By Dr Awinder Kaur

Artificial Intelligence has moved beyond the confines of computer science labs and specialized tech courses. It has subtly turned into a skill that is transforming the way we operate, study, and make decisions in nearly every field. From healthcare, manufacturing, finance, education, creative sectors, and public policy, AI is impacting significant results.

AI literacy doesn’t require all students to become data scientists or software engineers. It means knowing how AI tools basically work, how information leads to decisions, what their limitations and risks are, and how they might be used responsibly in a variety of contexts. In much the same way that digital literacy emerged two decades ago as an essential element for disciplinary studies, AI literacy now constitutes the next critical competence everyone will need.

Building critical thinking and problem-solving skills

Perhaps the biggest reason AI literacy is needed for students is to develop much stronger critical thinking skills. AI systems increasingly support decision-making in areas such as recruitment, supply chains, and financial risk assessment, among others. Without an understanding of how those systems are trained, the data they rely on, and the biases they may carry, there is a risk of acceptance of outputs at face value.

AI-literate graduates would be better equipped to question results, interpret insights, and combine human judgment with AI-driven analysis. Equally important, AI literacy allows students to solve some of the most complex problems in today’s world more effectively. Today’s challenges, whether they are about sustainability, access to healthcare or productivity, are all deeply interrelated and data-driven. AI tools allow students to analyze large datasets, model scenarios, and identify patterns that otherwise may be invisible. Importantly, this capability does not stop at purely technical roles but also involves managers, designers, policymakers, and researchers who have to work with AI-enhanced systems.

Preparing students for a global and industry-driven future

For students in big talent hotspots like India, AI literacy offers more options. India has been counted among the fastest-growing AI markets in the world, with rapid adoption of digital technologies in most industries. This demand is evident across our programmes, including the MSc Applied Artificial Intelligence, which has had a very strong first year, with Indian students making up around 20% of the cohort, highlighting India’s appetite for applied, responsible and industry-relevant AI education.

Students who understand how AI reshapes processes within industries will also be in a good position to contribute significantly to this transformation. At the same time, exposure to global standards of AI education helps graduates operate confidently in international environments, where the future of AI increasingly demands an “AI without boundaries” mindset, something that values collaboration across cultures, disciplines, and industries to drive innovation.

Universities will play a critical role in making AI literacy accessible and relevant outside the disciplines. This requires more than just taking AI as a subject; rather, include it into diverse programmes. For instance, business students can explore AI-driven decision-making; healthcare could do data-driven diagnostics; engineering students can be exposed to AI-enabled manufacturing systems; AI-powered tools could be used to scan vast databases of case law, and judgments,

It’s also the duty of universities to make AI literacy accessible and relevant across disciplines. This means moving beyond the treatment of AI as a subject in its own right and integrating it within diverse programmes.

Responsibility & ethics

The responsibility for education in AI is as much about learning as it is about its usage. As AI use increases, so do concerns around bias, transparency, deepfakes, cybersecurity, and data privacy. This is the responsibility of universities to embed responsible innovation and use of AI into their syllabus from the very beginning. Students also need to understand the ethical, social, and regulatory consequences of applying them in the real world.

Industry collaboration also becomes crucial here. Working with actual datasets, tools, and case studies makes AI education immensely more practical and relevant. The exposure to real challenges faced within the industry lets them understand how AI systems work in live environments, how decisions are made under real constraints, and how responsibility and accountability actually work.

AI literacy means preconditioning students for a world in which automation and intelligence are part of everyday work. Crucially, AI literacy is not just about preparing students for jobs, but about developing future leaders who can make informed, ethical, and strategic decisions in AI-enabled environments. AI-literate students do not just make themselves more employable but become adaptable, confident, and better equipped to shape technology rather than be shaped by it. By including AI literacy across disciplines, universities can prepare students not just for the first job but also for a lifetime.

Dr Awinder Kaur is the Associate Professor and Head – Digital Technologies and Machine Intelligence, University of Warwick.

DISCLAIMER: The views expressed are solely of the author and ETEDUCATION does not necessarily subscribe to it. ETEDUCATION will not be responsible for any damage caused to any person or organisation directly or indirectly.

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