Better than Study Mode: 3 science-backed strategies to learn with AI
ChatGPT’s Study Mode falls short pedagogically. Discover three research-backed strategies (knowledge transfer, retrieval practice, generation effect) to transform AI into a true cognitive partner.
ChatGPT’s Study Mode hasn’t lived up to expectations.
Only a month after its launch, Study Mode faded from the conversation. Not just because GPT-5 stole the spotlight or because it’s summer and students are on break, but because it hasn’t delivered on its promise to transform learning.
In this article, I’ll show why Study Mode falls short and how you can instead apply key insights from cognitive science to maximize learning with AI. It’s not about hacks or perfect prompts. It’s about intentionality and… science!
Where Study Mode falls short
At first glance, Study Mode appears to be a learning upgrade. However, in practice, it feels more like a slightly repackaged version of ChatGPT than a genuinely new approach. Here are some issues that many users have flagged:
Answers before the right time: Although it provides step-by-step guidance with Socratic questioning, it often provides explanations and solutions too early (sometimes in the first exchange despite its system prompt). This takes away the productive struggle of guessing and figuring things out on your own.
Shallow structure: It works best with homework-type problems. For concept explanations, it loops through superficial explanations that are then simply rephrased as questions. It doesn’t build toward a coherent, goal-oriented lesson.
Missed optimal challenge point: By making things too easy, it undershoots the “desirable difficulty” needed for growth, leaving the learner just outside the optimal zone of challenge. Add ChatGPT’s sycophantic habit of telling you what you want to hear and it’s easy to feel like the sessions are ineffective.
One could argue these study modes are just the start of more learner-driven LLM experiences (likely true), but for now, they’re still only a superficial layer over the usual LLM interaction. For deeper critiques, see Dr. Philippa Hardman’s test and Daniel Nest’s comparison. While imperfect, many agree Gemini’s Guided Learning mode performs better.
Real innovation in learning-focused AI requires more than simply adding a couple of token ‘study’ instructions as a system prompt.
Better than Study Mode: own your learning
There’s an uncomfortable truth here: every AI use involves a choice. Either we use AI to learn or we use AI to bypass learning. No preset mode, prompt, custom GPT or strategy will magically turn the avoidance of learning into learning, because it’s actually a choice, and it’s yours to make.
If you choose to take ownership of your learning, research shows you should also know how the AI is teaching you. Metacognition, or knowing how you learn and adapting your learning accordingly, is one of the strongest predictors of academic success. Before your next AI session, ask yourself:
What exactly do I want to learn?
What strategy will I use to get there?
How will I know if it’s working?
The first is straightforward; the next two are less obvious. Most AI advice focuses on cheating or making AI outcomes “look real”. But what about using AI well to actually learn?
Here’s a cheat sheet of three science-backed strategies that I’ve covered in previous posts and how you can leverage them into a full-fledged AI-powered lesson.
Three science-backed strategies
1. Knowledge transfer
Principle: Applying knowledge to new contexts builds flexible, adaptable understanding: the hallmark of expertise.
How can I implement that?
Examples: Ask for real-life situations where the concept applies and come up with your own: “Give me two real-life examples of how supply and demand works, then I’ll try to come up with my own.”
Analogies: Request comparisons from similar concepts in different contexts, then build your own: “Compare how electricity flows in a circuit to how water flows in pipes and prompt me to come up with my own analogy.”
Problem-based learning: Ask for a problem that can be solved by implementing the concept, then solve it. “Describe a realistic problem that can be solved using the Pythagorean theorem, then let me figure out the answer.”
Storytelling: Have the AI place the concept in a story, then become the storyteller. “Tell a short story where Newton’s first law is essential to the plot, and now I’ll write my own story using the same law. Give me feedback.”
Evidence: Transfer is hard but critical; research demonstrates that prompting learners to apply concepts to new domains improves problem-solving ability.
2. Retrieval practice
Principle: Repeatedly pulling information from memory strengthens your understanding far more than passive review.
How can I implement that?
Ask for questions, not answers: “Quiz me on the three main causes of the French Revolution”
Define how many questions and ensure focus: “Ask me 10 questions, one at a time, only proceeding when I answer each one.”
Set the difficulty and scaffolding: “Make the questions progressively harder”.
Add subject-specific requests: “Ask me to connect causes to specific historical events.”, “Ask me to explain each vocabulary item in the target language.”, “Ask me to justify which formula I used”.
Follow up before giving up: If you get stuck, ask for a hint. If the retrieval isn’t very effortful, ask to increase it. This keeps you close to the optimal challenge point.
Evidence: Practicing retrieval can boost retention by 10–20% compared to re-reading, according to research by Karpicke and Roediger.
3. Generation effect
Principle: Generating your own explanations, even imperfect ones, leads to deeper understanding and better retention.
How can I implement that?
Explain in your own words: After learning, rephrase the idea yourself and ask, “Here’s my explanation in my own words. Did I miss anything?”
Write your own questions: Think of what you’re unsure about and prompt, “Here are three questions about the French Revolution. I’ll give you my attempts at answering each of them one by one. Can you provide feedback?”
Summarize from memory: Close your notes, summarize what you remember, and ask, “This is what I know about the water cycle. Did I get it right?”
Teach the topic to the AI: Walk through the material as if teaching and ask, “If I taught you about mitosis, what mistakes or missing parts would you point out?”
Evidence: Studies show that generating content (such as summaries, analogies or examples) improves both comprehension and recall by 20-40%.
Play the AI game
Implementing knowledge transfer, retrieval practice and the generation effect will ensure your learning is maximized. However, LLMs weren’t designed for the core use of education. Here are four simple AI-specific strategies to steer your LLMs towards an outcome that radically increases your learning.
Reduce sycophantic behavior
Even though recent versions like GPT-5 have improved a lot in this regard, you still need to ensure that your LLM is giving you the friction you need. Add this explicitly to your prompt:
Don’t praise me just for asking questions or simply attempting to answer. If my answers are too simple or not developed enough, don’t praise me for them. Rather, ask me to try harder. Specifically tell me where my answers need improvement, where my arguments are weak and where I’m missing evidence. Give me direct criticism without softening your feedback.
Role prompting
By now, you've probably heard that assigning a role and personality to your LLM improves its quality of outcomes. This is because it adds consistency to the LLM's predictive text power in terms of knowledge, tone of voice, and reactivity. You can also use this technique to reduce sycophantic behavior. Here’s a sample prompt:
You are a strict history professor with extensive experience evaluating advanced student writing. You expect critical thinking, depth and originality, not simply recitation or superficial engagement. You don’t give credit for merely asking questions.
Context engineering
Providing context has a massive effect in the quality of the interaction with your LLM. Many argue that context engineering overshadows prompt engineering as a core AI skill. There are many ways to do this: from uploading PDFs or pasting an article on the topic to following this simple interaction:
Request context on the topic from me until you feel confident enough to start a study session on it with me.
Here’s an additional tip: providing links doesn’t always do the trick well, due to reliance on web fetching features. Copying and pasting the content of a website onto the chat works best.
Personalize learning
Don’t forget to tell your LLM what kind of learner you are, your background, and your knowledge level. Study modes might ask up front, but their personalization is usually limited. Don’t hesitate to remind the LLM regularly to adapt whatever reply it gives you to your level, background and current level of knowledge.
Putting it all together: A sample session
Imagine you’re studying cell biology. Here’s how a research-backed AI session might look:
Set the stage: Here’s where you want to reduce sycophancy, try role prompting, provide context and introduce yourself. Set a goal: “I want to explain the five stages of mitosis without my notes.”
Retrieval practice: A great way to start a lesson is by recalling what you know on the topic. Prompt the AI: “Ask me 5 questions on the stages of mitosis. Start simple, then make it harder. For example, ask how errors in each stage might affect the cell.”
Generation effect: After answering, write your own summary and paste it to the AI: “Here’s my explanation. Please check it and suggest improvements.”
Knowledge transfer: Now make things messy on purpose. Create a struggle that you need to overcome. Ask the AI for a scenario: “Describe a situation in which mitosis goes wrong, and ask me to explain the consequences in terms of the stages we discussed.”
Review and reflect: At the end, ask yourself, “Did I recall and apply these concepts? What was hard? What should I revisit?”
This is just a sample lesson, you can adapt it depending on your case. For instance, if you struggle with understanding, starting with knowledge transfer (analogies, examples or problems) also helps you grasp concepts fast.
Beyond the AI: integration and adaptation
Mix tools: Combine AI prompts with flashcards (e.g., Anki), handwritten notes, and practice problems for a blended learning system.
Track progress: Use a spreadsheet or journal to note what you’ve learned, what’s challenging and how you’ve adapted your approach. It’s easy to lose track inside a chat.
Space your reviews: Ask the AI, “Based on our session, what are the most important concepts for me to review?” Then, set reminders to revisit those topics at increasing intervals.
Privacy and copyright: Be mindful of the data you share with AI tools and check their policies before diving in.
It’s about ownership!
Use these strategies in one of your next study sessions. You can download the visuals in this article to keep a cheat sheet. Save and bookmark this article for future reference. Track your progress. Adapt as you go. Let me know in the comments if you’d like more sample sessions or AI strategies that are more tailored to specific subjects.
And remember: AI can be transformative, but only if you take charge. Intentional, reflective learners will gain the most. Use science-backed methods, guide the AI deliberately and make learning your choice.