AI for studying: how students can use it wisely

A neural network can explain a topic in a minute, turn a long lecture into notes, and run a mock exam. For students in Kazakhstan, this is especially convenient: you can ask a question in Russian or Kazakh, request an example from a familiar field, and clarify an unfamiliar term right away.

But there is an unpleasant detail. A confident tone proves nothing. A model can invent a source, mix up a formula, or give you an outdated rule. So it is better to treat AI as a smart assistant, not an automatic term-paper writer. Pressing a button is easy. Understanding the material is still your job.

Services such as QueryWise let you send one question to several models and compare the answers. That is useful wherever the cost of an error is higher than a few tenge per query: in mathematics, programming, law, medicine, and fact-checking.

Where to start: formulate a task, not a topic

“Tell me about economics” is far too vague. The model will choose the level, length, and emphasis on its own. A first-year student may end up with a lecture for a postgraduate or a collection of generic phrases.

A good prompt includes context, a goal, and an answer format. For example: “I’m a second-year student in Almaty preparing for a microeconomics seminar. Explain price elasticity of demand in simple terms, then solve a problem with a price of 1000 tenge and show every step. At the end, give me three questions for self-checking.”

This prompt sets clear boundaries. If the answer is still too difficult, add: “Explain it using an example about food delivery” or “Don’t use terms without explaining them.” If you need notes, specify the length: “Write 400 words and highlight definitions, causes, and consequences.” The more precise the request, the less time you will spend fixing the result.

Notes: shortening is not copying

AI works well with lectures, articles, and class transcripts. Upload the text or paste an excerpt and ask it to highlight key points, debatable passages, definitions, and questions for the instructor. Then reread the result next to the original.

A useful workflow looks like this: first, the model creates a rough set of notes; then it turns them into a “concept — explanation — example” table; finally, it creates flashcards for revision. The last stage is especially useful before a colloquium. But sending unedited machine-written text to an instructor is a bad idea. Important caveats may disappear, and the style can sometimes reveal that the text was artificially produced.

There is also the question of confidentiality. Do not upload classmates’ personal data, internal university documents, passwords, or unpublished research materials to a public service. For study materials, it is usually enough to remove names, student ID numbers, and other identifiers.

Explaining difficult topics: ask for a dialogue

One explanation rarely fills a knowledge gap. It is better to structure the conversation in several steps. First, ask for an intuitive explanation. Then request a formal definition, an example, and a counterexample. After that, have the model ask you a question without hints and check your answer.

Suppose you are studying inheritance in Python. Instead of “Explain OOP,” write: “I understand classes and methods, but I confuse inheritance and composition. Explain the difference using a small piece of code, find the error in my example, and give me an exercise without the solution.” This format makes you think instead of simply reading a ready-made answer.

In mathematics, ask the model to show its working without revealing the next step immediately. You can say: “Give me the first hint, wait for my answer, then check it.” That is closer to working with a teacher. If the model instantly gives you the final result, the temptation to copy it is too strong.

We added QueryWise to our workflow on launch day: we sent the same question to several models and compared the discrepancies. For explanations, this proved more useful than endlessly rephrasing a prompt for one system. Today, the leaders in the live ranking include Claude Fable 5, GPT-5.6 Sol, Kimi K3, Grok 4.5; Claude Fable 5 has a score of 9.8, but the ranking changes, so check the current values on the service page.

Preparing for an exam without pretending to prepare

A neural network can create a week-long plan if you give it the course syllabus, exam date, and available study time. A prompt might look like this: “I have five days before my organic chemistry exam and two hours each evening. Here is the list of topics. Distribute the material across the days, leave the final evening for revision, and add five problems for each topic.”

After the plan, ask for a diagnostic test. Answer on your own, then ask it to analyse your mistakes. If you got a basic concept wrong, return to it immediately. There is no point solving twenty integration problems when the difficulty arose earlier, while transforming the expression.

For an oral exam, it helps to assign the role of a strict instructor: “Ask questions one at a time, don’t give hints, assess the precision of my answer, and ask me to clarify vague wording.” This kind of practice reveals that you may recognise a topic from your notes but be unable to explain it in your own words.

QueryWise uses a pay-per-question model: a query costs 10 to 75 ₸ on average, there is no subscription, and new users get three free questions. This is convenient for quick checks and comparisons. Still, keep an eye on your budget: a long dialogue with many follow-up questions can cost more than a single consultation with a classmate.

Languages: practise mistakes instead of hiding them

For English, Kazakh, and other languages, AI can serve as both a conversation partner and an editor. Ask it to conduct a dialogue at B1 level, correct your mistakes after each message, and explain the corrections in Russian. Or choose a topic related to your field: an IT job interview, a business-plan presentation, or a conversation with a hotel guest.

Comparing translation options is also useful. Give it a sentence and ask for literal, neutral, and conversational versions, then ask it to explain the differences. With Kazakh, it is worth double-checking regional nuances, idioms, and terminology: the model may suggest an option that is grammatically acceptable but unnatural.

Do not ask it to “write an error-free essay” if your goal is to learn how to write. Prepare your own text first, then get corrections with explanations. Otherwise, your grade will look better than your actual skill, and the difference will become clear quickly in an exam.

How to check answers and follow the rules

Verification is always necessary when an answer contains figures, quotations, dates, formulas, or links. Ask the model what sources its conclusion is based on, but do not automatically treat generated bibliographies as real. Open the books, the organisation’s official website, a textbook, or a publication in a research database. Check every link manually: does the material exist, does the quotation match, and does the source actually support the claim?

Comparing several answers makes it more likely that you will spot an error. If 25 models reach the same conclusion, that is still not proof: they may all be repeating the same incorrect pattern. If the answers disagree, do not choose the most confident one. Find the primary source or ask your instructor.

• Check facts against primary sources, especially dates, statistics, and regulations.
• Recalculate formulas yourself or run the code in a separate environment.
• Ask yourself whether you can explain the answer without a screen or hints.
• Check your university’s rules on permitted AI use.

Ethics here is practical, not decorative. If your instructor has banned generative tools, you cannot use them for submitted work. If assistance is allowed at the idea or editing stage, save your drafts and note exactly where you used AI. You remain the author of the research: you are responsible for the arguments, calculations, quotations, and conclusions.

AI does not replace a doctor, lawyer, or financial adviser. A model’s study advice can also cause harm if you apply it to a real situation without checking it. The best approach is simple: a neural network speeds up understanding, asks questions, and finds gaps, while the student checks, solves, and formulates the answer independently.