A New Dawn for Learning: The Transformative Journey of Education, from Tutors to AI

Before 2010, the methods of acquiring knowledge were relatively static. Our schooling was entirely based upon these three elements: textbooks, school teachers, and, in case it was critical, a tutor. As the only solutions available to an uncracked question were either to wait for the next day’s period or dedicate the night to flipping through volumes, there was hardly ever any distraction. We had complete faith in this traditional approach. Initially, the disruption originated from platforms like YouTube. All of a sudden, we had the best professors in the world or simple explanations of complicated topics at our fingertips. This soon transitioned into platforms dedicated solely to online education, such as Khan Academy and Coursera, thereby intensifying access to learning. They offered us the opportunity to learn at our own pace, when and where we wanted to. These days, a different technology is staking a larger claim to that space: Artificial Intelligence (AI). More information is being constantly released by AI, but now AI is a personal assistant, a patient tutor, and a quick and flexible engineer of solutions for us, changing how we learn.

A student who can look up from their desk in the middle of the night, unable to solve the math problem, need not pick up the phone and call their teacher; they can simply pull up a Computational Knowledge Engine, such as WolframAlpha, or photograph the equation with PhotoMath, and receive an immediate step-by-step solution, oftentimes with no more than a click of a mouse. Rather than simply providing the answer, these tools show the steps to arrive at the solution, reinforcing the students’ knowledge. This is because writing does not happen in a vacuum; when a student sits down to complete an assignment, they have an indefatigable editor working alongside them—Grammarly (free version) or another grammar-checking software. Grammarly is a tool that provides not only spelling and grammar corrections but also helps improve writing style, vocabulary, and sentence clarity. Similarly, when a text needs to be paraphrased or reformulated, tools such as QuillBot or general Generative AI can help students avoid redundancy and expand their vocabulary.
Yet, the most groundbreaking application of AI is individualized tutoring.

These specialized AI tutors examine the information on how each student has performed to identify their weak areas with pinpoint accuracy (Khanmigo). Importantly, rather than providing a verbatim answer, it employs a Socratic method by asking leading questions to help the student reason out the answer for themselves. This quality is one of the factors that distinguishes AI from traditional tutoring methods. AI tools are helping to find the right information from vast amounts of information for university-level and research work. Tools such as ChatPDF or Perplexity AI essentially enable students to upload and ask questions of thousands of pages of PDF documents, returning either specific or summarized answers, which makes the research process orders of magnitude faster. Additionally, AI-based design tools, such as Canva Magic Studio or Gamma App, can be utilized by students to create visually appealing presentation slides or infographics for their projects in a matter of time.

At the core of all these abilities are the complex training mechanisms that underpin AI models. Based on Transformer Architecture and Deep Learning algorithms, these models are trained on billions of learning data points. Then comes a specialized process known as Reinforcement Learning with Human Feedback (RLHF), wherein human experts (teachers) rate the responses of the AI, ensuring that it not only makes factually correct assertions but also behaves as a patient, ethical, and pedagogically sound educator. Training these educational AI tools to behave well is more than just generic data; it is a highly advanced, multifaceted deep learning task. These systems are powered by Transformer Architecture-based Large Language Models (LLMs), which are first pre-trained on a large, unlabeled dataset from the internet using Self-Supervised Learning to learn the context and long-range dependencies of language.

Next comes Supervised Fine-Tuning (SFT): The model is then trained on high-quality educational datasets, which are labeled and contain high-quality data, such as expert-written problem solutions and instructional dialogues. This SFT stage constrains the model to memorize the precise language style required in academia, such as using LaTeX for mathematical expressions. The Proximal Policy Optimization (PPO) is A fundamental step in achieving teacher-like behavior, which is a key component of reinforcement learning with human feedback (RLHF). In this case, subject-matter experts and educators rank the AI’s different responses according to their quality, ethical compliance, and, importantly, pedagogical soundness. This human ranking data is instrumental in building a Reward Model that directly optimizes the core AI model with powerful algorithms, such as Direct Preference Optimization (DPO). This guarantees that the AI trains itself on being more subtle and implements critical thinking, like, maybe something that would be: “What do you think should be the next step? – rather than telling the answer right away, transforming it from a machine that knows everything to a real learning assistant.

AI is ushering in speed, personalization, and possibility into the education system. But the most important question is, are we using AI simply as an answer-dispensing machine, or are we using it to promote critical thinking and problem-solving? While many benefits can be reaped from teaching with AI in this manner, there are also several challenges. First, the approach must be ethically sound. Second, it is also essential to resist the temptation to make AI an assistant that is too good, because in this case, students can become overly dependent on it. Moreover, as helpful as teaching with AI can be, nothing can replace the love, motivation, and emotional involvement that a teacher has for their students. This means that currently, our resources are at odds with our educational destiny.