Artificial Intelligence in Learning and Development Training Course

Participants can frame an AI-enabled learning request as a measurable performance problem.

• Performance consulting intake questions for unclear training requests
• Gilbert Behavior Engineering Model applied to capability and environmental factors
• Stakeholder interviews identifying critical tasks, decisions and error consequences
• Current-state workflow mapping from trigger through completed work output
• Root-cause analysis using five whys and fishbone categories
• Defining performance measures, leading indicators and operational constraints
• Writing problem statements that separate symptoms from verified causes

Participants can define governance controls for AI used in learning and development.

• NIST AI RMF functions: govern, map, measure and manage
• Risk registers covering privacy, bias, accuracy, security and intellectual property
• Data classification rules for learner records and proprietary materials
• Human-in-the-loop review points for consequential learning decisions
• Documenting model purpose, limitations, owners and approval status
• Escalation routes for harmful output, suspected leakage and vendor incidents
• Assessment integrity controls for AI-assisted learner submissions

Participants can connect AI-supported learning interventions to job competence and skills evidence.

• Competency models separating knowledge, skills, behaviours and proficiency levels
• Task analysis identifying critical decisions, cues and performance standards
• Skills taxonomy design using role families and observable descriptors
• Mapping learning objectives to job tasks and competence evidence
• xAPI statements for learning experiences, practice and workplace application
• Data quality checks for incomplete, duplicate and outdated skills records
• Ethical limits on automated skills inference and employee profiling

Participants can use AI to accelerate analysis while validating evidence with stakeholders.

• Structured prompt briefs specifying context, source material, constraints and output
• Interview transcript coding for recurring tasks, pain points and capability gaps
• AI-assisted document analysis with source citations and confidence labels
• Triangulating survey, interview, operational and observation evidence
• Persona development based on roles, workflows and access constraints
• Prioritisation matrices comparing business impact, urgency and learning readiness
• Validation workshops resolving disputed findings with subject-matter experts

Participants can write, test and refine prompts for controlled learning outputs.

• Prompt structure: role, task, context, constraints, examples and format
• Few-shot prompting using approved examples and counterexamples
• Chain-of-thought alternatives using concise rationale and evidence requests
• Output schemas for lesson plans, question banks and scenario scripts
• Prompt libraries organised by workflow, audience and approval status
• Adversarial prompts testing unsafe, fabricated and biased responses
• Version control recording prompt changes, inputs and output decisions

Participants can apply AI within an evidence-based instructional design process.

• ADDIE phases adapted for AI-assisted design and production
• Backward design from performance evidence to assessment and instruction
• Bloom's taxonomy verbs matched to observable learning outcomes
• Merrill's First Principles sequencing demonstration, application and integration
• Cognitive load controls for AI-generated explanations and visual materials
• Storyboards specifying interactions, media, feedback and review requirements
• Design documents recording source authority and AI contribution boundaries

Participants can produce and quality-check AI-assisted learning content for approved use.

• Source-grounded generation using approved policies, manuals and technical references
• Fact-checking protocol comparing generated claims with authoritative source material
• Plain-language editing for clarity, terminology consistency and reader action
• WCAG 2.2 considerations for headings, alternatives, contrast and media
• Inclusive language checks for stereotypes, assumptions and exclusionary examples
• Copyright review for generated text, images, code and external sources
• Content approval workflow using subject expert, legal and learning sign-off

Participants can design blended learning journeys that use AI at appropriate moments.

• Learning journey maps linking prework, instruction, practice and transfer
• Selecting classroom, virtual, digital and workplace learning modalities
• AI coaching boundaries for low-risk guidance and human escalation
• Adaptive practice paths based on demonstrated proficiency and confidence
• Performance support design using checklists, job aids and searchable knowledge
• Spaced retrieval schedules supporting retention after formal learning
• Social learning prompts supporting peer review and community knowledge exchange

Participants can create role-based AI simulations that assess workplace decision making.

• Critical incident method identifying realistic high-consequence workplace situations
• Scenario architecture defining role, context, stakes, choices and consequences
• Branching logic connecting learner decisions to credible feedback paths
• AI role-play prompts simulating customers, managers and operational stakeholders
• Rubrics scoring evidence, judgement, communication and procedural compliance
• Guardrails preventing inappropriate advice in sensitive simulated conversations
• Debrief questions converting simulation performance into workplace commitments

Participants can design valid assessments where AI assistance is appropriately controlled.

• Assessment blueprint aligning tasks, criteria, evidence and proficiency levels
• Validity threats caused by unverified AI-generated assessment items
• Item-writing rules for plausible distractors and unambiguous stems
• Authentic assessment using workplace artefacts, demonstrations and oral defence
• AI-use declarations specifying permitted assistance and attribution requirements
• Rubric calibration methods improving assessor consistency and feedback quality
• Credential evidence requirements for skills badges and completion records

Participants can measure AI-enabled learning outcomes without confusing activity with impact.

• Kirkpatrick levels applied to reaction, learning, behaviour and results
• Logic models linking learning activities to performance and business outcomes
• Evaluation questions distinguishing adoption, proficiency, transfer and impact
• Baseline measures established before AI-enabled learning interventions begin
• Dashboard metrics for completion, practice quality, confidence and performance
• Qualitative evidence from manager observations, interviews and work samples
• Interpreting correlations without making unsupported causal impact claims

Participants can assess AI learning technology options against operational and governance requirements.

• Requirements catalogue covering users, workflows, integrations, controls and reporting
• Vendor due diligence questions on training data, retention and model updates
• SCORM, xAPI and LTI interoperability requirements for learning ecosystems
• Proof-of-concept criteria measuring output quality, usability and administrative effort
• Total cost model including licences, implementation, review and support
• Service-level requirements for availability, incident response and accessibility
• Procurement scorecards weighting capability, risk, evidence and commercial terms

Participants can plan AI-assisted knowledge support with authoritative sources and ownership.

• Knowledge audit identifying high-value, volatile and hard-to-find information
• Content lifecycle rules for authorship, review dates, archival and retirement
• Retrieval-augmented generation concepts for source-based organisational answers
• Taxonomy and metadata fields improving retrieval precision and content governance
• Answer quality criteria covering citation, relevance, currency and actionability
• Knowledge assistant escalation paths for uncertainty and high-risk queries
• Usage analytics identifying unanswered questions and knowledge gaps

Participants can use skills evidence to plan targeted AI-supported reskilling interventions.

• Future-role analysis identifying changing tasks, tools and decision requirements
• Skills gap analysis comparing current evidence with target proficiency
• Build-buy-borrow framework for workforce capability response options
• Role-based learning pathways combining formal learning, practice and mentoring
• Internal mobility signals based on validated skills and career interests
• Manager enablement for coaching, assignment and performance feedback
• Change communications addressing employee concerns about AI and job security

Participants can define how an L&D function will govern, produce and improve AI-enabled services.

• Operating model components: processes, roles, governance, technology and measures
• RACI matrix for content creation, review, release and incident management
• Standard operating procedures for approved AI learning workflows
• Quality assurance gates before pilot, release and periodic content review
• Capability development plan for designers, facilitators and subject experts
• Communities of practice sharing prompts, patterns and lessons learned
• Continuous improvement cycles using user feedback, metrics and risk signals

Participants can prepare a staged implementation plan for an AI-enabled learning initiative.

• Pilot selection criteria balancing value, risk, sponsorship and measurable outcomes
• Implementation roadmap sequencing discovery, configuration, testing and scale-up
• Stakeholder mapping by influence, impact, concerns and required involvement
• Change impact assessment for learners, managers, designers and administrators
• Communications plan explaining purpose, safeguards, expectations and support channels
• Training plan for AI users, reviewers, administrators and service owners
• Benefits realisation tracking against baseline measures and agreed targets

Participants can conduct consistent quality reviews of AI-generated learning materials and interactions.

• Review checklist covering accuracy, relevance, safety, accessibility and tone
• Source traceability checks linking claims to approved reference materials
• Bias testing using varied personas, contexts and protected-characteristic scenarios
• Red-team review challenging misleading, unsafe and noncompliant outputs
• Readability measures matching content complexity to intended learner proficiency
• Facilitator review of AI-generated activities, timings and debrief guidance
• Defect logs recording severity, correction owner and release decision

Participants can adapt AI learning methods to high-risk compliance and operational contexts.

• Banking conduct case: advisor coaching with approved policy sources
• Public-service case: citizen-facing decisions, fairness and accountability controls
• Healthcare case: clinical education boundaries and patient-safety escalation
• Manufacturing case: maintenance troubleshooting and procedural adherence support
• Data privacy impact assessment for learner and performance information
• Incident response tabletop for erroneous AI-generated compliance guidance
• Control selection based on consequence, user autonomy and source authority

Participants can build, review and present a complete AI-enabled learning solution for a defined workplace performance need.

• Performance brief defining audience, task, evidence and business outcome
• AI use-case decision supported by value, risk and feasibility analysis
• Learning journey design with objectives, practice, support and assessment
• Prompt pack and sample assets documented with source and review controls
• Governance plan covering data, approvals, quality assurance and incidents
• Evaluation plan using baseline, Kirkpatrick measures and operational indicators
• Implementation roadmap presented for stakeholder challenge and peer review