What if your learning system could sense when a learner is about to forget, and deliver just the right content at just the right time? This course dives into the frontier of machine learning to make that a reality. Through smart algorithms and predictive models, participants will learn how to time content delivery, adjust repetition dynamically, and fine-tune frequency for each individual—transforming passive consumption into deeply personalized learning journeys.
- Fundamentals of machine learning in learning environments
- Content timing models based on learner behavior
- Dynamic frequency adjustment using AI algorithms
- Spaced repetition algorithms and memory decay curves
- Reinforcement learning for learning interval optimization
- Predictive analytics for knowledge decay forecasting
- Adaptive feedback and real-time learning path updates
- AI tools for automating personalized content delivery
- A/B testing models for delivery optimization
- Data ethics and learner privacy in ML-driven systems
- Explain core ML concepts used in learning optimization
- Apply spaced repetition models tailored by AI
- Customize timing and frequency of micro-content
- Use AI tools to deliver personalized repetition cycles
- Monitor learner data to refine delivery intervals
- Develop ML-powered learning loops using reinforcement logic
- Integrate ethical AI practices in learning systems
- Create adaptive pathways using behavioral and performance data
L&D professionals, learning engineers, instructional designers, education technology strategists, AI developers in training contexts, and performance consultants aiming to automate and optimize content delivery using intelligent timing and repetition systems.
The course combines case-based learning, hands-on tool application, live demos, and algorithm simulations. Delegates will engage in scenario-based design labs, test ML models for spaced repetition, and build adaptive content pathways using AI-powered platforms.
- Overview of ML concepts relevant to L&D
- Understanding learner behavior data structures
- Mapping learning outcomes to ML models
- Introduction to adaptive learning technologies
- Case examples: ML use in Coursera, EdApp, Duolingo
- Ethics and data privacy in machine learning
- Behavioral signal processing (clickstreams, engagement, pauses)
- Machine learning models to predict attention cycles
- Designing content release strategies using ML insights
- Introduction to AI tools: Realizeit, Area9 Lyceum
- Practice: Build ML-driven content timing maps
- Group feedback on timing design plans
- The science of forgetting and retention forecasting
- Designing personalized spaced repetition sequences
- Integrating Anki and Memrise ML models
- Using ML to calculate optimal learning intervals
- Tools: Cerego, SuperMemo, Smart Learning Rate AI
- Lab: Program personalized repetition for 3 learner personas
- Real-time data feeds and content frequency updates
- Reinforcement learning for dynamic content cycles
- Feedback loops and responsive adjustments
- Platforms: Domoscio, Knewton, Squirrel AI
- Design sprint: Create a smart content-frequency engine
- Peer testing and algorithm calibration
- A/B testing for timing and repetition strategies
- Interpreting algorithm outcomes in human terms
- Visualization dashboards: Tableau, Google Data Studio
- Building an end-to-end adaptive microlearning funnel
- Ethics review: explainability, bias, learner autonomy
- Final showcase: customized ML content delivery map
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.