Introduction
The programme “Artificial Intelligence in Human Resource Management” develops professional practice in Human Resources. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from artificial intelligence applications and controls to training design and measurement of learning transfer, with practical work on human resource planning and job analysis, recruitment, selection, and candidate experience, performance, competency, and individual development management. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Design an AI use case with verification criteria and human review in an application related to “Artificial Intelligence in Human Resource Management”.
- Connect workforce and job requirements to objectives and required capabilities.
- Design a selection process based on job requirements and consistent assessment evidence.
- Build a performance agreement, feedback process, and development plan tied to specific gaps.
- Design a training intervention that begins with a performance gap and ends with evidence of application.
Learning areas
1. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur. The application examines a case related to “Artificial Intelligence in Human Resource Management”.
2. Human Resource Planning and Job Analysis
- Analyze workforce demand according to workload, services, and anticipated changes, and document estimation assumptions.
- Inventory current skills, jobs, and vacancy risks, and compare the current position with target requirements.
- Prepare a job description covering purpose, responsibilities, relationships, competencies, and performance indicators without confusing the job with its incumbent.
- Select options for closing gaps through recruitment, development, redeployment, and work design, estimating resources and impact.
- Build indicators to monitor vacancies, workforce movements, retention, and skill coverage, and review the plan when organizational needs change.
3. Recruitment, Selection, and Candidate Experience
- Translate the job description into observable selection criteria, separating essential requirements from attributes that can be developed.
- Prepare a clear job advertisement and recruitment plan targeting appropriate sources, avoiding requirements unrelated to the work.
- Design a structured interview, behavioral and situational questions, and a standard template for recording evidence and evaluating answers.
- Select appropriate assessment tools and work samples, and review fairness, privacy, and permissions for accessing candidate data.
- Document the selection decision, its rationale, communication steps, and onboarding, and measure quality of hire and candidate experience after implementation.
4. Performance, Competency, and Individual Development Management
- Translate unit objectives into agreed individual results, specifying weights, success criteria, and sources of performance evidence.
- Define competencies using observable levels and behaviors, and connect assessment to role requirements without broad personal judgments.
- Conduct a follow-up discussion covering achievements, obstacles, and required support, and record facts and commitments professionally.
- Analyze performance gaps to distinguish causes related to skills, procedures, resources, and motivation, and select an intervention suited to the cause.
- Prepare a development plan with practical tasks, support, and review dates, and monitor the transfer of learning into actual work results.
5. Training Design and Measurement of Learning Transfer
- Analyze performance gaps at organizational, job, and individual levels, and verify that training addresses a cause related to knowledge or skill.
- Write observable learning objectives, define entry requirements, and link each objective to a training activity and assessment evidence.
- Build a sequence of content, activities, and practical cases, balancing explanation, experimentation, and feedback within the available time.
- Select suitable learning methods, face-to-face delivery, and digital learning, and ensure accessibility, material quality, and delivery instructions.
- Design pre- and post-learning assessments and a follow-up plan with the line manager to measure application and identify additional support needs.
Practical outputs
- A use case brief and a test template for the quality of AI outputs.
- A workforce plan, job description, and capability gap map.
- A selection matrix, interview guide, and candidate scorecard.
- A performance agreement card, individual development plan, and review session guide.
- A needs analysis, concise learning package, and application evaluation plan.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in human resources, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Artificial Intelligence in Human Resource Management” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Artificial Intelligence in Human Resource Management” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.