Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Personalized Learning and Adaptive Education focuses on the technology that tailors education to the individual. You learn how adaptive systems model what a learner knows, adjust content and difficulty in real time, and deliver targeted feedback and recommendations. The course covers learner modelling, knowledge tracing and the design of adaptive experiences, alongside the fairness, transparency and human-oversight questions that personalised learning raises. Connecting method to genuine learning benefit, it goes beyond adaptive-for-its-own-sake. You finish able to reason about designing an adaptive-learning solution. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in personalised learning and adaptive education — building systems that adapt content, pace and feedback to each learner’s needs.
📋 Course Objectives
1. Model what a learner knows and needs.
2. Apply knowledge tracing and learner modelling.
3. Adapt content, pace and difficulty.
4. Deliver targeted feedback and recommendations.
5. Address fairness, transparency and oversight.
👥 Who Should Enroll?
• Ed-tech developers and designers
• Educators and instructional designers
• Learning-science professionals
• Students of education technology
🚀 Key Learning Outcomes
• An understanding of adaptive learning AI.
• A learner-modelling perspective.
• A benefit-focused design approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
What Personalisation Must Respect
• Evidence base for adaptive instruction, and claims that outrun the evidence
• Spacing, retrieval practice and desirable difficulty as design constraints
• Learning styles and other persistent myths that shape bad products
Learner Models and Knowledge Tracing
• Bayesian knowledge tracing and mastery estimation
• Deep knowledge tracing and the interpretability trade-off
• Item response theory for calibrated difficulty and ability estimates
Sequencing and Content Selection
• Prerequisite graphs and mastery-gated progression
• Bandit and reinforcement approaches to next-item selection
• Avoiding filter bubbles that narrow a learner's exposure
Automated Assessment and Tutoring
• Automated scoring of open response and its failure modes
• Language-model tutors: scaffolding versus answer-giving
• Detecting and responding to disengagement rather than punishing it
Evaluation and Fairness
• Measuring learning gains, not time on platform
• Differential performance across language, disability and socioeconomic groups
• Teacher-in-the-loop design and preserving professional judgement
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
