Schools cannot prepare students for every future job; they can prepare them to remain capable learners.

As artificial intelligence reshapes education, Vivek Prakash, Co-founder & CEO of Codingal, discusses how schools can move beyond teaching technology as a standalone skill and instead use it to build problem-solving and computational thinking. In this interview with Education Post’s Prabhav Anand, he explores integrating coding across subjects, making project-based learning more experiential, and using AI as a thinking partner rather than an answer machine. He also highlights the gaps in implementing NEP 2020, particularly around teacher preparedness, curriculum integration and access. Looking beyond coding and AI, Prakash emphasises communication, curiosity, creativity, collaboration, critical thinking and continuous learning as essential to preparing students for an uncertain future.

1. You have argued that coding should be embedded into existing school subjects rather than treated as an additional burden. What would it take for Indian schools to make this approach work at scale?

Coding at scale will work when schools stop treating it as a separate subject. AI can be used across the curriculum as a facilitator. For mathematics, coding can be used to explore patterns. Experiments can be modelled for science. Data-driven projects can be made more engaging for social science. All this can be attained through age-appropriate curricula, teacher training, accessible devices, and practical resources. Schools should begin incrementally rather than wait for perfect infrastructure. Ultimately, the goal is not to make every child a programmer, but a confident problem-solver and creator.

2. In our previous conversation, you described coding as a “superpower” in the AI age. As AI becomes more accessible, how should schools redefine what students actually need to learn about technology?

Today, when both technology and information are available in abundance, schools need to shift their focus from teaching students the basic know-how of the technology to helping them understand how it works. Working with Artificial Intelligence is not satisfactory; students need to understand it well enough to question it, and dictate it, and create in tandem with it. This is where programming proves to be a meaningful contributor with logical thinking and system operations at its core, helping students to assess AI-generated codes to observe where they fail and deciding whether an output is reliable and worth building on.

3. You have emphasised moving students from passive consumption to active creation. How can this shift reshape the way schools approach project-based and experiential learning?

Moving from consumption to creation helps make project-based learning more central to the learning process. Additionally, it enables the students to discover, test, and apply new ideas instead of simply demonstrating pre-learnt concepts. Coding provides students with a platform to convert ideas into testable actions through building apps, debugging, feedback, and iteration exercises. Throughout these exercises, students also learn to persevere and learn how failure is not an endpoint, but another data point to arrive at a better solution. Teachers can assess the reasoning, collaboration and persistence behind a project, not simply the final product. This creates a more meaningful measure of learning, where the process of thinking and building becomes as important as what a student ultimately produces.

4. Codingal uses live instruction, projects, competitions and hackathons to engage learners. What does this model reveal about the limitations of conventional classroom-based learning?

Our experience shows that conventional classrooms can deliver concepts effectively, but students also need opportunities to apply them in unfamiliar situations. Live instruction provides interaction and immediate feedback, while projects give concepts a practical purpose. Competitions and hackathons add constraints, deadlines and independent decision-making. These experiences complement classroom teaching rather than replace it. When students build an app or game, they encounter problems that cannot always be solved through memorisation. That is where creativity, confidence and problem-solving become visible.

5. With AI increasingly capable of writing code and solving routine problems, should computational thinking now take greater priority than simply teaching students programming languages?

Yes, computational thinking deserves greater priority, but it should complement programming rather than replace it. Programming is an effective way to practise decomposition, pattern recognition, sequencing and testing. In an AI-enabled world, however, knowing a programming language is not enough. Students must frame problems, evaluate outputs, debug solutions and recognise when automated results are flawed. As AI handles more routine coding, the ability to define the right problem and judge the quality of a solution becomes even more important.

6. NEP 2020 has brought computational thinking and skill-based learning into sharper focus. What gaps still need to be addressed for these ideas to translate into everyday classroom practice?

NEP 2020 has created momentum, but policy intent must translate into everyday classroom practice. Key gaps include teacher preparedness, curriculum integration, assessment and access to appropriate infrastructure and resources. Computational thinking should not remain confined to computer labs. Teachers need practical training and subject-linked resources showing how these skills can fit existing lessons. Assessments must reward reasoning, experimentation and application rather than recall alone. Sustained teacher support and collaboration among policymakers, schools and education providers will be essential for meaningful implementation.

7. As students increasingly use AI tools for learning, how can educators ensure that technology strengthens critical thinking and creativity rather than encouraging dependence on ready-made answers?

AI should become a thinking partner rather than an answer machine. Students can first attempt a problem independently, explain their reasoning and then use AI to challenge or improve their approach. Teachers can ask students to verify AI-generated answers, identify errors, compare solutions and explain their choices. Assignments should include reflection, discussion and original application, where simply reproducing AI output is insufficient. The goal is not to prevent AI use, but to teach students how to question, evaluate and use it intelligently.

8. India is preparing students for jobs that may not exist yet. Beyond coding and AI, which foundational skills should schools prioritise to make students adaptable to a rapidly changing world?

Beyond coding and AI, schools should prioritise communication, curiosity, collaboration, creativity, critical thinking and continuous learning. Adaptability comes from being comfortable with unfamiliar problems rather than relying only on fixed technical skills. Students should learn to ask better questions, communicate clearly, collaborate effectively and respond constructively to failure. Ethical judgement will also matter as emerging technologies create new choices and challenges. If schools build curious, resilient learners who can independently acquire new skills, students will be better prepared for careers that are difficult to predict today. Ultimately, schools cannot prepare students for every job that the future may create. They can, however, prepare them to remain capable learners when the skills those jobs require inevitably change.