On April 1, the CBSE launched its Computational Thinking and Artificial Intelligence (CT & AI) curriculum for Classes 3 to 8, igniting considerable debate. Stakeholders, including students, teachers, and schools, need clarity on the design and objectives of the curriculum.
The drivers of this curricular area are the aspirations enunciated in the National Education Policy 2020, which call for India to pioneer emerging areas such as Artificial Intelligence, Machine Learning, Coding, Robotics, etc. The National Curriculum for School Education (NCF-SE 2023) recognises it as an area that needs to find a place in the school curriculum. This is a significant departure from the earlier approach, where curricular areas were limited to subjects such as Mathematics, Science, Social Sciences, and languages. Thus, accommodating these is challenging in the existing scheme. Also, the subject of the introduction of this new curricular area remains an evolving issue. Thus, although the present effort of the CBSE seems to take that debate in one direction, it may provide a good starting point for the debates around the necessity and structuring of this area.
The context is also the changing world around us. No technological advancement has been as meteoric in its rise or as far-reaching as AI. AI has changed how we learn, work, and make decisions. In the context of education, students are increasingly engaging with AI tools; therefore, the real question for education is not whether students must be exposed to AI, but rather what is the best way to prepare and empower them to engage with AI meaningfully.
Another critical factor necessitating such efforts is the widespread accessibility of AI that has been enabled by a variety of tools, leading to an increasing tendency to conflate AI education with access/ability to use AI tools. Students are already experimenting with chatbots, image generators or coding tools. While such exposure is valuable, they end up treating these tools as black boxes.
What is the vision? Key to our efforts is our vision of an AI-ready generation that will not be a mere customer for these tools, but also participate in building these tools, and importantly, understand their risks, benefits and limitations. Some countries, including the UK, Finland, Singapore, Australia, China, and South Korea, have already integrated Computational Thinking and AI into their school curricula. Our approach has been centred on the premise that such literacy demands the ability to think systematically about problems, data and solutions, which is where Computational Thinking (CT) becomes indispensable.
CT is not coding, nor is it computer science. It advocates a systematic approach to problem-solving by breaking problems into parts (decomposition), identifying patterns, using abstraction, and designing step-by-step solutions (algorithms). Brizard and Mills (2021) describe these as connected skills applicable across domains, from data analysis to automated decision-making. Yadav et al (2016) note that CT also involves knowing when a computer can help solve a problem more efficiently. CT thus forms a strong base for the development of AI and ML capacities; these foundational cognitive skills will serve their purpose beyond computing, as they are relevant across multiple disciplines and in day-to-day problem-solving. CT connects naturally to the complex technologies that underpin AI. Machine learning heavily relies on pattern recognition. Key concepts in AI, such as statistical inference, high-dimensional vector spaces, and iterative optimisation, ultimately stem from structured ways of thinking about data and their relationships. Therefore, as also suggested by research in the domain, CT serves as a key backbone for problem-solving practices that enhance the development of AI/ML capabilities. Without foundational CT education, AI will be admired but not understood by our students, and may remain a black box.
Keeping this in mind, the CBSE CT & AI curriculum adopts a graded age-sensitive approach to cultivate CT & AI literacy across various stages, paving the way for AI-ready learners and AI skilling. Instead of introducing AI concepts in isolation, the CT & AI curriculum emphasises CT like any other school subject, with a deliberate progression from structured thinking/CT to AI concepts to AI applications. In Classes 3-5, CT is part of the mathematics curriculum, and in Classes 6-8, the treatment becomes deeper. By the end of Class 8, students are expected to have a foundational understanding of AI concepts and AI ethics. This gradation is a conscious choice, so that children find the transition from CT to AI and ML smoother.
There is a fear that the introduction of new curricular areas is burdening students. Content burden is a question of priority. Throughout NCFSE and NEP, the emphasis has been made on CT & AI, ML, etc., but adequate space has not been created until now. This curriculum builds on CT to introduce areas such as AI, coding, and ML. Though CT has been addressed in the existing textbooks of Mathematics, the scope for enrichment was visible. So, the curriculum suggests a set of activities to enhance CT skills in students.
There is also an apprehension that the introduction of AI does more harm than good. The point is that students from Class 5 onwards (maybe even earlier in some schools) are already exposed to AI tools and are using them without proper training or an understanding of the risks involved. Therefore, it is critical to build AI literacy (basic understanding of AI tools and understanding ethical use of AI), which is mostly independent of mathematical skills, and will help students appreciate the benefits and risks of AI better.
The curricular goals are derived in alignment with the curriculum standards detailed in the NCF. The objectives mentioned in the curriculum may sound loftier, but one must interpret these objectives for the particular grade. For example, the curriculum states that a “child distinguishes machine intelligence and human intelligence”. The interpretation must be made keeping children’s age and the learning experiences in mind. We know that this “ability to distinguish” varies for children of different age groups. A 5th grader can do it, an 8th grader too, but what differs is the “degree” and complexity of this “distinguishing capacity”. It also depends on the kind of material and experiences the child engages with.
The curriculum is also flexible, as any good curriculum should be. The CT activities suggested in the curriculum are illustrative, and the AI projects included are suggestive. This helps the teachers to bring in contextual changes needed for the curriculum while implementing it. It provides a lot of autonomy for the teachers to choose the content and pedagogy suited to their students. The curriculum also has a set of support materials for teachers and students. In fact, the teacher’s handbook is much more elaborate than the student material. As AI is an emerging area, the curriculum highlights the criticality of teachers’ capability for curriculum implementation. It also suggests approaches for teacher capacity development, and the handbook directly addresses teacher capacity.
The grand aim of the curriculum is to develop learners who do not merely consume technology, but are informed, capable and responsible digital citizens, who can critically question, analyse, and even create, shaping our AI-rich future.
The writer is a member of the core leadership, IIT Madras Bodhan AI Foundation and professor, Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras. The author wishes to acknowledge inputs from his team in preparing this article
