The Central Board of Secondary Education (CBSE) has rolled out Sample Question Paper-1 for Class 12 Artificial Intelligence (Code 843), a strategic tool for the 2025-2026 academic session, released on March 24, 2025. This 50-mark, two-hour theory paper—geared toward the March 24, 2026, board exam—marks a pivotal step in embedding AI as a skill subject, aligning with India’s ambition to integrate AI education across schools from 2026-27. With over 18,000 CBSE schools already offering AI from Class 9, this sample emphasizes hands-on application over rote learning, addressing a 15-20% gap in practical STEM skills among graduates. Our analysis breaks down its architecture, pedagogical focus, and synergies with the revised curriculum, highlighting how it equips 1.2 million+ Class 12 students for AI-driven careers.
Paper Structure: A Balanced Blend of Skills and Concepts
The sample paper adopts a dual-part format to mirror the full 100-mark curriculum (50 theory + 50 practical), ensuring holistic assessment. It promotes exam familiarity while testing interdisciplinary competencies, with a blueprint that allocates marks across employability and AI-specific domains.
| Section | Marks | Duration Contribution | Key Components | Question Types |
|---|---|---|---|---|
| Part A: Employability Skills | 10 | Integrated (full 2 hours) | Communication, self-management, ICT basics, entrepreneurship, green skills | 6 MCQs (answer 4); 5 short answers (answer 3) |
| Part B: Subject-Specific Skills | 40 | Integrated (full 2 hours) | AI units: Capstone Project (16 Qs), Model Life Cycle (10 Qs), Story Telling through Data (8 Qs) | 24 MCQs (answer 20); 6 short answers (answer 4); 5 descriptive (answer 3) |
| Total | 50 | 2 hours | N/A | Mix of objective, case-based, and application-oriented |
This structure, available for download from cbseacademic.nic.in, includes visual aids like data flow icons and regression diagrams, enhancing comprehension for diverse learners. Notably, Part B draws from the 2025-2026 curriculum’s 120-hour theory allocation, emphasizing ethical AI and data ethics.
Emphasis on Practical Skills: Real-World Scenarios for Exam Readiness
At its core, the paper shifts from theoretical recall to applied problem-solving, reflecting CBSE’s NEP 2020 mandate for 50% competency-based questions in boards. Questions simulate industry challenges, such as:
- Data Modeling and Evaluation: Compute Root Mean Square Error (RMSE) for given datasets or compare Mean Squared Error (MSE) vs. RMSE in regression models—testing predictive analytics for scenarios like house price forecasting.
- AI Lifecycle Application: Describe advantages of cross-validation over train-test splits in model deployment, or outline scoping/design phases for capstone projects.
- Tool Proficiency and Storytelling: Identify AI platforms like DataRobot or Scikit-Learn for automation; craft narratives from data visualizations to convey insights on trends like climate impact.
CBSE officials underscore this orientation: “The paper is designed to practical skills, with scenarios such as predicting house prices using regression or stating AI tools like DataRobot and Scikit Learn.” Case-based queries (integrated in descriptive sections) encourage critical thinking, while MCQs ensure quick recall of ethics and bias mitigation—vital for the practical exam’s project defense (20 marks). This approach could boost student performance by 25% in application-based sections, per pilot feedback from 2024-25 trials.
Curriculum Integration: Aligning with National AI Education Reforms
The sample paper dovetails with CBSE’s 2025-2026 AI syllabus (Subject Code 843), a 200-hour module (120 theory + 80 practical) spanning units like AI foundations, neural networks, and natural language processing. It previews broader reforms: From 2026-27, AI and computational thinking will be mandatory from Class 3 in NCERT/CBSE schools, with electives in advanced ML for Classes 11-12—aiming to skill 25 crore students by 2030. Gender inclusivity is woven in, with Vigyan Dhara initiatives targeting 40% female participation in AI streams, countering the current 28% STEM gender gap.
- Practical Component Tie-In: The theory paper feeds into lab assessments, where students build models (e.g., using Python for sentiment analysis) and present viva-style defenses.
- Assessment Evolution: Internal choices (e.g., “answer any 4”) reduce stress, aligning with competency-based education to foster innovation over memorization.
This positions AI as a bridge to employability, with 70% of questions linking to NSDC-aligned job roles in data science and automation.
