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AI for Learning, Competencies & Human Performance Training Course

Artificial Intelligence is becoming a strategic enabler for developing workforce capabilities, improving organizational learning, managing competencies, and enhancing human performance. AI technologies enable…

HRDA · HR Analytics, Digital HR, Technology & AIAll LevelsClassroomEnglish , Arabic
Duration
5 Days
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Sectors:Human Resources & Talent ManagementCategories:HR Analytics, Digital HR, Technology & AI

Course Overview

Artificial Intelligence is becoming a strategic enabler for developing workforce capabilities, improving organizational learning, managing competencies, and enhancing human performance. AI technologies enable organizations to analyze skills and competency data, identify capability gaps, personalize learning pathways, and connect employee development with business requirements and strategic objectives. This course provides a practical and management-focused framework for applying Artificial Intelligence to Learning and Development, competency management, and human performance. It focuses on how employee data, competency assessments, learning records, and performance information can be transformed into actionable insights that support more effective workforce development decisions. Participants will explore AI applications for skills-gap analysis, competency mapping, training needs analysis, personalized learning, learning content development, learning impact measurement, performance management, talent development, and future skills forecasting. The course also addresses responsible AI in learning and human performance, including data quality, employee privacy, algorithmic bias, fairness, transparency, ethical considerations, and human oversight. Participants will learn how to use AI to augment human capabilities and managerial judgment rather than replace professional decision-making. Through practical exercises, case studies, and applied workshops, participants will develop an integrated approach to connecting learning, competencies, and performance with the organization's current and future workforce requirements.

Learning Objectives

  • By the end of this course, participants will be able to:
  • Understand the strategic role of AI in Learning and Development and competency management.
  • Identify high-value AI applications for improving human performance.
  • Analyze workforce skills and competency data using AI-enabled approaches.
  • Identify current and future skills and capability gaps.
  • Develop data-driven competency and skills frameworks.
  • Apply AI to training needs analysis and workforce development.
  • Design personalized learning and development pathways.
  • Use AI to improve learning impact measurement and evaluation.
  • Connect learning, competencies, and employee performance with organizational objectives.
  • Apply predictive analytics to identify future workforce capability requirements.
  • Identify AI applications that can support performance management.
  • Strengthen talent development decisions through workforce analytics.
  • Assess privacy, bias, fairness, and ethical risks in AI-enabled workforce applications.
  • Establish appropriate governance and human oversight for AI-supported people decisions.
  • Develop an actionable roadmap for applying AI to learning, competencies, and human performance.

Who Should Attend

This course is designed for HR directors, Learning and Development managers, Talent Management leaders, Competency Management professionals, Performance Management managers, Organizational Development leaders, and Human Capital executives responsible for workforce capability development. It is also suitable for Learning and Development specialists, training managers, workforce planning professionals, People Analytics teams, talent development specialists, competency framework owners, HR transformation professionals, digital transformation teams, data and analytics professionals, and managers responsible for organizational capability development. The programme is particularly relevant to government and public-sector organizations, banks and financial institutions, oil and gas and energy companies, engineering and industrial organizations, telecommunications, healthcare, logistics, and large corporations seeking to build an agile, capable, and future-ready workforce.

Learning Outcomes

  • By the end of this course, participants will be able to:
  • Explain how AI can transform learning, competency management, and human performance.
  • Identify appropriate AI use cases across Learning and Development.
  • Analyze skills and competency data to identify capability gaps.
  • Develop AI-enabled skills and competency maps.
  • Identify training and development needs using workforce and performance data.
  • Design personalized learning and development pathways.
  • Apply AI to support learning content development and personalization.
  • Use analytics to evaluate learning effectiveness and business impact.
  • Connect learning and development with performance management.
  • Use predictive analytics to forecast future skills and capability requirements.
  • Identify high-potential talent and development priorities using data-driven insights.
  • Develop KPIs for measuring learning, competencies, and human performance.
  • Recognize data quality, privacy, bias, and ethical risks in AI-enabled people analytics.
  • Apply governance and human oversight to AI-supported learning and performance decisions.
  • Develop an integrated AI-enabled framework and roadmap for human capital development.

Course Outline

Course Outline

Day 1

Artificial Intelligence and the Future of Learning & Capability Development

  • Understanding Artificial Intelligence and its role in Human Capital.
  • From traditional training to AI-enabled learning.
  • The role of workforce data in capability development.
  • AI across the Learning and Development lifecycle.
  • Identifying high-value AI use cases for learning.
  • AI and organizational capability development.
  • Assessing organizational readiness for AI-enabled learning.
  • Building a data-driven continuous learning culture.
  • Aligning learning strategies with organizational objectives.
  • Practical Application: Identifying AI opportunities across the Learning and Development function.
Day 2

AI for Skills and Competency Management

  • Understanding modern skills and competency management.
  • Building data-driven competency frameworks.
  • Analyzing current workforce skills and capabilities.
  • Identifying skills and competency gaps.
  • Identifying critical skills for future roles.
  • AI-enabled skills-to-role matching.
  • Developing intelligent skills and competency maps.
  • Identifying individual and organizational development priorities.
  • Linking competencies with talent management and succession planning.
  • Practical Application: Developing a skills and competency map and identifying critical capability gaps.
Day 3

AI in Learning, Development and Human Performance

  • AI-enabled training needs analysis.
  • Designing personalized learning pathways.
  • Intelligent learning recommendations and content personalization.
  • Generative AI for learning content development.
  • Intelligent assistants and continuous learning support.
  • Monitoring learning progress and skills development.
  • Connecting learning with employee performance.
  • Using analytics to measure learning effectiveness.
  • Identifying performance development interventions.
  • Practical Application: Designing an AI-enabled learning and development pathway for a workforce segment.
Day 4

Predictive Analytics, Human Performance and Responsible AI

  • Predictive analytics for future skills requirements.
  • Forecasting workforce capability and competency gaps.
  • AI applications in performance management.
  • Analyzing performance and workforce capability indicators.
  • Identifying high-potential employees and development priorities.
  • Analytics for succession planning and talent development.
  • Employee data quality and its impact on AI results.
  • Employee privacy and workforce data protection.
  • Bias, fairness, transparency, and responsible AI in people decisions.
  • Practical Application: Assessing AI risks in learning, competency, and human performance applications.
Day 5

AI Strategy for Learning, Competencies and Human Performance

  • Building an integrated AI strategy for Human Capital.
  • Connecting Learning and Development, competencies, and performance management.
  • Prioritizing AI initiatives and use cases.
  • Evaluating business value, cost, risk, and expected return.
  • Developing KPIs and measuring the impact of AI initiatives.
  • Building an AI-enabled learning and competency operating model.
  • Managing change and developing AI capabilities within HR teams.
  • Strengthening collaboration between HR, data, technology, and business functions.
  • Building a sustainable AI-enabled workforce development ecosystem.
  • Final Workshop: Developing an Integrated AI Strategy and Implementation Roadmap for Learning, Competencies & Human Performance.

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