LLM & the Rote Learning Debate

The birth of Large Language Models (LLMs) and machine learning did not arrive with a dramatic announcement in our daily lives. They entered quietly through smart suggestions, auto-complete, chatbots, and tools that promised to make our work faster and easier. Over time, these systems found their way into our emails, documents, presentations, and eventually, our classrooms.

What began as convenience soon became dependence.

The Emergence

Many people who once struggled to compose a simple email or articulate a clear sentence found a powerful ally in LLMs. Suddenly, writing felt effortless. A few prompts, and polished paragraphs appeared. It empowered those who lacked confidence in written communication but came with a set of questions:

·       Did they read what the model wrote for them?

·       Did they try to understand the structure, tone, or clarity of the message?

·       Or did the tool simply become a replacement for thinking rather than a support for learning?

What’s important is that fewer users asked a language model to teach them to communicate instead of making it their spokesperson.

Rote Learning

LLMs are often blamed for pushing learners back into copying, pasting, and reproducing information without understanding it. But, the irony is palpable. Rote learning existed long before artificial intelligence entered classrooms. Memorisation, recall-based assessments, and answer-focused teaching have shaped education systems for decades. LLMs did not invent this culture; they adapted themselves to it.

If anything, these tools reflect how learning has already been designed.

When students are rewarded for speed, accuracy, and output rather than reasoning, reflection, and process, they will naturally look for the fastest way to succeed. LLMs offer exactly that. Not because they promote shallow learning, but because our systems often do.

Blaming LLMs for rote learning is like blaming a calculator for weak number sense. The tool reveals the gap it doesn’t create.

The Classroom Culture

In many classrooms, learning still revolves around coverage, completion, and performance. Students are expected to move quickly, produce correct answers, and keep up with demanding academic schedules. There is little space for slow thinking, exploration, or struggle. In such environments, LLMs become coping tools. They help students ace their assessments in the blink of an eye, skipping depth or understanding of the subject. Survival becomes the agenda rather than learning.

And when survival becomes the goal, learning becomes transactional. The problem, then, is not that students use LLMs. It is how and why they use them.

Are they using these tools to:

• Generate ideas

• Explore multiple perspectives

• Improve clarity

• Reflect on structure

• Strengthen arguments

Or are they using them to:

• Avoid thinking

• Skip the process

• Deliver faster outputs

  
  

The difference lies not in the technology, but in the design of learning experiences. If classrooms continue to prioritise speed over depth, answers over reasoning, and products over process, students will naturally lean on tools that deliver quick results. LLMs simply meet the demand that the system creates.

Modifying the Usage

Educators can design tasks that require students to:

• Compare AI-generated responses with their own

• Analyse tone, structure, and clarity

• Edit and improve drafts

• Reflect on how meaning is constructed

• Question the accuracy and bias of information

  
  

In such spaces, LLMs become thinking partners rather than answer machines.

This shift requires us to move away from fear-based narratives and towards design-based solutions. Instead of asking, “Is AI ruining learning?” we might ask, “What kind of learning are we designing in the first place?”

Rote learning thrives in rigid systems. Deep learning thrives in thoughtful ones. LLMs are not taking us backwards. They are simply revealing where we already were.

Conclusion

The future of learning does not depend on banning tools, but on reimagining classrooms spaces where curiosity matters, reflection is valued, and thinking is visible. When education becomes less about speed and more about sense-making, students will no longer need shortcuts. They will have reasons to slow down, engage, and truly learn.

And in that world, LLMs won’t replace thinking. They’ll strengthen it.