CBSE makes AI a part of formal teaching, NCERT develops module

NCERT has developed an introductory module combining computational thinking and artificial intelligence for students in Classes IX-XII. CBSE has earmarked the module for school-based assessment, so schools will evaluate students internally rather than through a separate central examination. The aim is to make basic AI understanding part of school education.

The module introduces how AI works, where it is useful, and where it can fail. For example, students may learn to question a chatbot’s polished answer or spot a deepfake instead of accepting digital content immediately. These activities connect classroom learning with everyday technology.

NCERT prepared the module in consultation with IIT Madras and IIT Jodhpur. CBSE announced that the modules would support internal assessment during 2026-27. Separate textbooks are also being prepared for students who study AI at a more advanced level, suggesting a pathway from basic awareness to deeper subject learning.

The new introductory course is a 20-hour module covering computational thinking and artificial intelligence. It is intended for students in Classes IX-XII, giving them a shared foundation even if they do not select AI or computer studies as electives. CBSE has linked the module to school-based assessment.

Its short format makes the subject accessible without turning every student into an advanced programmer. Lessons can introduce how technology works, how AI is used, and why its outputs need checking. A student might examine whether an online image is genuine or whether a chatbot has supplied reliable information.

The module is part of the 2026-27 academic plan announced by CBSE. Class IX students will face a compulsory CT and AI module, while Class X remains under the existing scheme. NCERT is also preparing separate AI textbooks for students taking the subject at a more advanced level.

Computational thinking means approaching a problem through clear steps, patterns, rules, and smaller parts. It does not simply mean using a computer or writing code. It teaches learners to describe a problem precisely and design a process that could be followed consistently. This is useful because AI systems depend on structured instructions, data, and decision processes.

For example, a student could sort examples of genuine and misleading online content by identifying repeated clues. The student would separate the problem into parts, compare patterns, and create rules for checking evidence. That exercise develops the same kind of organised reasoning needed to understand how AI tools classify information or generate responses.

The article reports that NCERT developed the CT and AI module with IIT Madras and IIT Jodhpur. The source does not give a formal definition of computational thinking, so this explanation uses the established educational meaning. Together, CT and AI offer a foundation for responsible technology use.

AI literacy is being taught broadly because artificial intelligence is already part of everyday activity. The education ministry official cited in the article pointed to studies, research, content writing, and career guidance. Students may encounter AI even if they never choose computer science or AI as an elective. Basic knowledge can help them use these tools thoughtfully.

For instance, a student using a chatbot for research should not treat a fluent response as automatically true. The module is intended to explain how AI tools work, where they are useful, and where they can make mistakes. Fact-checking becomes a practical habit, not merely a technical skill for specialists.

This approach makes AI awareness a common school skill rather than a niche subject. NCERT wants students to become familiar with the technology before moving to more structured textbook study. CBSE’s internal-assessment plan for 2026-27 gives schools a formal way to introduce that foundation.

The module will build practical judgment around AI, not just technical familiarity. Students are expected to learn how to use AI tools correctly, check information, and recognise the technology’s limitations. This matters because digital content can look convincing even when it is manipulated or inaccurate.

A concrete example is a chatbot that gives a smooth, confident answer containing errors. Instead of accepting it, students can compare the claim with reliable information and verify the evidence. They may also learn to spot deepfakes, which requires looking carefully at digital material rather than trusting its appearance. The key mechanism is critical checking before sharing or using content.

These skills connect AI learning with everyday decisions in education and research. The article describes AI use in content writing and career advice as well. By teaching verification early, schools can prepare students to use AI productively while recognising that its answers and outputs require human judgment.

For Class IX, computational thinking and artificial intelligence will become compulsory during the 2026-27 academic session. This changes AI awareness from an optional interest into a required part of the class’s school-based learning. Students will encounter the subject even if they do not plan to specialise in computers or AI.

The requirement is built around introductory learning. Students will become familiar with how AI works, its uses, and its limitations. They will also learn to use AI tools correctly and fact-check information. In practice, a Class IX learner might examine whether a chatbot answer is reliable or whether digital content could be a deepfake.

CBSE’s revised scheme makes CT and AI compulsory for Class IX, while Class X continues under the existing scheme. NCERT developed modules for Classes IX-XII for internal assessment, and the board announced their use in 2026-27. The change therefore begins with Class IX rather than applying identically across both classes.

AI systems commonly learn by processing examples in data and detecting patterns among them. During use, they generate an answer by estimating which words, labels, or outputs best fit the input and patterns learned earlier. This helps explain why AI can respond quickly across many topics, but it does not guarantee that every response is factual.

For example, a chatbot may have seen many sentences that resemble a question and produce a polished answer based on likely language patterns. If its training data is incomplete, conflicting, or unsuitable, the result may contain an error. The answer can still sound confident because fluent wording and factual accuracy are different things.

The article highlights this risk by asking students to question confident but incorrect chatbot answers and fact-check information. It does not explain AI training technically, so this explanation uses established knowledge. The module’s broader purpose is to teach students both AI’s uses and its limitations before advanced study.