Section 1: AI Recruitment Landscape and Value
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Where AI fits in modern talent acquisition workflows
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Key AI capabilities: NLP, prediction, ranking, and search
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High-impact use cases across sourcing to offer stages
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Business outcomes: speed, quality, cost, and candidate experience
Section 2: Data Foundations for Recruiting AI
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Recruiting data types: resumes, profiles, and interviews
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Job data: role requirements, skills, and success signals
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Data quality risks: bias, missing data, and label leakage
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Privacy and retention basics for candidate data in HR tech
Section 3: AI for Screening and Matching
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Resume parsing and skills extraction at scale
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Semantic search vs. keyword matching in talent pools
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Ranking and recommendation logic for shortlisting
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Handling nontraditional signals and career changes
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Human-in-the-loop review models and control points
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Measuring model performance beyond “accuracy”
Section 4: AI for Assessment and Interviewing
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Assessment types: skills tests, simulations, and structured data
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Interview intelligence: transcription, summaries, and insights
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Predictive validity vs. proxy signals in candidate evaluation
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Candidate experience: transparency and trust considerations
Section 5: Responsible AI and Compliance in Hiring
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Fairness concepts: disparate impact and job relevance
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Bias sources across data, models, and process design
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Governance: documentation, approvals, and audit readiness
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Vendor evaluation: model explainability and reporting
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Regulatory landscape: EEOC, GDPR, and emerging laws
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Monitoring: drift, adverse impact, and ongoing controls
Section 6: Final Review
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Review of key concepts
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Future learning directions