AHSEC Class 12 Artificial Intelligence Syllabus 2026–27: Complete Details

AHSEC Class 12 Artificial Intelligence Syllabus: The Assam Higher Secondary Education Council (AHSEC) has released the Syllabus of Artificial Intelligence for Class XII (2026–27 onwards) for students studying Artificial Intelligence as a subject in Higher Secondary level. The updated syllabus focuses on advanced AI concepts, practical applications, and project-based learning to prepare students for future studies and careers in the field of Artificial Intelligence.

The syllabus builds upon the basic AI knowledge learned in Class XI and provides students with deeper understanding and hands-on experience with modern AI technologies.

AHSEC Class 12 Artificial Intelligence Syllabus 2026–27 Overview

• Board: Assam Higher Secondary Education Council (AHSEC)
• Class: XII
• Subject: Artificial Intelligence II
• Academic Session: 2026–27 onwards
• Official Notification: Click Here

The main objective of this course is to provide students with detailed knowledge of advanced AI techniques, including neural networks, deep learning architectures, generative models, and explainable AI systems.

AHSEC Class 12 Artificial Intelligence Unit-wise Syllabus 

Unit	Topic	Theory Topics	Practical Work
Unit 1	Artificial Neural Networks	• Introduction to Artificial Neural Networks (ANN) • Structure and function of neural networks • Perceptron and Multilayer Perceptron • Backpropagation • Activation Functions • Applications of Neural Networks	• Introduction to ANN framework in Scikit-learn • Implement a neural network to predict housing prices
Unit 2	Deep Neural Networks	• Introduction to Deep Learning and Deep Neural Networks • Convolutional Neural Networks (CNN) • Convolution and Pooling • Recurrent Neural Networks (RNN) • Sequence Modeling • Attention Mechanism • Transformer Architecture	• Introduction to PyTorch framework • Train a CNN model to classify handwritten digits using the MNIST dataset
Unit 3	Generative AI	• Introduction to Generative AI • Generative Models • Autoencoders • Generative Adversarial Networks (GANs) • Large Language Models (LLMs) • Applications of Generative AI	• Use a pre-trained GAN model to generate new images
Unit 4	Multimodal AI	• Introduction to Multimodal AI • Combining text, image, and audio data • Applications of Multimodal AI	• Build a basic multimodal model for sentiment analysis using text and image input
Unit 5	Explainability in AI (XAI)	• What is Explainable AI? • Importance of XAI • Making AI models interpretable • Trade-off between accuracy and interpretability • SHAP and LIME techniques	• Use SHAP or LIME to explain predictions of a machine learning model
Unit 6	Sustainable AI and SDGs	• Introduction to Sustainable AI • United Nations Sustainable Development Goals (SDGs) • Role of AI in climate action, education, and reducing inequalities • Ethical, social, and environmental challenges of AI	• Presentation on an AI model aligned with SDGs
Unit 7	Case Study	• Real-world applications of Deep Neural Networks • Computer Vision • Natural Language Processing (NLP) • Time Series Applications • Future Trends in AI Applications	• Analyze successes and failures of AI projects
Unit 8	Capstone Project	• Application of AI knowledge and skills to real-world problems • Developing complete AI solutions • Project presentation and findings	• End-to-end implementation of an AI project with visualizations

Learning Outcomes

After completing this course, students will be able to:

• Design and implement advanced AI models, including neural networks and deep learning systems.
• Apply AI techniques to solve real-world problems through practical projects.
• Understand and implement Generative AI and Multimodal AI technologies.
• Explain the importance of Explainable AI and sustainable AI development.

Practical Project List

• Housing Price Prediction using Neural Networks
• Handwritten Digit Classification using CNN and MNIST Dataset
• Image Generation using Pre-trained GAN Model
• Multimodal Sentiment Analysis using Text and Image Inputs
• Explaining Machine Learning Predictions using SHAP/LIME
• AI Model Presentation based on SDGs
• Complete AI Capstone Project Implementation

Importance of AHSEC Class 12 Artificial Intelligence Syllabus 2026–27

The revised AI syllabus helps students develop both theoretical knowledge and practical skills. Instead of only learning concepts, students get opportunities to build AI models and understand their real-world applications.

The course prepares students for higher studies and career opportunities in areas such as:

• Artificial Intelligence
• Machine Learning
• Data Science
• Robotics
• Software Development
• Research and Innovation

Conclusion

The AHSEC Class 12 Artificial Intelligence Syllabus 2026–27 provides students with a strong foundation in advanced AI technologies. With topics ranging from Neural Networks and Deep Learning to Generative AI and Sustainable AI, the syllabus is designed to develop problem-solving skills, technical understanding, and practical experience.

Students should carefully follow each unit, complete practical assignments, and focus on project-based learning to achieve success in Artificial Intelligence. The syllabus will help learners become better prepared for future academic and professional opportunities in the rapidly growing field of AI.

FAQs

1. What is AHSEC Class 12 Artificial Intelligence syllabus 2026–27?

Ans: It is an advanced AI syllabus covering Neural Networks, Deep Learning, Generative AI, Multimodal AI, Explainable AI, and practical projects.

2. How many units are included in the AI syllabus?

Ans: There are 8 units in the syllabus, from Artificial Neural Networks to Capstone Project.

3. Is practical work included in AI?

Ans: Yes, students will complete practical projects like CNN classification, GAN image generation, and AI model development.

4. What topics are covered in Generative AI?

Ans: Generative models, Autoencoders, GANs, Large Language Models (LLMs), and their applications are included.

5. What is the Capstone Project?

Ans: It is a complete AI project where students apply their knowledge to solve real-world problems.