Pilot learning assistant
How can you prototype an internal assistant that supports learning without inventing answers or exposing data?
3500 € per session, excl. VAT
In short
Over two days, totalling 14 hours excluding breaks, you build a prototype educational assistant based on a controlled corpus. You define its limits, design its interactions, test its responses and prepare an internal pilot. The aim is a solution that can be evaluated, with human oversight, rather than an industrialised production deployment.
Why this training?
Learners often look for an explanation across several conflicting or ageing learning resources. An educational assistant therefore requires an explicit documentary scope, verifiable sources and editorial accountability. Answering correctly is not enough to support learning. The prototype must guide learners without systematically providing the solution, acknowledge its limits and allow tutors to assess its educational quality. Running case study: The fictitious organisation Avelune Réemploi is preparing to onboard its sorting staff. Using fictitious procedures and modules, its training team wants an assistant that explains instructions, offers exercises and refers sensitive questions to a tutor. Transfer to the workplace: Have the corpus and access permissions validated, then run the pilot with a limited group. Review errors with tutors before any expansion.
Objectives of this training
- Define the assistant’s uses and exclusions
- Structure a traceable educational corpus
- Configure a prototype in an authorised environment
- Test its reliability and educational approach
- Formalise the conditions for launching the pilot
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Training managers and instructional designers
- AI leads and managers of internal learning platforms
Prior skills
- Use generative AI and write structured instructions.
- Design learning activities and understand internal approval processes.
What you will work on
Define the scope of the service
Objective: Choose the situations to be covered. Learning support, exclusions and recourse to a tutor.
Design the interactions
Objective: Define a tutoring approach. Questioning, progressive hints, correction and vocabulary adaptation.
Assess the learning resources
Objective: Select reliable sources. Document authority, versions, contradictions and access rights.
Choose the setup
Objective: Adapt the prototype to the available resources. Supplied context or document retrieval, depending on verified capabilities.
Configure the instructions
Objective: Set boundaries for educational responses. Sources, abstention, confidentiality and separation of documents/instructions.
Assemble the learning journey
Objective: Connect assistance and learning. Welcome, guided exercise, explanatory feedback and escalation.
Build the test setup
Objective: Make the evaluation reproducible. Representative questions, expected answers and an educational assessment rubric.
Measure reliability
Objective: Assess responses and citations. Accuracy, coverage, abstention and relevance of hints.
Test the limits
Objective: Verify behaviour in sensitive situations. Document-based injection, unauthorised access and requests for individual assessment.
Correct and retest
Objective: Verify the improvements. Review of the corpus, instructions and regression checks.
Organise the pilot
Objective: Prepare a supervised trial. Limited group, human assistance, minimal retention of logs.
Support the decision
Objective: Make the case for launch or postponement. Remaining limitations, documentation maintenance and required approvals.
What you take away
- Usage charter and corpus register
- Documented prototype and version-controlled instructions
- Test suite and evaluation report
- Pilot plan with responsibilities and stopping conditions
Method and assessment
- Short instructional inputs followed by immediate practice using the case study that runs throughout.
- Individual work followed by work in pairs, with peer review of the outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare fictitious or anonymised materials, with versions and learning objectives. Check access to the tools authorised by the organisation and their document configuration options.
Observable criteria: Each factual answer tested refers to a verifiable source or indicates that none is available. ; The learning scenarios use progressive hints before providing the correction. ; The tests cover contradictions, out-of-scope requests and malicious instructions. ; Errors are classified, corrected and then retested. ; The pilot specifies responsible parties, indicators, acceptance thresholds and a stopping procedure..
Frequently asked questions
Is programming required?
Not necessarily; the setup depends on the verified capabilities of the authorised environment.
Can learner records be used?
The exercises use only fictitious or anonymised cases, without identifiable individual records.
Does the assistant replace the tutor?
No: sensitive or undocumented situations are referred to a designated human contact.
Will the service be operational at scale?
No: the two days produce an evaluated prototype and a pilot plan, subject to internal approvals.
Last updated: