Syllabus & learning experience
From the syllabus
to the working fleet.
Explore what you will study, how you will practise and how your progress will be recorded. This is our developing course plan, with an illustrative learner dashboard below.
Reference: CILT road haulage specification v4.3, October 2026. This outline is not a complete assessment-criterion mapping or an approved course.
Four areas of responsibility.
Each area will develop into lessons, knowledge checks and appropriate practical assignments. The exercises below are our proposed learning activities, subject to technical and subject-specialist review.
Managing Business Operations
Business structures and law; commercial decisions; finance and taxation; employment relationships; measuring performance.
Review the fictional business’s costs and job margins. Explain whether a new contract supports its growth.
Lesson mapping in development
Managing Drivers
Driver qualifications and training, working schedules, driving-time limits and the records used to demonstrate compliance.
Prepare a driver schedule for a fictional job, identify conflicts and explain the changes needed.
Lesson mapping in development
Managing Compliance and Risk
Licensing, insurance and accident handling; vehicle upkeep and testing; load limits, secure carriage and road rules.
Review a maintenance calendar, identify a vehicle that should not be allocated and submit a revised plan.
Lesson mapping in development
Managing Transport Operations
Transport contracts and data; selecting and costing vehicles; vehicle taxes, operating permissions, trade terms, borders, documents, CMR and budgets.
Cost an international job, check its document pack and explain the proposed price and route.
Lesson mapping in development
Source: CILT’s official qualification specification. Our summaries and proposed exercises do not replace its full learning outcomes and assessment criteria.
A clear link from learning to evidence.
Some topics need a worked explanation rather than a software exercise. Exam preparation will also include practice in the relevant assessment format.
Documentation we will attach to each learning outcome
- Reference to the syllabus criterion and specification version.
- Lesson notes, video script, worked example and supporting sources.
- Questions, answer explanations and completion requirements.
- Where relevant, the GLS scenario, starting data and marking rubric.
- Content reviewer, review date and change history.
Your next step, at a glance.
An illustrative screen showing the proposed learner experience. All names, scores and progress below are fictional. Preview links explain the intended behaviour; they do not start a course or change any fleet records.
Welcome back, Alex.
Continue where you left off.
Learner · Example account
Illustrative counts only. The final lesson structure is still being mapped.
What does this journey cost?
Separate fixed and running costs, then apply them to a fictional transport job.
Northway Training Logistics
Isolated learner workspace
Stage 1 of the planned journey towards 80 trucks.
Talk through the decision.
SCRIPTED EXAMPLE
Choose a sample prompt to see how the discussion could continue.
Explain a concept
Contribution is revenue less the costs that change because you accept the job. Full costing also allocates costs such as vehicle finance and insurance. A positive contribution can help cover standing costs, but it does not by itself prove that a regular rate is sustainable.
Challenge my reasoning
If this customer offered the same job every week, would you still accept £850? What would happen if an empty return added £180? With only your £600 estimate and that extra £180, the remainder would be £70 before any other omitted costs. Which assumptions should we verify?
Give me a hint
Check the total loaded and empty distance, then the driver’s full duty time. List missing cost items before deciding which are additional costs and which are allocated standing costs. You do not yet have enough information to call the quote profitable.
Design preview only: these responses are written examples, not generated by a live model. No message is submitted and no GLS records are accessed or changed.
Example tutor feedback: check the inspection due date before allocating vehicle NW-014.
Cost the job.
Explain your decision.
Your 20-truck business has been asked to quote for a new journey. Use the supplied fictional distances, running costs and vehicle overheads to prepare your answer.
What you would submit
- A calculation showing your assumptions and working.
- A completed fictional job costing in GLS.
- A short explanation of the proposed selling price.
The knowledge check and practical submission would be recorded separately. The marking rubric and pass requirements will be defined during course development.
Explain it. Challenge it.
Understand the decision.
The proposed AI instructor would discuss the lesson with you, ask why you made a decision and help you test your assumptions. It should recognise a sound counterargument and adjust when you provide new facts.
Find another way in
Ask for a plain-English explanation, a worked example or a breakdown of an unfamiliar term. Responses would refer back to reviewed course materials.
Defend your reasoning
Discuss the trade-offs in your answer. The tutor would challenge unsupported assumptions without treating legal requirements as opinions or arguing for its own sake.
Get a useful hint
Receive progressively stronger hints before a worked answer. The aim is to help you reason through the problem rather than complete the exercise for you.
How the instructor would work with your costing lesson
- You read the reviewed lesson and open your fictional GLS job.
- The server supplies only your authorised exercise data and the relevant lesson references.
- You discuss your calculation with the tutor; assumptions and missing information remain explicit.
- Tested calculation rules check the arithmetic. AI feedback helps explain your reasoning.
- You decide when to submit. Defined assessed tasks, uncertain answers and disputes go to a qualified human reviewer.
The initial integration would be read-only: the AI could not change a quote, submit work, reset a fleet or select another learner’s tenant. Conversation retention and privacy settings will be defined before launch.
AI support is a planned feature. It will need subject-specialist evaluation before release and will not replace the official examinations or qualified review of course content.
One learner. One training tenant.
The proposed design gives each learner a separate GLS tenant within a dedicated training deployment. Everyone starts from the same versioned fictional business, then develops their own records and decisions as the fleet grows from 20 to 80 trucks.
Open the right exercise
Sign in to the learning platform once. A secure, short-lived handoff would open your own GLS workspace at the task you are studying.
Keep the evidence
A fixed copy of submitted work would be retained for marking. Later practice would not change the evidence your tutor reviews.
Learn from another attempt
Restart from a known scenario or return to a checkpoint. Earlier results, feedback and submissions would remain in your learning record.
Room to make mistakes.
These preview links describe the controls. No reset or rollback is performed.
Restart exercise
Restore the current exercise’s starting data and begin a new attempt. Work affecting other exercises must be protected or the dependency clearly explained.
Before confirming: show which unsaved or unsubmitted work will be replaced.
Return to checkpoint
Choose an available saved fleet state. Review when it was created and what later practice work will be discarded.
Before confirming: preserve a recovery point and retain all submitted evidence.
Reset practice fleet
Rebuild your fictional fleet at the current course stage from its scenario template. This affects your training workspace only.
Before confirming: show the scope of the reset and require explicit confirmation.
Planned isolation and recovery safeguards
- Validate tenant permissions for records, files, background jobs and every practice-area request.
- Keep learning results and submitted evidence outside resettable fleet data.
- Use tenant-specific templates and checkpoints; never restore the shared database to reset one learner.
- Pause writes during a reset, validate the restored state and audit the action.
- Provide tutor or administrator recovery where dependencies make a learner reset unsafe.
- Prevent training actions from sending real customer emails, producing real invoices or calling production integrations.
These are design requirements. The training integration and controls still need to be implemented and tested.
Completion is only
part of the picture.
Finishing a video, passing a quiz and demonstrating a practical skill will be separate achievements. Learners should always see what is complete, what needs attention and what to do next.
- Lessons: not started, in progress or completed.
- Knowledge checks: attempts, scores and explanations.
- Practical work: submitted, changes requested or passed.
- Feedback: tutor comments and resubmission history.
- Official exams: separate verified outcomes, not inferred from course progress.
Each attempt will retain its content version and result. A fleet reset will not reset these learning records.
Clear roles and responsibilities.
Learner
Own lessons, results and practice fleet.
Tutor
Assigned learners, feedback and practical marking.
Course author
Draft lessons, questions and scenarios.
Content reviewer
Accuracy checks and publication approval.
Administrator
Enrolments, permissions and recovery operations.
A course taking shape.
The learning platform and practice fleet are in planning. We will publish enrolment and examination arrangements before launch.