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
Enhancing Student Engagement with AI focuses on a specific, high-value goal: keeping learners motivated and involved. You learn how AI can detect early signs of disengagement from learning data, personalise content and nudges to individual motivation, and power interactive experiences — from adaptive feedback to conversational tutors — that hold attention. The course keeps the emphasis on genuine engagement and learning benefit, and on the ethics of monitoring students. You finish able to reason about applying AI to a student-engagement challenge. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI for enhancing student engagement — detecting disengagement, personalising motivation and designing interactive, responsive learning experiences.
📋 Course Objectives
1. Detect early signs of disengagement.
2. Personalise content and motivational nudges.
3. Design interactive, responsive experiences.
4. Apply conversational and adaptive tutoring.
5. Address ethics of student monitoring.
👥 Who Should Enroll?
• Educators and instructional designers
• Ed-tech developers and product teams
• Learning-experience professionals
• Students of education technology
🚀 Key Learning Outcomes
• An understanding of AI for engagement.
• A learner-motivation perspective.
• An engagement-focused ed-tech project.
• 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
Defining What Is Being Improved
• Behavioural, emotional and cognitive engagement as separate constructs
• Attendance and click counts as weak proxies for actual learning
• Baseline measurement before any intervention is introduced
Detecting Disengagement
• LMS trace data, early warning systems and their realistic accuracy
• False positives and the harm of labelling a student at risk wrongly
• Prediction that leads to support, not to surveillance
Adapting Instruction
• Adaptive learning systems, mastery pathways and the evidence base
• AI tutors and feedback generation, with the accuracy caveat
• Learning styles as a persistent myth that adaptive marketing still repeats
Interactive Learning Experiences
• Retrieval practice, spacing and generative activity as the mechanisms that work
• Where AI genuinely lowers the cost of good pedagogy
• Assessment redesign when generated answers are freely available
Deploying Responsibly
• Student data privacy, FERPA and equivalent regimes, and consent
• Algorithmic proctoring harms and the documented accessibility failures
• Transparency with students and evaluating a vendor claim with evidence
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
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