AHSEC / ASSEB Higher Secondary Artificial Intelligence Syllabus (2025–2026)
Comprehensive curriculum blueprint approved by the Assam State School Education Board (Bamunimaidam, Guwahati-21) in consultation with faculty from IIT Guwahati and Dibrugarh University. Complete unit-by-unit marks distribution, practical examination rubrics, and textbook errata guidelines for Class XI & XII.
Class XI Artificial Intelligence Syllabus (Sub 527)
Curriculum approved by ASSEB in consultation with IIT Guwahati & Dibrugarh University. Total 100 Marks (50 Theory + 50 Practical).
Part A: Employability Skills
10 Marks • 60 Prescribed Periods| Unit | Unit Title & Domain Focus | Periods | Marks | Study Link |
|---|---|---|---|---|
| Unit 1 | ICT Skills (OS fundamentals, cloud tooling, file hygiene & cyber security) | 15 | 4 | Read Unit → |
| Unit 2 | Entrepreneurial Skills (Startup mindset, venture planning & Assam tech opportunities) | 10 | 4 | Read Unit → |
| Unit 3 | Green Skills (Sustainable practices, green IT & environmental ethics) | 10 | 2 | Read Unit → |
Part B: Subject Specific Skills (AI Core)
40 Marks • 150 Prescribed Periods (50 Theory + 100 Practical)| Unit | Official Subject Unit Title | Theory (Periods) | Practical (Periods) | Marks | Chapter Resources |
|---|---|---|---|---|---|
| Unit 1 | Introduction to Artificial Intelligence | 4 | 10 | 4 | |
| Unit 2 | AI Paradigms | 6 | 10 | 5 | |
| Unit 3 | Mathematics for AI | 6 | 15 | 5 | |
| Unit 4 | Programming for AI | 10 | 20 | 5 | |
| Unit 5 | Machine Learning Paradigms | 4 | 10 | 4 | |
| Unit 6 | Data Collection and Exploration | 6 | 15 | 6 | |
| Unit 7 | Supervised Algorithms | 5 | 10 | 5 | |
| Unit 8 | Unsupervised Algorithms | 4 | 5 | 4 | |
| Unit 9 | Storytelling with Data | 5 | 10 | 5 | |
| Unit 10 | AI Ethics and Values | 4 | 5 | 4 | |
| Unit 11 | Case Studies & Project | 4 | 10 | 4 |
Part C: Practical Examination Rubric & Marks Distribution
50 Marks • 3 Hours Evaluation ProtocolExecution of Python algorithms (NumPy array manipulation, Pandas data cleaning, or Scikit-learn model fitting).
Analyzing real-world dataset scenario, model evaluation metrics, precision/recall trade-offs, and bias audit.
Submission and demonstration of a working Python script based on curriculum algorithms.
Verified laboratory journal documenting a minimum of 15 practical session exercises and outputs.
Oral defense in front of the external board examiner covering code concepts, libraries, and error resolution.
Teacher evaluation based on laboratory attendance, session participation, and hands-on ethics adherence.
Class XII Artificial Intelligence Syllabus (Sub 527)
Advanced AI curricula featuring Artificial Neural Networks, Generative AI, Multimodal systems, and UN SDGs. Total 100 Marks (50 Theory + 50 Practical).
Part A: Employability Skills - IV
10 Marks • 60 Prescribed Periods| Unit | Unit Title & Focus Area | Periods | Marks | Study Link |
|---|---|---|---|---|
| Unit 1 | Communication Skills - IV (Technical reporting, presentation & stakeholder dialogue) | 15 | 4 | Read Unit → |
| Unit 2 | Entrepreneurial Skills - IV (Tech venture scaling, intellectual property & compliance) | 10 | 4 | Read Unit → |
| Unit 3 | Green Skills - IV (Corporate green governance, energy-efficient cloud & e-waste management) | 10 | 2 | Read Unit → |
Part B: Advanced Artificial Intelligence (Subject Specific)
40 Marks • 150 Prescribed Periods (50 Theory + 100 Practical)| Unit | Advanced AI Unit Title | Theory (Periods) | Practical (Periods) | Marks | Chapter Resources |
|---|---|---|---|---|---|
| Unit 1 | Artificial Neural Networks | 6 | 15 | 8 | |
| Unit 2 | Deep Neural Networks | 6 | 15 | 8 | |
| Unit 3 | Generative AI | 6 | 15 | 9 | |
| Unit 4 | Multimodal AI | 6 | 15 | 8 | |
| Unit 5 | Explainability in AI / XAI | 6 | 15 | 8 | |
| Unit 6 | Sustainable AI and UN SDGs | 6 | 15 | 9 | |
| Unit 7 | Case Study | 6 | 15 | – | |
| Unit 8 | Capstone Project | 6 | 15 | – |
Part C: Class XII Practical Examination Rubric
50 Marks • 3 Hours Evaluation ProtocolLive implementation of Artificial Neural Network, image classification, or fine-tuning transformer pipelines during exam.
Evaluation of original Capstone Project solving local Assam challenges (Assam Tea Blight, Flood Alert, Assamese Vernacular OCR).
Certified laboratory journal with minimum 20 hands-on code experiments, error tracebacks, and model accuracy curves.
Viva Voce questions posed by external and internal examiners testing conceptual neural architecture and prompt safety.
Prescribed Laboratory & Computing Infrastructure
Official specifications for school computer laboratories conducting ASSEB Subject 527.
Python 3.10+ Ecosystem
Standard Anaconda or standalone Python 3.10 installation with standard data science packages: NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
Hardware Specifications
Any standard school PC equipped with a Dual-Core 2.0 GHz CPU, 4 GB RAM, and 20 GB free disk space. Dedicated GPU graphics cards are not required.
Cloud Jupyter / Google Colab
For Class XII Deep Learning and Generative AI modules, free cloud Jupyter notebook runtime via Google Colab is officially recognized for training neural networks.
Frequently Asked Questions on ASSEB AI (Subject 527)
Everything students, parents, and school faculties need to know regarding admissions, examination criteria, and university recognition.
What is the official Subject Code for Artificial Intelligence in AHSEC / ASSEB?
The official elective subject code is 527 for Higher Secondary (Class XI and XII). For vocational streams under the National Skills Qualifications Framework (NSQF Level 4: AI Assistant), the designated subject code is 985.
Can students from Arts and Commerce streams take Artificial Intelligence in Class 11?
Yes. ASSEB designed Subject 527 as an open academic elective available to students across all three streams: Arts, Science, and Commerce. No prior advanced mathematics or computer science background is required to enroll.
What is the Theory vs Practical marks distribution for ASSEB AI examinations?
The examination carries a total of 100 marks: 50 Marks for the 3-hour Board Theory Paper (comprising Part A Employability Skills for 10 marks and Part B Subject Specific Skills for 40 marks), and 50 Marks for the 3-hour Board Practical Examination (Part C).
How is the 50-mark Practical Examination scored in Class 11 and Class 12?
For Class XI: Live Python Programming (15m), Basic AI Python Project (10m), Case Study Interpretation (10m), Practical Session File (5m), Viva Voce (5m), and Continuous Internal Assessment (5m). For Class XII: Deep Learning/Python implementation (15m), Regional Capstone Project (15m), Practical Lab Journal (10m), and Board Viva Voce (10m).
What programming language and libraries are prescribed in the official syllabus?
Python 3.10+ is the prescribed programming language. Core required libraries include NumPy, Pandas, Matplotlib, and Scikit-learn for Class XI, expanding to TensorFlow/PyTorch, Hugging Face Transformers, and Gradio/Streamlit for Class XII.
What are the Part A Employability Skills units for Class 11 and 12?
Part A carries 10 marks and 60 periods in both years. In Class XI: Unit 1 ICT Skills (4m), Unit 2 Entrepreneurial Skills (4m), and Unit 3 Green Skills (2m). In Class XII: Unit 1 Communication Skills-IV (4m), Unit 2 Entrepreneurial Skills-IV (4m), and Unit 3 Green Skills-IV (2m).
Start Studying the 26 Curriculum Units with Free Chapter Notes
Access verbatim textbook walkthroughs, verified Python 3.10 bug fixes, trilingual concept decoders (অসমীয়া • বাংলা • English), and timed board MCQ practice quizzes.