Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Education Technology looks at how machine learning is reshaping teaching and learning, and how to apply it responsibly. You will examine adaptive and personalised learning systems, intelligent tutoring, automated assessment and feedback, and learning analytics that flag students who need support. The course pairs the techniques with hard questions: fairness, privacy of student data, and the risk of over-automating a fundamentally human process. You leave able to evaluate or design an ed-tech feature that genuinely helps learners rather than merely adding AI for its own sake. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
AI in Education Technology explores how AI powers adaptive learning, intelligent tutoring, automated assessment and learning analytics — and the ethics of using it well.
📋 Course Objectives
1. Explain adaptive learning and intelligent-tutoring systems.
2. Apply AI to automated assessment and feedback.
3. Use learning analytics to identify at-risk learners.
4. Weigh fairness, privacy and data-ethics in ed-tech.
5. Evaluate whether an AI feature genuinely helps learning.
👥 Who Should Enroll?
• Educators and instructional designers
• Ed-tech product and content teams
• Data scientists working in education
• Students of learning science and technology
🚀 Key Learning Outcomes
• The ability to evaluate or design an ed-tech AI feature.
• A critical, ethics-aware view of AI in education.
• A project applying AI to a learning problem.
• 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
AI Fundamentals, Mathematics, and Foundations
Apply linear algebra and calculus concepts to solve AI-related problems in education technology • Analyze the role of probability and statistics in machine learning models for educational data analysis • Develop a comprehensive understanding of AI fundamentals, including supervised, unsupervised, and reinforcement learning
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines to preprocess and feature-engineer educational datasets • Configure data storage solutions, such as relational databases and NoSQL databases, for education technology applications • Evaluate the effectiveness of data preprocessing techniques, including handling missing values and data normalization
Model Architecture, Algorithm Design, and Methods
Implement deep learning models, including convolutional neural networks and recurrent neural networks, for educational data analysis • Develop and train machine learning models using popular algorithms, such as decision trees and random forests • Optimize model architecture and hyperparameters to improve performance on educational datasets
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate machine learning models using techniques, such as cross-validation and walk-forward optimization • Analyze the performance of machine learning models using metrics, such as accuracy, precision, and recall • Implement hyperparameter tuning using grid search, random search, and Bayesian optimization
Deployment, MLOps, and Production Workflows
Deploy machine learning models using cloud-based platforms, such as AWS SageMaker and Google Cloud AI Platform • Design and implement MLOps workflows to automate model training, deployment, and monitoring • Configure and manage production-ready environments for education technology applications
Ethics, Bias Mitigation, and Responsible AI Practices
Evaluate the ethical implications of AI in education technology, including bias, fairness, and transparency • Develop strategies to mitigate bias in machine learning models and ensure fairness in educational outcomes • Implement responsible AI practices, including data privacy, security, and accountability
Industry Integration, Business Applications, and Case Studies
Analyze the applications of AI in education technology, including personalized learning, intelligent tutoring systems, and automated grading • Develop business cases for AI-powered education technology solutions, including cost-benefit analysis and ROI calculation • Evaluate the effectiveness of AI-powered education technology solutions using case studies and industry reports
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Scikit-learn |
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