Generative AI for Learning Teams Training Course: Design, Delivery and Evaluation

Programme Summary

Generative AI for learning teams is the subject of this one-week training course for heads of learning, training directors and programme managers, ending in a learning team AI playbook for one training portfolio. Learning functions are asked to produce more content, faster, while the quality checks, learner data rules and impact measures lag behind the tools. The programme moves from needs analysis and prompt design to content and assessment production, delivery support and evaluation, applied to a portfolio you select. Mars Training Centre UK delivers this programme on generative AI for learning teams to mixed-organisation cohorts.

The Business Case

Training budgets are judged on performance change, not on the volume of materials produced. When teams adopt generative tools without agreed review steps, inaccurate content reaches learners, assessment quality varies and nobody can show what changed. Leaders who set quality gates, protect learner data and link evaluation to business measures can speed up production while keeping the trust of sponsors and participants.

• Content Development Cycle Time: Falls as drafting, review and approval steps are defined for AI-assisted work.
• Rework from Inaccurate Material: Falls as subject expert checks are placed where errors are most costly.
• Evidence of Training Impact: Improves as evaluation plans are set before programmes are built.

Principles and Evidence Base

• Learning Needs Analysis: Compares performance gaps with possible learning solutions, applied in Session 1 to choose where generative tools are used.
• NIST AI Risk Management Framework: Provides the govern, map, measure and manage functions used in Session 4 to set review and monitoring rules.
• ISO/IEC 42001: Describes a management system for AI, used in Session 4 to assign ownership of tools and data.

Intended Participants

• Heads of learning who set the strategy and budget for training functions.
• Training directors who sponsor new tools and approve content standards.
• Programme managers who commission, schedule and quality-check training materials.
• Senior instructional designers who lead teams producing courses and assessments.

Typical settings include banks, government departments, manufacturers and healthcare providers.

Learning Outcomes

1. Participants will be able to evaluate learning needs to decide where generative tools add value.
2. Participants will be able to construct prompts and review criteria that produce usable learning drafts.
3. Participants will be able to diagnose accuracy, bias and privacy risks in AI-assisted learning material.
4. Participants will be able to design assessments and learner support that use generative tools with human oversight.
5. Participants will be able to prioritise governance controls for tools and learner data.
6. Participants will be able to justify an evaluation plan that links training to business measures.

Programme Structure

Day 1: Generative AI for Learning Teams and Needs Analysis

Where in the learning cycle do generative tools earn a place?

• Learning Cycle Mapping
• Performance Gap Analysis
• Task Suitability Screen
• Learner Profile Canvas
• Use Case Shortlist

Day 2: Prompt Design and Content Drafting

How are prompts and review criteria written to give usable drafts?

• Prompt Structure Template
• Learning Objective Drafting
• Content Outline Generation
• Subject Expert Review Checklist
• Draft Quality Rubric

Day 3: Assessment, Practice and Learner Support

How are assessments and learner support built with human oversight?

• Question Bank Design
• Scenario and Case Generation
• Feedback Wording Standards
• Coaching Assistant Boundaries
• Accessibility Review

Day 4: Risk, Data and Governance

Which controls keep AI-assisted learning accurate, fair and private?

• Hallucination Test Protocol
• Bias Review of Examples
• Learner Data Handling Rules
• NIST AI Risk Management Framework Functions
• ISO/IEC 42001 Ownership Mapping

Day 5: Evaluation and the Team Playbook

Do the needs, prompts, assessments and controls hold together for one portfolio?

• Applying the Session 1 Use Case Shortlist
• Testing the Session 2 Draft Quality Rubric
• Checking the Session 3 Coaching Assistant Boundaries
• Training Impact Evaluation Plan
• Completing the Learning Team AI Playbook

Methodology

The programme uses a structured exercise method built on a shared case of a learning function asked to double its output with a flat budget. Participants apply each tool to the case, then to their own portfolio, with written facilitator feedback and peer comparison in every session.

• Suggested activity: Rewriting a weak prompt and scoring the drafts it produces against a rubric.
• Suggested activity: Planting errors in a draft module and testing whether reviewers find them.
• Suggested activity: Drafting an evaluation plan for one programme before it is built.

Capstone Output

Each participant completes a learning team AI playbook for one training portfolio. It holds a use case shortlist, a prompt and review standard, assessment guidelines, a risk and data control sheet and an evaluation plan. The playbook is used at work to brief sponsors, instruct designers and suppliers, and guide the first pilot.

Frequently Asked Questions

Is generative AI for learning teams training suitable for leaders who have not used these tools?

Yes. Generative AI for learning teams is taught through guided exercises and templates, assuming experience of managing training work but no technical or programming skills.

Which frameworks does generative AI for learning teams training rest on?

Generative AI for learning teams training rests on learning needs analysis, the NIST AI Risk Management Framework and ISO/IEC 42001 for management of AI.

How is the playbook from generative AI for learning teams training used at work?

You use the learning team AI playbook to brief sponsors, instruct designers and suppliers and run a first pilot on one training portfolio.

How does generative AI for learning teams differ from instructional design training?

Generative AI for learning teams centres on tool use, review and governance across the learning cycle, while instructional design training centres on learning theory and course structure.