prompts and instructions.

Use context and iteration to improve the usefulness of AI outputs.

Check important AI-generated information before relying on it.

Recognise common hallucination and misleading-confidence risks.

Apply basic data-protection, confidentiality and cyber-security awareness when using AI.

Identify potential bias or unfair effects and seek appropriate human review.

Explain the role of meaningful human oversight and organisational governance.

Use a proportionate pause-check-escalate approach when AI output may create material risk.

Module 1 - AI in the Workplace: What It Is, What It Can Do and Its Limits

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Describe, at foundation level, what workplace AI and generative AI tools can help with.

• Identify common limitations including inaccurate or fabricated outputs.

• Explain why human judgement remains necessary before AI-assisted work is used or shared.

Module 2 - Practical Workplace Uses of AI

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Identify suitable routine tasks for AI support.

• Recognise when a task needs more human judgement or specialist review.

• Use AI assistance without presenting unverified output as final work.

Module 3 - Writing Clear Instructions and Prompts

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Write a clear workplace instruction for an AI tool.

• Use context, constraints and output-format requirements where helpful.

• Recognise when an instruction is too vague to produce a reliable result.

Module 4 - Improving AI Outputs Through Context, Iteration and Settings

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Improve an initial AI output through follow-up instructions.

• Use tone, length and relevant settings appropriately.

• Know when to stop iterating and use human judgement or another source instead.

Module 5 - Checking Accuracy, Hallucinations and Critical Thinking

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Check important AI-generated information against an authoritative or original source.

• Recognise hallucinations and misleading confidence.

• Use critical thinking to decide whether an AI output is suitable for the task.

Module 6 - Data Protection, Confidentiality and Cyber Security

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Distinguish personal-data risks from wider confidential-business-information risks.

• Apply data minimisation at an awareness level.

• Recognise cyber risks including prompt injection and data exposure.

• Use only authorised tools and follow organisational controls.

Module 7 - Bias, Fairness, Transparency and Responsible Use

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Recognise that AI outputs can be biased or uneven across groups and contexts.

• Explain why fairness and transparency matter in AI-assisted work.

• Escalate potentially discriminatory, high-impact or unexplained AI-assisted outcomes.

Module 8 - Human Oversight, Accountability and Safe Workplace Adoption

Module Learning Outcomes

By the end of this Module, learners will be able to:

• Explain why meaningful human oversight is required.

• Follow organisational responsibilities, approvals and escalation routes when using AI.

• Recognise that accountability depends on the organisation, role, processing context and applicable law or policy.