Turn your L&D initiatives into dynamic, AI-driven engines for skill validation and competency alignment. This course equips professionals to deploy intelligent testing regimes, automate competency mapping, and generate real-time skill gap insights. Emphasizing ethical governance, predictive forecasting, and data-driven decisions, participants will gain tools to optimize talent development and create responsive learning ecosystems tied to business outcomes.
- AI-powered test automation and self-healing test scripts
- Automated competency framework generation and role-based models
- Real-time skill gap analysis using machine learning
- Generative AI for dynamic assessment item creation
- Intelligent dashboards for skills forecasting & workforce planning
- AI coaching assistants and adaptive feedback systems
- Ethical design, governance, and bias mitigation in AI systems
- Agile and augmented methodologies for continuous L&D improvement
- Integration with HR and talent systems for ecosystem alignment
- Future-ready workforce: AI-skills, Gen Z, and automation readiness
- Deploy AI-driven test automation in L&D settings
- Generate competency frameworks from job-role data
- Automate skill gap discovery using live performance inputs
- Create adaptive assessment items via generative AI
- Build intelligent dashboards to forecast workforce needs
- Leverage AI coaching tools to deliver personalized feedback
- Implement governance frameworks for ethical AI usage
- Apply agile/augmented techniques to scale L&D programs
L&D professionals, talent development leads, instructional technologists, HR analytics specialists, and learning designers who aim to embed AI into competency, assessment, and skilling ecosystems.
The program uses interactive labs, tool demonstrations, peer-driven workshops, and iterative design challenges. You’ll test real AI tools, model competency frameworks, and build real-life skill-gap dashboards—all within a collaborative, ethics-first learning environment.
- Overview of AI-enhanced test automation and self-healing scripts
- Key tools: Applitools, Functionize, Mabl
- Handling maintenance and reliability in automated test suites
- Lab: Build and debug an AI test automation prototype
- Peer feedback: refining test logic with learning accuracy in mind
- Introduction to data-driven competency modeling
- Using AI to derive frameworks from job descriptions and performance data
- Aligning frameworks with business goals and role profiles
- Workshop: Create a role-based competency model
- Case review: competency frameworks in dynamic companies
- Machine learning for real-time skills gap identification
- Integrating tools like Glider and DevSkiller
- Forecasting future skill needs using predictive analytics
- Lab: Build a skills-gap dashboard using sample data
- Workshop: Interpret insights and plan L&D interventions
- Deploying coaching bots and intelligent feedback loops
- Managing 'stagility': balancing innovation and stability
- Building governance frameworks: consent, bias review, transparency
- Lab: Prototype an AI coach using open-source tools
- Group debate: ethical scenarios in automated testing
- Embedding agile and augmented L&D processes
- Connecting AI systems to HRIS and talent platforms
- Preparing Gen Z / AI-native workforce
- Capstone: Design an integrated AI-skilling ecosystem proposal
- Final peer review, future roadmap, and
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.