The work in plain language
Understand the answer. Notice what is missing.
AI literacy training teaches employees to recognise appropriate AI tasks, understand basic limitations and check outputs before relying on them. Paloren uses workplace scenarios, fictional source material and practical assessment to build shared judgement across a team. Learners practise task selection, instructions, information handling and escalation rather than only watching demonstrations. No coding experience is needed. Approved tool access is prepared beforehand when hands-on work is included. Follow-up identifies remaining learning or policy gaps; course participation alone is not a certification or proof of regulatory compliance.
01 / 04AI literacy
Learn enough to make a sound working decision
AI literacy training gives employees a practical understanding of the systems they encounter, without requiring them to become engineers. Learners distinguish generating a response from finding a verified fact, and a suggested action from an authorised action. They examine why a fluent answer can still leave out important information. The vocabulary stays connected to work: prompts are instructions, context is the material available for a task, and evaluation is checking against an agreed standard. The objective is not to memorise definitions. It is to recognise when AI assistance fits a task, what evidence is needed and when another method or person is more appropriate.
- Generation versus verified source retrieval
- Suggested action versus authorised action
- Task selection based on consequences and evidence
02 / 04AI literacy
Use a simple agenda with visible mistakes
The literacy agenda starts with learners sorting example tasks into suitable, needs-review and do-not-use categories under a supplied policy. Next, they compare a generated summary with its source and find an omitted exception. Guided prompting practice follows using a fictional internal announcement, an audience and a clear output format. Learners then review a deliberately flawed response containing an unsupported claim. The final exercise asks them to choose a method and explain their checks without a facilitator leading each step. This can be organised as a half-day workshop or shorter linked sessions, with the pace and breaks matched to the group's starting confidence.
- Task sorting using a supplied acceptable-use policy
- Summary checking against an original source
- Independent explanation of checks and escalation
03 / 04AI literacy
Establish safe-use habits before real data appears
AI literacy includes the practical question of what information may enter a tool. Employees learn to identify personal details, confidential business information and credentials, then apply their organisation's rules instead of guessing from a product name. The organiser confirms approved accounts and any restrictions before hands-on practice. Fictional materials allow beginners to make mistakes without experimenting on customer records. Learners who cannot obtain tool access can still analyse supplied outputs, but that is distinguished from completing interactive practice. We also cover how to report a concern, stop an unsuitable task and ask for help without treating uncertainty as a personal failure.
- Data-category recognition exercise
- Approved account and policy preparation
- A named route for questions and concerns
04 / 04AI literacy
Assess judgement and support the next attempt
Literacy assessment looks at the decisions around a task, not how impressive the output appears. A learner should identify an unsuitable input, explain a limitation, check a claim and find the responsible reviewer. We compare a baseline scenario with a later independent exercise to see which concepts need another explanation. The follow-up asks whether employees can apply the organisation's rules in ordinary work and whether those rules are clear enough to use. A short learning record can document participation and practical feedback. It is not a professional certification or proof of legal compliance. Further tool or role training is recommended only where a demonstrated need remains.
- Baseline and final scenario assessment
- Individual feedback on judgement and checking
- Follow-up questions for policy and learning owners
What you take forward
Not just a session. Something to work with.
Beginner learning agenda
Safe-use scenario pack
Plain-language terminology sheet
Practical assessment feedback
Task-checking checklist
Follow-up learning actions
- 01
Establish the baseline
Ask learners to classify a task and explain their current checks.
- 02
Teach through examples
Compare sources, instructions and deliberately flawed outputs.
- 03
Practise independently
Apply the policy and checking method to a new scenario.
- 04
Close the gaps
Review unresolved questions with the learning and policy owners.
Before we begin
Your questions.
Straight answers.
Is this a technical AI course?
No. The focus is everyday judgement for employees and managers, not model development or coding. Technical concepts are introduced only when they help someone make a working decision. Engineers needing repository-based practice should use a dedicated technical training route such as Claude Code.
Can literacy training satisfy a legal requirement?
Training records can support an organisation documenting its learning activity, but participation does not establish compliance. Applicable obligations and the evidence needed should be assessed by your responsible legal or compliance team. We scope learning objectives and records without promising a legal outcome.
What if employees are worried about AI?
The session gives people room to ask questions and distinguish known plans from speculation. Exercises focus on actual tasks and review responsibilities. Managers should explain the purpose of the programme honestly, including what has and has not been decided about workplace changes.
What happens after the introductory session?
Learners receive a defined practice task or checking exercise, with a follow-up agreed in the scope. The review separates missing skills from unclear policy and unavailable access. Some teams need tool-specific practice next; others need managers to clarify the rules before doing more training.
Your team. Your next chapter.