What defines a modern learning platform isn’t its interface — it’s its intelligence. This course explores how to convert static learning systems into dynamic, AI-powered ecosystems that adapt to learners in real time. From integrating machine learning and analytics to automating learning paths and improving engagement, participants will learn how to build smarter platforms that support continuous learning and evolving organizational needs.
- Key differences between legacy platforms and AI-enhanced ecosystems
- Building blocks of an intelligent learning environment
- AI integration frameworks for LMS and LXPs
- Natural Language Processing (NLP) for learner interaction
- Predictive analytics and behavior tracking in L&D
- Dynamic content recommendation and personalization engines
- Workflow automation for upskilling and certification
- AI tools for adaptive assessments and feedback
- Real-time learning diagnostics and interventions
- Ensuring ethical use and transparency in AI learning systems
- Analyze gaps in traditional LMS platforms
- Plan AI transformation strategies for digital learning systems
- Deploy AI tools to automate personalization and engagement
- Use analytics to drive smarter learning decisions
- Implement adaptive testing and feedback mechanisms
- Integrate intelligent chatbots and NLP agents into platforms
- Align learning ecosystems with workforce development goals
- Manage change and risk in AI-enabled learning adoption
L&D leaders, instructional designers, HR tech strategists, platform administrators, and transformation consultants seeking to modernize learning systems by integrating AI-driven functionalities and intelligent automation features.
This program features case-based learning, live platform walkthroughs, tool simulations, AI workflow mapping, and collaborative ecosystem planning. Participants will engage in real-time problem-solving, build prototypes, and assess existing platforms against AI-readiness criteria.
- Understanding static vs. intelligent learning environments
- Principles of ecosystem thinking in L&D
- Core system architecture for AI integration
- Mapping learner data flows and touchpoints
- Overview of xAPI and LRS (Learning Record Store)
- Case review: Smart content delivery in action
- Introduction to AI personalization strategies
- Building learning recommendation engines
- Behavior-driven content adjustments using ML
- Platforms: Valamis, Cornerstone, Docebo AI
- NLP agents in learning (e.g., chatbots and voice AI)
- Lab: Designing a smart recommendation workflow
- Predictive analytics in learner performance
- Using heatmaps and engagement tracking tools
- Designing adaptive learning interventions
- Dashboards for skill gap analysis
- Platform integrations with HR systems and CRMs
- Lab: Building a learning insights dashboard
- Automating content tagging and metadata generation
- AI-generated microlearning and scenario branching
- Intelligent workflows for re-skilling and onboarding
- Integrating LXP, LMS, and content providers
- Tools: Synthesia, EdApp, LearnUpon AI
- Group exercise: Building an AI-enabled module
- AI ethics in learning (bias, transparency, fairness)
- Governance structures for AI-enhanced systems
- Risk management and privacy compliance (e.g., GDPR)
- Creating scalable AI transformation roadmaps
- Stakeholder management and internal buy-in
- Final project: Presenting your intelligent learning ecosystem blueprint
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.