How to go beyond AI in education

AI clissing is powerful. He accelerates the work, evolves from ideas and connects information. But without context, conservation and personal agency, it leaves you painfully mediocre. To go beyond AI, focus on what it cannot do and work on non-practitioners in learning.

You can invite AI to write a test, to summarize a book, to resolve mathematics or the code. It provides quick and polished results. But being fast is not control and being polite does not need to be original. The rapid paradox is the illusion that all knowledge can be generated thanks to an appropriate incentive. The prompts are useful but insufficient to manage the context, conservation and metacognition. As you can see, learning also calls things that cannot be invited.

Actual experience

Reading on climate change can provide information, ideas and statistics, but understanding its impact on a fishermen's village implies knowledge of the increase in sea level in a lived context. The facts remain hollow without the weight of the experience of the real world. Not all contexts cannot be experienced. In the same way, all knowledge cannot be invited.

Invites can describe experiences, but they cannot reproduce the visceral nature of embodied learning, such as dance, the taste of a mango or the harm to learn cycling. Likewise, prompts can provide ethical executives, but they cannot really simulate the lived experience of moral deliberation.

Spotify has changed the way we listen to music. Instead of following artists or albums, many of us get algorithmic reading lists based on mood or other preferences. This is practical, but it flattens diversity and lacking the context. In the same way, in AI learning, when we receive personalized content, the context is collapsed and intellectual diversity is reduced. The treatment of knowledge in fragments The body of its depth.

Active conservation

Real learning, like real music, requires active conservation. Algorithms can help but should not decide. Otherwise, we risk being trapped in a family loop – never challenged, never changed. A repeated exposure to used information allows illusion of knowledge, no expertise. Conservation is to know what to leave aside to focus on what is important. The more we count on the promotion of Ai-Ai-Automated, the less we apply to agencies to choose with what to get involved as learners.

A student looking for war can bring together hundreds of articles in a few seconds and summarize his dimensions, but the formation of a perspective requires human discernment, which cannot be invited. Many online courses are often recycled, leading to a decrease in yields because each iteration only repeats its predecessors. Invite cannot solve the problem of digital regurgitation, which brings together internet with zero originality. Learners and educators must be aware that the real personal voice emerges from reality; Not from algorithms or altered data. If avatars blur reality, students risk losing a personal agency, becoming trapped in characters defined by algorithm.

Fighting with mathematical proof strengthens mental resilience. However, when AI provides rapid solutions, it short-circuits this essential struggle, retaining natural cognitive growth. Although AI can imitate thought models using graphic thought prompts, it lacks self -awareness. Metacognition – Thinking about your own thought – is the key to assessing and planning how you learn, in such situations. But here is the thing: you can't just invite him. To go beyond AI, focus on what it cannot do.

Before questioning the AI, reporting your thoughts to clarify understanding and shortcomings in understanding. Complete yourself with the real world. Read, discuss and experience to go further. Filter and actively interpret information to develop discernment, rather than passive consumption of online content.

Use AI as a starting point, not a conclusion. Integrate ideas instead of replacing them. Combine incentive with your ideas to contribute significantly rather than echo. While AI provides answers, only you can assess their meaning; The real understanding stems from your interpretation, and not from simple data of data where the originality SAPS of an endlessness.

Determine AI by creating questions that expose its limits and prejudices. Run bias audits by generating AI content on controversial subjects and examining inherent prejudices. Let the learner understand the fragility of the “knowledge” caused.

Practice cognitive disobedience: Question on algorithmic suggestions and respect your human agency. The undeniable dependence on the algorithmic authority will soon be a serious problem of AI in learning. Paradoxically, these practices will make you a promptur of the best and more responsible.

You can cause answers. You cannot invite yourself to understand. A real learning occurs where AI stops and you go from incitement to understanding with the autonomy of the learner. There, you are counting on context, metacognition, personal expression and constructive struggle of meaning creation. Surrounded by algorithms, if we do not face potential biases and to ignore the unptable in learning, we reduce ourselves to an average learner. This is the act of critical engagement with AI. It separates thinkers from simple AI users.

The views expressed are personal

The writer is assistant secretary of the University Grants Commission.