14. AI for HR Professionals

Course Positioning

This course teaches AI for HR productivity and people operations while foregrounding fairness, privacy, legal risk, and human judgment. It covers job descriptions, sourcing support, interview design, onboarding, learning programs, policy drafting, employee communication, and analytics.

Learning outcomes

Use AI to draft and improve job descriptions, interview questions, onboarding plans, policies, and employee communications.
Identify bias and fairness risks in AI-assisted hiring and performance workflows.
Build structured HR workflows with human review and documentation.
Use AI for L&D content design and internal knowledge support.
Create an HR AI governance checklist for the organization.

Expanded Topic-by-Topic Coverage

Module 1. AI across the employee lifecycle

Module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits. Primary live activity or lab: Map HR tasks by value and risk. Expected take-home output: HR AI opportunity map.

Topics and coverage

Workforce planning

What it means: define Workforce planning clearly and connect it to the module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

hiring

What it means: define hiring clearly and connect it to the module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

onboarding

What it means: define onboarding clearly and connect it to the module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

learning

What it means: define learning clearly and connect it to the module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

engagement

What it means: define engagement clearly and connect it to the module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

performance

What it means: define performance clearly and connect it to the module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

exits

What it means: define exits clearly and connect it to the module focus: Workforce planning, hiring, onboarding, learning, engagement, performance, exits.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Map HR tasks by value and risk.
Learners produce: HR AI opportunity map.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 2. Job descriptions and role design

Module focus: Skills, outcomes, inclusive language, clarity, leveling, compensation caveats. Primary live activity or lab: Rewrite a weak JD using AI and bias checklist. Expected take-home output: Improved JD.

Topics and coverage

Skills

What it means: define Skills clearly and connect it to the module focus: Skills, outcomes, inclusive language, clarity, leveling, compensation caveats.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

outcomes

What it means: define outcomes clearly and connect it to the module focus: Skills, outcomes, inclusive language, clarity, leveling, compensation caveats.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

inclusive language

What it means: define inclusive language clearly and connect it to the module focus: Skills, outcomes, inclusive language, clarity, leveling, compensation caveats.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

clarity

What it means: define clarity clearly and connect it to the module focus: Skills, outcomes, inclusive language, clarity, leveling, compensation caveats.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

leveling

What it means: define leveling clearly and connect it to the module focus: Skills, outcomes, inclusive language, clarity, leveling, compensation caveats.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

compensation caveats

What it means: define compensation caveats clearly and connect it to the module focus: Skills, outcomes, inclusive language, clarity, leveling, compensation caveats.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Rewrite a weak JD using AI and bias checklist.
Learners produce: Improved JD.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 3. Recruitment and sourcing support

Module focus: Search strings, candidate communication, screening rubrics, structured evaluation. Primary live activity or lab: Create a candidate evaluation rubric without personal bias. Expected take-home output: Screening rubric.

Topics and coverage

Search strings

What it means: define Search strings clearly and connect it to the module focus: Search strings, candidate communication, screening rubrics, structured evaluation.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

candidate communication

What it means: show where candidate communication appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

screening rubrics

What it means: define screening rubrics clearly and connect it to the module focus: Search strings, candidate communication, screening rubrics, structured evaluation.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

structured evaluation

What it means: connect structured evaluation to the data lifecycle from source and structure through analysis, interpretation, and decision-making.
What to cover: source reliability, missing or biased data, leakage, assumptions, calculations, and the difference between correlation and decision-ready evidence.
Demonstration: walk through a small dataset or example table and mark the checks required before trusting the result.
Evidence of learning: learners produce a short analysis note that includes assumptions, limitations, and verification steps.

Practice and evidence of learning

Learners complete or discuss: Create a candidate evaluation rubric without personal bias.
Learners produce: Screening rubric.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 4. Interview design

Module focus: Competency questions, work samples, scoring guides, interviewer alignment. Primary live activity or lab: Build an interview kit for one role. Expected take-home output: Interview guide.

Topics and coverage

Competency questions

What it means: define Competency questions clearly and connect it to the module focus: Competency questions, work samples, scoring guides, interviewer alignment.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

work samples

What it means: define work samples clearly and connect it to the module focus: Competency questions, work samples, scoring guides, interviewer alignment.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

scoring guides

What it means: define scoring guides clearly and connect it to the module focus: Competency questions, work samples, scoring guides, interviewer alignment.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

interviewer alignment

What it means: define interviewer alignment clearly and connect it to the module focus: Competency questions, work samples, scoring guides, interviewer alignment.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Build an interview kit for one role.
Learners produce: Interview guide.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 5. Onboarding and employee support

Module focus: Onboarding plans, FAQs, buddy systems, policy explanations, helpdesk knowledge base. Primary live activity or lab: Create a 30-day onboarding plan. Expected take-home output: Onboarding workflow.

Topics and coverage

Onboarding plans

What it means: define Onboarding plans clearly and connect it to the module focus: Onboarding plans, FAQs, buddy systems, policy explanations, helpdesk knowledge base.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

FAQs

What it means: define FAQs clearly and connect it to the module focus: Onboarding plans, FAQs, buddy systems, policy explanations, helpdesk knowledge base.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

buddy systems

What it means: define buddy systems clearly and connect it to the module focus: Onboarding plans, FAQs, buddy systems, policy explanations, helpdesk knowledge base.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

policy explanations

What it means in this course: define policy explanations in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what HR managers, recruiters, talent acquisition teams, L&D teams, people operations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

helpdesk knowledge base

What it means: define helpdesk knowledge base clearly and connect it to the module focus: Onboarding plans, FAQs, buddy systems, policy explanations, helpdesk knowledge base.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Create a 30-day onboarding plan.
Learners produce: Onboarding workflow.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 6. Learning and development

Module focus: Skill gap analysis, microlearning, manager training, quizzes, role-play simulations. Primary live activity or lab: Design a short internal training module. Expected take-home output: L&D module outline.

Topics and coverage

Skill gap analysis

What it means: define Skill gap analysis clearly and connect it to the module focus: Skill gap analysis, microlearning, manager training, quizzes, role-play simulations.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

microlearning

What it means: define microlearning clearly and connect it to the module focus: Skill gap analysis, microlearning, manager training, quizzes, role-play simulations.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

manager training

What it means: place manager training inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

quizzes

What it means: define quizzes clearly and connect it to the module focus: Skill gap analysis, microlearning, manager training, quizzes, role-play simulations.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

role-play simulations

What it means: define role-play simulations clearly and connect it to the module focus: Skill gap analysis, microlearning, manager training, quizzes, role-play simulations.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Design a short internal training module.
Learners produce: L&D module outline.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 7. People analytics and communication

Module focus: Survey summaries, attrition themes, engagement insights, privacy-safe reporting. Primary live activity or lab: Summarize fictional survey results and recommend actions. Expected take-home output: People insights memo.

Topics and coverage

Survey summaries

What it means: define Survey summaries clearly and connect it to the module focus: Survey summaries, attrition themes, engagement insights, privacy-safe reporting.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

attrition themes

What it means: define attrition themes clearly and connect it to the module focus: Survey summaries, attrition themes, engagement insights, privacy-safe reporting.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

engagement insights

What it means: define engagement insights clearly and connect it to the module focus: Survey summaries, attrition themes, engagement insights, privacy-safe reporting.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

privacy-safe reporting

What it means in this course: define privacy-safe reporting in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what HR managers, recruiters, talent acquisition teams, L&D teams, people operations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

Practice and evidence of learning

Learners complete or discuss: Summarize fictional survey results and recommend actions.
Learners produce: People insights memo.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 8. Fairness, privacy, and governance

Module focus: Protected characteristics, adverse impact, transparency, consent, data retention, human decision rights. Primary live activity or lab: Audit an HR AI workflow for risk. Expected take-home output: HR AI governance checklist.

Topics and coverage

Protected characteristics

What it means: define Protected characteristics clearly and connect it to the module focus: Protected characteristics, adverse impact, transparency, consent, data retention, human decision rights.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

adverse impact

What it means: define adverse impact clearly and connect it to the module focus: Protected characteristics, adverse impact, transparency, consent, data retention, human decision rights.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

transparency

What it means: define transparency clearly and connect it to the module focus: Protected characteristics, adverse impact, transparency, consent, data retention, human decision rights.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

What it means: define consent clearly and connect it to the module focus: Protected characteristics, adverse impact, transparency, consent, data retention, human decision rights.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

data retention

What it means: connect data retention to the data lifecycle from source and structure through analysis, interpretation, and decision-making.
What to cover: source reliability, missing or biased data, leakage, assumptions, calculations, and the difference between correlation and decision-ready evidence.
Demonstration: walk through a small dataset or example table and mark the checks required before trusting the result.
Evidence of learning: learners produce a short analysis note that includes assumptions, limitations, and verification steps.

human decision rights

What it means in this course: define human decision rights in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what HR managers, recruiters, talent acquisition teams, L&D teams, people operations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

Practice and evidence of learning

Learners complete or discuss: Audit an HR AI workflow for risk.
Learners produce: HR AI governance checklist.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Labs, projects, and assessments

Lab 1: Draft an inclusive job description and structured interview kit.
Lab 2: Build an onboarding assistant outline using company-safe content.
Lab 3: Analyze fictional employee survey data and write an action memo.
Capstone: HR AI playbook covering approved use cases, prompt templates, fairness checks, review process, and prohibited uses.

Evaluation approach

20% JD and interview kit.
20% onboarding/L&D workflow.
20% people analytics memo.
40% HR AI governance playbook.

Recommended tools and materials

AI assistant approved for HR use, ATS/HRIS sandbox or templates, spreadsheet, policy documents, L&D tool.
Use synthetic candidate and employee data during training.

Safety, ethics, and governance emphasis

Do not use AI to make final hiring, firing, compensation, or performance decisions without human accountability and legal review.
Avoid entering sensitive employee or candidate data into public tools.
Check outputs for discriminatory, exclusionary, or culturally biased language.

Delivery notes

This course is especially valuable for HR teams before company-wide AI rollouts.
Include legal/compliance review for jurisdiction-specific hiring and employment rules.

Instructor Build Checklist

Prepare one short demo for each module and one learner activity that creates a saved artifact.
Prepare examples that match the audience, local context, and likely tools learners can access.
Add a verification step to every AI-generated output: factual check, source check, data sensitivity check, and quality review.
Keep a running portfolio folder so each module contributes to the final project or learner playbook.
Reserve time for reflection on what the learner did, what AI did, what was checked, and what remains uncertain.