CBSE Class 10 Artificial Intelligence
A complete digital coursebook for Class 10 students covering the AI Project Cycle in depth, modelling foundations, model evaluation, statistical data reasoning, no-code analytics, computer vision, and natural language processing. Ethics, fairness, and responsible design run through every unit alongside a final capstone AI project.
Infographic slides
Interactive games
Teacher-ready view
Fullscreen learner mode
Animated slide stage • games • app blocks • journal tasks
Review the Class 9 foundations, understand the capstone roadmap, and set up the evidence journal you will use across the course.
- Recall the main Class 9 ideas that this course builds on
- Identify the six-unit capstone journey you will follow in this course
- Set up an evidence journal for reflections, labs, and capstone notes
Revisit the AI Project Cycle, refresh the three AI domains, and use ethical frameworks plus bioethics to judge real AI systems.
- Explain the six stages of the AI Project Cycle in plain language
- Reconnect real systems to Data for AI, Computer Vision, and NLP
- Compare value-based and sector-based ethical frameworks, including rights-based, utility-based, virtue-based, and bioethics lenses
- Apply bioethics principles to critique a healthcare AI case
Understand how machines learn: distinguish AI, ML, and DL, compare supervised, unsupervised, and reinforcement learning, and build intuition for perceptrons and neural networks.
- Distinguish AI, machine learning, and deep learning and decide when systems are rule-based versus learning-based
- Compare supervised, unsupervised, and reinforcement learning with clear real-world examples, including clustering and association
- Explain in simple language how a perceptron and a neural network convert inputs into predictions
- Describe loss, backpropagation, learning rate, and overfitting without relying on heavy mathematics
Test whether a model can be trusted on unseen data. Learn train/test splits, confusion matrices, metrics, thresholds, and fairness checks before deployment.
- Explain why training, validation, and testing data must stay separate
- Diagnose overfitting, underfitting, and data leakage using model behaviour
- Build and interpret a confusion matrix using TP, TN, FP, and FN
- Choose between accuracy, precision, recall, and F1 based on real-world cost
- Explain why subgroup evaluation matters before deployment
Learn how to reason with samples, averages, spread, charts, and no-code workflows. Build a simple data dashboard in the universal lab and communicate one evidence-based insight.
- Distinguish population, sample, representative sample, and sampling bias
- Interpret mean, median, mode, range, and standard deviation in simple datasets
- Choose suitable chart types and spot misleading visualisations
- Calculate simple absolute error and mean absolute error for regression-style predictions
- Explain how a no-code analytics workflow turns raw data into an insight
- Build a simple data dashboard or monitoring workflow in the universal lab
Understand how machines turn pixels into meaning. Learn image basics, core computer vision tasks, CNN intuition, data-quality risks, and build a simple visual classifier.
- Explain that digital images are grids of pixels and color values
- Distinguish classification, localisation, object detection, and segmentation
- Describe in simple language what convolution, activation, pooling, and output layers do
- Explain why lighting, angle, background, and class balance affect computer vision accuracy
- Build or test a simple computer vision classifier and record one honest failure case
Understand how machines read, write, and interpret human language. Build a sentiment classifier, explore chatbots and large language models, and close the course with your final capstone submission.
- Explain how raw text is converted into machine-readable representations
- Describe the NLP pipeline from tokenisation to stemming or lemmatisation, Bag-of-Words, TF-IDF, and prediction
- Analyse sentiment analysis, named entities, chatbots, and large language models
- Build a simple text classifier and present the full Class 10 AI capstone clearly
