Generative AI for Human Resources Training Course

Participants can prioritise HR generative AI use cases using a structured value and risk assessment.

• Use-case inventory covering recruitment, learning, operations and employee relations
• Value-versus-risk matrix for prioritising HR generative AI opportunities
• Process mapping identifies repetitive drafting, search and summarisation tasks
• Baseline measures define time, quality, service and risk outcomes
• High-impact employment decisions separated from administrative support activities
• Business case assumptions tested against process ownership and data availability
• Pilot scope defined through users, workflows, controls and success measures

Participants can write and refine prompts that produce useful, controlled HR outputs.

• Prompt anatomy: role, task, context, constraints, format and evaluation
• Few-shot prompting uses approved HR examples to guide outputs
• Structured output requests create tables, checklists, rubrics and draft messages
• Context windows managed through concise source material and clear boundaries
• Iterative prompting improves job adverts, interview guides and policy summaries
• Negative constraints prevent unsupported claims, discriminatory language and speculation
• Prompt libraries organised by HR process, audience and approval status

Participants can apply generative AI to recruitment tasks while protecting fairness and candidate experience.

• Job description prompts separate essential criteria from unnecessary credential inflation
• Inclusive language review identifies gendered, age-coded and exclusionary phrasing
• Interview question banks align with competency frameworks and role requirements
• Candidate communications drafted with clear timelines, tone and escalation routes
• CV screening risks examined against job relevance and adverse impact
• Structured interview scoring protected from AI-generated unsupported inferences
• Recruitment workflow controls document reviewer accountability and decision evidence

Participants can use generative AI for learning and workforce tasks without substituting it for people judgement.

• Learning objectives converted into scenarios, quizzes and facilitator guides
• Skills taxonomies used to structure development pathway recommendations
• Performance conversation prompts preserve manager accountability and employee context
• Engagement comment summaries checked for minority views and sentiment distortion
• Workforce planning narratives distinguish facts, assumptions and generated hypotheses
• Career guidance outputs constrained by approved role architecture data
• Learning content review checks accessibility, accuracy and intellectual property

Participants can determine what HR data may be used with AI tools and apply appropriate safeguards.

• HR data classification separates public, internal, confidential and special-category information
• Data minimisation removes unnecessary identifiers before prompt submission
• GDPR principles applied to purpose limitation, retention and transparency
• Employee consent distinguished from other lawful bases for processing
• Cross-border transfers assessed when AI providers process HR information
• Redaction techniques protect names, identifiers and sensitive case details
• Vendor terms reviewed for training data use and retention commitments

Participants can identify and mitigate bias risks in AI-assisted HR content and decisions.

• Bias sources traced through data, prompts, models, users and workflows
• Protected characteristics considered when reviewing recruitment and workforce outputs
• Adverse impact analysis introduced for selection processes and employment outcomes
• Counterfactual prompts test whether recommendations change across equivalent profiles
• Disparate treatment risks distinguished from disparate impact concerns
• Explainability records document inputs, reviewer decisions and justification evidence
• Escalation thresholds define when legal, HR or ethics review intervenes

Participants can establish review processes that make AI-generated HR outputs fit for use.

• Quality criteria assess accuracy, completeness, relevance, tone and traceability
• Source verification confirms policy references, legal claims and numerical statements
• Red-team review probes unsafe, discriminatory and misleading output patterns
• Human-in-the-loop controls assign review authority before external or employee use
• Four-eyes review applied to sensitive employee relations and recruitment communications
• Version control preserves approved prompts, outputs and reviewer changes
• Incident logs capture failures, corrections, impacts and preventive actions

Participants can design practical governance and vendor controls for HR generative AI.

• NIST AI Risk Management Framework applied to HR use cases
• ISO/IEC 42001 governance concepts translated into HR accountability structures
• RACI matrices assign ownership across HR, IT, legal, security and risk
• AI inventory records approved tools, use cases, owners and data categories
• Approval gates distinguish experimentation, pilots and production deployment
• Vendor due diligence examines security, privacy, model transparency and support
• Monitoring indicators track adoption, quality failures, complaints and control breaches

Participants can create a phased implementation plan for a controlled HR AI initiative.

• Ninety-day roadmap sequences discovery, pilot, review and scale decisions
• Stakeholder mapping identifies sponsors, users, reviewers, challengers and approvers
• Change impact assessment addresses roles, skills, communications and resistance
• Operating procedures define prompt approval, output review and incident escalation
• Success metrics combine productivity, quality, adoption, fairness and risk measures
• Training plans specify user guidance, manager briefings and reviewer calibration
• Application plan commits immediate actions, owners, deadlines and evidence requirements