AI for Learning and Development Training Course

Participants can define practical governance controls for AI-supported learning work.

• NIST AI RMF functions applied to learning-content production
• Risk registers covering privacy, bias, accuracy, copyright, and accessibility
• Data classification rules for learner records and source documents
• Approval gates for generated materials, assessments, and learner communications
• Audit trails documenting prompts, source materials, reviews, and revisions
• Escalation criteria for harmful, misleading, or sensitive AI outputs

Participants can use AI to accelerate evidence-based learning needs analysis without replacing stakeholder judgment.

• Performance consulting questions separating knowledge, skill, motivation, and process gaps
• Stakeholder interview guides generated from stated business and performance problems
• Root-cause analysis using the five whys and fishbone diagram
• Audience personas based on validated role, context, and capability evidence
• Task analysis identifying critical decisions, errors, and performance standards
• AI-assisted synthesis checked against interview notes and operational data

Participants can write structured prompts that produce controllable learning-design outputs.

• Prompt structure using role, task, context, constraints, and output format
• Few-shot examples for consistent tone, terminology, and instructional patterns
• Source-grounded prompting with approved policies, procedures, and reference documents
• Rubric-based prompts for self-checking generated learning materials
• Iterative prompt refinement through critique, revision, and comparison
• Prompt libraries organised by analysis, design, development, and evaluation tasks

Participants can apply learning science to review and improve AI-generated learning designs.

• ADDIE phases mapped to AI-supported learning-development activities
• Bloom's Taxonomy verbs matched to observable workplace performance
• Cognitive load principles for chunking explanations, examples, and practice
• Gagné's nine events used to sequence instructional interactions
• Adult-learning assumptions applied to relevant, problem-centred learning tasks
• Quality criteria distinguishing information delivery from performance-focused instruction

Participants can design a blended learning journey supported by appropriate AI-enabled touchpoints.

• Learning journey maps connecting prework, instruction, practice, and transfer
• Modality decisions for classroom, virtual, self-paced, and performance support
• Spaced retrieval schedules aligned with risk, task frequency, and mastery
• Scenario sequencing from familiar decisions to ambiguous workplace cases
• Personalisation rules based on role, baseline capability, and learning evidence
• AI touchpoints selected for preparation, feedback, reinforcement, and support

Participants can produce and review AI-assisted learning content from approved source material.

• Content briefs specifying audience, performance outcomes, sources, and constraints
• Source hierarchy prioritising policies, procedures, standards, and expert validation
• Plain-language rewriting without changing regulated or technical meaning
• Fact-check protocol comparing generated claims with authoritative source documents
• Version-control conventions for AI drafts, reviewed copy, and approved assets
• Content-review rubrics covering accuracy, relevance, clarity, and completeness

Participants can create realistic AI-assisted scenarios that develop workplace decision skills.

• Critical incident method for capturing consequential workplace decisions
• Branching scenario structures with choices, consequences, and feedback
• Case facts calibrated for realism without exposing confidential information
• Distractor design based on common errors and plausible misconceptions
• Feedback scripts explaining consequences, standards, and better decisions
• Scenario review for bias, stereotyping, ambiguity, and operational accuracy

Participants can create valid assessments and quality-check AI-generated items.

• Assessment blueprints linking objectives, content domains, and item formats
• Selected-response items testing discrimination rather than simple recall
• Constructed-response rubrics with observable criteria and scoring anchors
• Item-writing rules addressing stems, options, clues, and distractors
• AI-generated question review for validity, ambiguity, and factual correctness
• Standard-setting considerations for high-consequence learning assessments

Participants can adapt AI-assisted learning materials for accessibility, inclusion and local relevance.

• WCAG 2.2 principles applied to digital learning content
• Alt text, captions, transcripts, and readable document structures
• Inclusive language checks for assumptions, stereotypes, and exclusionary phrasing
• Localisation review covering terminology, examples, dates, and legal references
• Reading-level analysis matched to learner language and task requirements
• Human review of translations for meaning, tone, and technical precision

Participants can use AI to prepare facilitators and support learners within defined boundaries.

• Facilitator guides generated from validated session objectives and timings
• Question banks supporting checks for understanding during live delivery
• AI-assisted coaching prompts for reflection, practice, and action planning
• Learner-support boundaries for advice, escalation, and sensitive disclosures
• Virtual classroom scripts balancing explanation, interaction, and practice
• Facilitator review procedures before using generated discussion questions

Participants can define responsible personalisation rules for differentiated learning support.

• Learner segmentation using role, proficiency, performance, and preference evidence
• Diagnostic assessments identifying starting points and targeted learning paths
• Recommendation rules separating helpful adaptation from discriminatory profiling
• Mastery criteria for progression, remediation, and additional practice
• Data-minimisation principles for learner profiles and behavioural records
• Human intervention triggers for repeated failure or learner distress

Participants can specify useful learning data and interpret it responsibly for improvement decisions.

• xAPI statement structure using actor, verb, object, and context
• Learning metrics distinguishing activity, completion, proficiency, and performance
• Dashboard questions linked to decisions rather than available data
• Data-quality checks for missing records, duplicates, and inconsistent definitions
• Cohort comparisons interpreted with context, sample limits, and fairness checks
• Analytics action logs documenting decisions, owners, and follow-up measures

Participants can build an evaluation plan that tests learning effectiveness and business contribution.

• Kirkpatrick levels aligned with outcomes, evidence sources, and timing
• Success measures defined before development begins and communicated to sponsors
• Transfer-of-learning plans involving managers, workflow cues, and reinforcement
• Comparison methods using baseline, follow-up, and matched performance data
• Contribution analysis separating training effects from operational changes
• Evaluation reports combining learner evidence, performance data, and recommendations

Participants can evaluate AI tools and vendors against L&D requirements, risk controls and operating fit.

• Vendor due-diligence questions covering security, data retention, and model training
• Use-case scorecards comparing capability, cost, risk, and implementation effort
• Integration requirements for learning platforms, identity systems, and content repositories
• Pilot success criteria covering quality, adoption, time, and error rates
• Contract clauses addressing data ownership, confidentiality, and audit access
• Exit plans for vendor changes, service interruption, and content portability

Participants can plan controlled adoption of AI-supported processes within an L&D function.

• Operating models defining content owners, reviewers, approvers, and administrators
• Standard operating procedures for prompting, review, approval, and publication
• Change-impact assessments for L&D roles, skills, and workload distribution
• Pilot roadmaps sequencing use cases by value, risk, and readiness
• Communications plans explaining permitted uses, controls, and support routes
• Adoption measures tracking usage, confidence, quality, and policy compliance

Participants can establish repeatable quality assurance for AI-generated learning assets and decisions.

• Quality gates from source selection through publication and post-launch review
• Review checklists for factual accuracy, instructional quality, and accessibility
• Red-team tests exposing unsafe, biased, or misleading generated outputs
• Sampling plans balancing review effort with content risk and volume
• Defect logs categorising errors, causes, corrective actions, and recurrence
• Continuous-improvement cycles using learner feedback and quality findings

Participants can communicate AI learning proposals, controls and decisions to business stakeholders.

• Business cases linking learning problems, AI use cases, costs, and benefits
• Executive briefings presenting risk, controls, decisions, and required sponsorship
• AI-use policies defining permitted, prohibited, and approval-required activities
• Manager guidance for reinforcing AI-supported learning after formal training
• Subject-matter expert agreements for source validation and content sign-off
• Incident communications for inaccurate content, data exposure, or learner harm

Participants can integrate analysis, prompts, learning design and governance into a documented solution draft.

• Problem statements connecting business goals, performance gaps, and learner groups
• Design assumptions tested against stakeholder evidence and available source material
• Prompt sequences producing traceable drafts for selected learning assets
• Learning blueprints specifying objectives, modalities, practice, and transfer support
• Governance controls matched to data sensitivity and decision consequences
• Peer review against agreed quality, risk, and implementation criteria

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

• Needs-analysis brief defining performance problem, audience, evidence, and constraints
• Learning architecture combining objectives, modalities, practice, and reinforcement
• Prompt library producing controlled drafts from approved source materials
• Assessment blueprint with item formats, review controls, and success criteria
• AI governance plan covering privacy, accuracy, bias, copyright, and approvals
• Evaluation plan linking xAPI evidence, Kirkpatrick levels, and business measures
• Stakeholder presentation defending design choices, controls, costs, and expected outcomes