Governing an AI-augmented academy
How can learning pathways, AI-assisted production and governance be coordinated to test an academy before its rollout?
3500 € per session, excl. VAT
In short
Over two days, totalling 14 hours excluding breaks, you will design the prototype of an academy that connects job-related needs, learning pathways and AI-assisted learning content production. You will establish its governance, test a module and prepare a measurable pilot, without confusing faster production, learning quality and industrialisation.
Why this training?
An academy is more than a catalogue: it connects expected competencies, learning experiences and decisions on authorisation to perform tasks. Without explicit responsibilities, content becomes outdated and local variants become contradictory. Generative AI can support scenario design and writing, but it also amplifies errors in documentation. Integrating it requires controlled sources, expert validation and separate measures of production and learning. Running case study: The fictitious organisation Ardalune Équipements wants to harmonise the onboarding of its technicians across three sites. Its materials differ and its experts have limited availability. The planned academy must offer a common core, validated local variants and a practical assessment before technicians work independently. Transfer to the workplace: Present the dossier to the sponsor, obtain approval for resources and data rules, then launch a limited pilot. A review will decide whether to continue, make corrections or suspend the pilot, based on evidence of learning and the observed workload.
Objectives of this training
- Prioritise competencies and target groups for the pilot
- Structure a shared and adaptable learning pathway architecture
- Prototype an AI-assisted module using validated sources
- Define responsibilities for validation and maintenance
- Test the prototype and formalise a launch decision
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Training managers and heads of internal academies
- Managers responsible for instructional design and skills transformation
- Business function leads mandated to trial an academy
Prior skills
- Be proficient in designing and evaluating a learning pathway
- Use generative AI regularly and verify its responses
- Understand resource trade-offs within a training programme
What you will work on
Mandate and scope
Objective: Define the scope of the academy’s promise. Sponsor, target groups, expected autonomy and exclusions.
Competencies and evidence
Objective: Translate job-related needs into learning outcomes. Critical situations, observable competencies and initial diagnostic assessment.
Learning pathway architecture
Objective: Organise the common core and variants. Prerequisites, delivery formats, tutoring and pathway allocation rules.
Editorial governance
Objective: Assign responsibility for decisions and checks. Subject-matter, educational and data roles; version control and withdrawal.
AI-enhanced production pipeline
Objective: Set boundaries for AI contributions. Approved sources, instructions, traceability and human review.
Learning prototype
Objective: Create a tangible, testable learning unit. Workplace situation, exercise, feedback and accessibility.
Critical challenge testing
Objective: Identify flaws before the trial. Accuracy, ambiguities, workload and educational alignment.
Measuring the academy
Objective: Distinguish activity from competency acquisition. Participation, practical assessment success, transfer to the workplace and maintenance.
Pilot plan
Objective: Set the scale of a realistic trial. Cohort, expert workload, support and risks.
Simulated decision-making committee
Objective: Make the case for a conditional launch. Available evidence, limitations and trade-offs.
What you take away
- Academy charter and responsibility matrix
- Learning pathway architecture and catalogue prioritisation
- Learning prototype with source references and test report
- Pilot dossier: resources, milestones, risks and dashboard
Method and assessment
- Short input sessions 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 review: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare an anonymised description of the target groups, skills and constraints; a fictional dossier remains available. Check access to the tools authorised by the organisation and the rules applicable to learning materials.
Observable criteria: Each learning pathway links a skill, an activity and evidence of acquisition; Each piece of generated content has a source and an identified validator; The test documents the prototype’s errors and corrections; Each indicator specifies its calculation, source, frequency and owner; The pilot explicitly sets out its scope, resources and conditions for continuation.
Frequently asked questions
Will the academy be rolled out after two days?
No: the outcome is a tested prototype and a pilot dossier.
Is an existing platform required?
No; the prototype can remain document-based, without technical integration.
Which documents can be used?
Fictional or anonymised cases, exclusively within authorised tools.
Does AI validate skills?
No; decisions remain human, based on observable evidence.
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