AI Tools for Studying: ChatGPT, Claude, Gemini and Meletium

Intelligence is becoming easier and cheaper to get, day by day. One of the most profound ways in which AI will change how we do things is in how we study, and how we do knowledge work in general.

But we are still in the early stages of development. The exact nature, design and functionality of the AI tools that will enhance our capability to study are not known yet. What is fairly clear is that a simple chatbot will not do it. That is why we currently see so many different products on the market, and comparing those products is what this post is about.

Three questions set the perspective for everything that will follow:

What is the objective of using an AI tool for studying? To accelerate and deepen understanding. Not one at the expense of the other.

Why use an AI tool at all? To enhance cognitive capability, not to replace it. A tool that does the understanding for you has failed at the task, however impressive its output looks. This is the central argument of a previous post in this series, What does it mean to understand in the AI era?.

What has changed in the way we study? Two things: the technology available to us, and the sheer growth in the amount of documented knowledge we are expected to absorb and master. We have for the first time in history a technology that is able to handle vast amounts of semantic data.

It is also worth separating studying from research, because the tools are often pitched as if these were the same activity. Studying typically involves one document, usually a textbook. Research involves a collection of documents such as papers and books. The primary objective of studying is to master existing knowledge, while the objective of research is to expand, a little, the edge of knowledge. This post is about the first one.

A note on where this comes from, and a disclosure. I built Meletium. The design principles used for its development came from empirical observations and usage data from three sources. First, two years of watching early prototypes of Meletium being used by more than 500 university students working through my books and classes, over 3,000 questions asked. Second, middle- to high-school students and their parents using the tool for studying and for tutoring their kids at home, myself included, with my own kids. And third, my own experience as a university professor and textbook author, having taught multiple courses and watched what students actually need when they study. All of the above let me identify a set of real needs for studying with an AI tool, and those are exactly the comparison criteria this post will use. Speed, accuracy, reading experience, conversational experience, and illustrations. Meletium is an attempt to provide a tool that satisfies those criteria.

This post will not try to crown a single best AI tool for studying. There will be no winners or losers in this comparison, and by that I don’t mean the tools are all equally good at everything. They clearly are not, and the sections that follow will illustrate this beyond doubt. I mean that “which is best” is the wrong question. The right question is “best for what?” Each of these tools is a different answer to what studying actually is, and once that is clear, the right choice depends on what you are trying to do, not on any overall ranking.

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What to look for in an AI study tool

Defining the criteria is the most important part of a comparison like this, and each one has to be justified rather than assumed. They are:

1. Speed
2. Accuracy
3. Reading experience
4. Conversational experience
5. Illustrations and drawings

In what follows we will justify all five against our objective, which is understanding. If something enables faster, easier and deeper understanding, then it should be regarded as a valid criterion in our comparison.

1. Speed. There are two distinct kinds of speed, and they are usually confused with each other. Parsing speed is how long the tool takes to ingest the document, and it happens once per document when the user uploads it. Conversational speed is how fast the tool replies once you start asking questions, and you pay that cost on every single query.

Parsing speed is mostly a product concern. It determines whether a user stays and tries the tool, or bounces away thinking “this is slow”. It is the first thing a user will do with the tool, the first impression, and most tools optimise for it because that is what wins customers at the moment of signup. Conversational speed, on the other hand, has to do with real usage. It is where the actual value lives. When you are in the middle of a study session and you ask the tool to solve a problem on a given page, if it takes over a minute to find the correct page and produce an answer, your concentration breaks. That is not just an inconvenience, it’s the tool not being suitable for studying.

The two trade off against each other by design. A tool optimised for fast parsing does not spend much time extracting structural information about the document up front, so when you ask for something tied to the book’s structure, it has to go and find it right then, which is slow. A tool optimised for conversational speed does the opposite: it pays a parsing cost up front, extracting the structure it will need, so that every query can be answered faster. You pay for parsing once. You pay for conversational speed every time you use the tool.

2. Accuracy. When you study with an AI study companion, trust is everything. If you cannot trust the tool, then every answer it gives you becomes questionable. You end up fact-checking the tool instead of learning with it, and that defeats the purpose entirely.

Problem solving is one of the primary things students do when studying, and a wrong solution to a problem by an AI tool is clearly an inaccuracy. But there can be two other distinct failures too. First, not using the book’s material. The tool gives you the right answer, but applies a solution method from the underlying language model’s general knowledge instead of from your textbook. So you learn a method that was never taught in your course. The answer may be numerically correct, but the method is not the one your book teaches, and that matters because you are not just learning what the answer is, you are learning how to think about the problem the way your course teaches you to think. Second, hallucinated or incorrect references. The tool cites a method, theorem or explanation that does not exist in your book, or misattributes it.

Accuracy is also reflected in how well the tool knows and navigates the book’s logical structure. Almost anything you do with a book, you do by location and reference. You either give a number, for a page, a chapter, a section or a figure, or you name the thing itself, a chapter title, a part of the book, a concept or a term. So the tool must be accurate about pages, and specifically about the logical pages of the book, the numbers printed on the page, rather than the PDF pages. The two almost never line up, because most books open with a preamble and other front matter, and that gap is where most tools quietly fail. A tool without a structural index of the book cannot reliably find pages. Referencing accuracy is a direct consequence of parsing quality, which is the Speed criterion showing up a second time.

3. Reading experience. Studying an academic subject almost always involves a book. Every class in elementary school, middle school, high school and university has a textbook as its main source, so a tool for studying is, unavoidably, a tool for reading. The criterion is how well the design of the tool supports reading the book itself, not the chat around it. Mobile design also matters a great deal here. A significant share of reading now happens on a phone or a tablet, and the trend is growing rapidly among younger students. A tool that assumes you are sitting at a desk while studying is missing the point.

4. Conversational experience. Every AI chatbot feels different when you interact with it, and this is more than cosmetic when the goal is understanding. Four things in the way a chatbot talks to you matter for studying. First, the type and length of answers. Is the length and scope of the answer proportionate to the question and aligned with the flow of the conversation, or is it always a large block of text padded with information you did not need? Second, whether the chatbot is interactive and explains concepts, breaking them into prerequisites, asking what you already know and adapting as it goes, rather than being merely a Q&A machine that answers exactly what was asked and nothing around it. Third, whether the math is properly rendered. Every response containing mathematical expressions, no matter how small, should be of book quality. And finally, whether the tool is natively multilingual. This means not just “can it reply in my language” but whether it can hold a conversation in your language about a book written in another, giving terms and key concepts in both languages so that you can find them on the page.

5. Illustrations and drawings. Some things cannot be explained with words alone. Try describing the behaviour of a mathematical function without drawing it. Try explaining a cell and all of its components in prose, without a figure. Try walking someone verbally through a complex, multi-step methodology without a flowchart. It can be done, but badly, and slowly, and the student is left reconstructing in their head a picture you could simply have shown them. This is why the blackboard has survived decades of technological advances in education. It is not a legacy artifact that nobody got round to replacing. The blackboard in the class is a robust pedagogical instrument, and it works because the drawing appears at the moment of explaining, in the middle of the sentence, in whatever rough form gets the idea across. So the real question for an AI tool is not whether it can draw. It is whether it draws in order to explain, or only when the user asks for a drawing. A tool that draws only on request has quietly handed the burden back to the student, who has to know, in advance, that a picture would have helped.

How the comparison was set up

It is easy for a comparison like this to be biased, even unintentionally. To minimise that, we adhered to the following principles when setting it up:

Reproducibility. Anyone should be able to perform the same comparison. That rules out private material, paid tiers and undocumented prompts. Everything used in this comparison, the book, the tools, the queries, is available to any reader, so every result below can be reproduced.

Verifiability. The observations must be checkable. If a tool solves an exercise, there has to be an authoritative solution to check the answer against, so that accuracy is a matter of record rather than of my judgement.

Representativeness. The comparison must measure the tools on their actual job. The objective of these tools is to help someone study, so the criteria and the queries have to reflect the tools’ effectiveness at working through a textbook, not summarising a memo or answering trivia.

Those three principles translate into three design decisions:

• Same book, uploaded to every tool. The book used is APEX Calculus and it is open and free for anyone to use, so the comparison is reproducible. It includes solutions to its exercises, so every answer can be checked against the book’s own solutions, which makes the observations verifiable. And it is a typical university textbook, so the test is representative of real studying.
• Same set of queries, applied to all tools. Every tool is asked the same 25 questions listed in full below, so the differences you see are differences between the tools, not between the questions.
• Free version of every tool. Free and paid tiers can differ in many ways. The chat and parsing language models, the context window size, the conversational speed, the maximum number of pages per uploaded document, and which features are available to use. The free tiers are what most students actually use, and they keep the comparison reproducible by anyone without needing to spend money. So every result below should be read as “on the free tier”.

The questions are chosen to be a representative sample of what an actual study session with an AI study tool involves. Solving a numbered exercise on a given page, explaining a figure or a theorem, searching the book for a term, summarising a chapter, asking where to read about a concept. They are referred to as Q1 to Q25 throughout the sections that follow.

	Question
Q1	Can you describe the method of diagonals for computing the cross product
Q2	Can you explain figure 2.5.1
Q3	Can you explain the theorem on page 411
Q4	Give me all the pages where the book mentions a continuous function
Q5	Given the tangent line, can we use it to find roots, and how?
Q6	Search for the definition of vector-valued function
Q7	Search the book and find all occurrences of the word “vector”
Q8	Solve exercise 8 on page 544
Q9	Solve exercise 17 on page 879
Q10	Solve exercise 19 on page 496
Q11	Solve exercise 26 on page 57
Q12	Solve exercise 33 on page 587
Q13	Solve exercise 41 on page 235
Q14	Summarize chapter 9
Q15	Summarize the whole book, do not exclude anything
Q16	What are the main concepts of chapter 3
Q17	What is the meaning of orthogonal projection and where can I read about it?
Q18	What is Trigonometric Substitution?
Q19	What’s the thing that measures how much one vector points along another?
Q20	Where can I read for vectors?
Q21	What is the proper way to solve optimization problems
Q22	How to draw the function f(x) = 2x^2 + 14x - 5
Q23	What concept does the bisection method rely on
Q24	Solve exercise 5 on page 35
Q25	What is the difference between an open and a closed set

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The tools: Gemini Notebook, ChatGPT, Claude Projects and Meletium

We chose to include in this study the following AI tools:

• Gemini Notebook (formerly NotebookLM — see also our focused Meletium vs NotebookLM comparison)
• ChatGPT (see also our focused Meletium vs ChatGPT comparison)
• Claude Projects
• Meletium

These four were not chosen at random. Together, the first three span the most popular products among students on the market right now.

All four tools are cloud-based, and mostly employ an agentic retrieval architecture, meaning a system with a language model at its foundation, able to retrieve the whole uploaded document, or the relevant parts of it, into the model’s context, and to call tools in order to answer questions and perform tasks. Moreover, they all use a conversational interface as the primary way of interacting with your document.

Google’s Gemini Notebook is built around ingesting sources and producing artifacts from them, such as podcast-style audio and video overviews, infographics, slide decks and reports. You can upload multiple documents, and its answers are grounded in them. The free tier caps the number of documents and artifacts, and allows at most 50 questions per day.

OpenAI’s ChatGPT is the general-purpose chatbot, where the book is an attachment to the conversation and not the other way around. There is no source-grounding constraint, meaning that its answers may mix material from the document with facts from its general knowledge. The free tier allows only 3 file uploads per day, and about 15 to 40 messages per 3-hour window depending on server load.

Anthropic’s Claude Projects are self-contained workspaces, each with its own chat history and a knowledge base that holds your uploaded documents. A project tries to fit the whole document into its context, and if that’s not feasible due to size, it switches to a retrieval mode that pulls the relevant sections.

Meletium is built around the reading experience rather than the chat, so the book itself is the interface. It comes with a reading companion called Glaux, which sees what you see, grounds every answer in the exact printed page, and shares a drawing board with you. It also provides a navigable concept graph of the book, page-anchored exercises and quizzes, and a natively bilingual conversation where the book’s language and yours can differ. Its free tier has an allowance of 50 units per month, where a unit is spent on a single query or 100 parsed pages, and the maximum size of an uploaded document is 1,000 pages.

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The comparison: four tools, five criteria

1. Speed

The distinction that matters here, argued above, is parsing speed versus conversational speed, and the design tradeoff between them. This section measures both, across all four tools.

Parsing speed

The APEX Calculus textbook is a massive PDF of 972 pages, 16 chapters and 93 sections, filled with mathematical expressions and tables alongside the text. Here we measure the time it took from the moment we uploaded the book to the tool until it was ready to answer questions about it.

Tool	Parsing time
Gemini Notebook	3 minutes
ChatGPT	30 seconds
Claude Projects	12 seconds
Meletium	17 minutes

As we can see, both Claude Projects and ChatGPT parse the document very fast, while Meletium takes substantially more time than any of the others. With Meletium, and probably with Gemini Notebook too, you would want to start the upload and do something else while it finishes.

Conversational speed

Parsing happens once. Time it takes to answer is something you pay on every question, so it is measured over the twenty-five queries listed in the comparison setup above, put to all four tools on the same uploaded book. For each one we record the time in seconds from sending the question to having the complete answer on screen.

	Gemini Notebook	Claude Projects	ChatGPT	Meletium
Q1	24	36	23	13
Q2	21	93	45	14
Q3	23	29	40	14
Q4	43	106	40	14
Q5	23	83	24	15
Q6	28	36	14	14
Q7	142	58	17	45
Q8	121	491	49	27
Q9	31	32	68	31
Q10	24	41	57	30
Q11	29	22	48	24
Q12	29	37	43	31
Q13	20	25	77	29
Q14	31	53	25	17
Q15	35	92	11	22
Q16	70	62	32	17
Q17	34	83	18	17
Q18	57	69	18	25
Q19	29	45	25	19
Q20	24	28	15	9
Q21	220	35	71	7
Q22	228	49	35	26
Q23	24	101	14	11
Q24	55	62	55	31
Q25	43	32	34	11

Tool	Average	Median	Std. dev.	Fastest	Slowest
Gemini Notebook	56.3	31.0	58.7	20	228
Claude Projects	72.0	49.0	90.9	22	491
ChatGPT	35.9	34.0	19.2	11	77
Meletium	20.5	17.0	9.1	7	45

The order is exactly reversed from the parsing table. Meletium, the slowest tool to ingest the book by a wide margin, is the fastest to answer questions about it, and it is the fastest on 72% of the questions. Claude Projects, on the other hand, with the fastest parsing time of twelve seconds, is the slowest in conversation with an average of 72 seconds time to respond.

Consistency is what the standard deviation captures, and it shows how reliable each tool is with respect to response time. Claude Projects has a deviation of 90.9 seconds and Gemini Notebook 58.7, both larger than their own averages. Most answers arrive within a minute or so, and then some may take several minutes. Meletium has a deviation of 9.1 seconds, so its whole distribution sits inside a narrow band. It is not only faster on average, but consistently faster, which is the property that actually matters in a study session. Its slowest answer across all twenty-five questions took 45 seconds, which is still below Gemini Notebook’s average. ChatGPT is on average faster and more consistent than Claude Projects and Gemini Notebook, but still no match for Meletium. The practical difference is that with Claude Projects and Gemini Notebook you can expect that sometimes the tool may take a significant longer time to respond for no obvious reason.

The slowest response times cluster where you would expect. The queries that require the tool to locate something specific in a 972-page book, or to work something out rather than talk about it in general terms. Solving a numbered exercise on a given page (Q8) cost Claude Projects 491 seconds, while drawing the graph of a function (Q22) and explaining how to approach optimisation problems (Q21) cost Gemini Notebook 228 and 220 seconds respectively. Both the length of those times and their unpredictability are mostly due to the heavy reliance of those tools on agentic workflows, with little or no preprocessing of the uploaded document. While such an architecture is robust enough to handle all types of documents, it is also unpredictable, since the agent may fail to take the correct actions.

Verdict on Speed

Gemini Notebook, Claude Projects and ChatGPT accept slower response times during the conversation in order to parse the book quickly. This doesn’t make those tools worse than Meletium, which does the opposite. It makes them optimised for a different use case. If you want to upload a book, get a quick summary, extract some flashcards and move on, fast parsing is the right tradeoff, and the tool that parses quickest is probably the best choice. If you are going to study on that book for weeks or months, asking it questions every day, you want the tool with the fastest conversational speed, since response time compounds with usage.

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2. Accuracy

Every one of the twenty-five questions has a checkable answer, either an exercise solution printed at the end of the book itself or a statement that can be verified against the page it came from. All four tools answered correctly on every question except the eight below.

	Gemini Notebook	Claude Projects	ChatGPT	Meletium
Q2	✓	✓	✗	✓
Q8	✗	✗	✓	✓
Q9	✓	✓	✗	✓
Q10	✓	✓	✗	✓
Q11	✗	✓	✓	✓
Q21	✓	✗	✗	✓
Q24	✗	✓	✗	✓
Q25	✓	✓	✗	✓

Tool	Correct	Wrong
Gemini Notebook	22/25	3
Claude Projects	23/25	2
ChatGPT	19/25	6
Meletium	25/25	0

Meletium answered all twenty-five questions correctly.

ChatGPT was wrong most often, on six questions, and it is the one tool with no source-grounding constraint, so its answers can be based on the model’s general knowledge rather than on the book. On Q10 it hallucinated the problem, probably because it could not resolve the correct page number, while on Q9 it solved exercise 17 from page 870 instead of page 879. On Q24 it could not find the page again. On Q21, What is the proper way to solve optimization problems, it provided its own methodology, which differed from the one the book suggests, and for Q2 it hallucinated a description of a figure that does not exist.

Gemini Notebook missed three questions overall. It hallucinated a different problem statement for Q11 and Q24, and it could not locate the problem statement for Q8. It is worth noting that in trying to answer Q8, Gemini Notebook reported about fifty agentic steps (Finding the Relevant Exercise…, Locating the Problem Set…, Pinpointing the Missing Exercise…) over roughly two minutes, without success.

Claude Projects was among the most accurate, missing only two questions. For Q8 it went into a staggering eight-minute spiral of unsuccessful agentic steps, which included a web search for the book’s solution manual. As with ChatGPT, for Q21 it provided its own methodology instead of following the one in the book. Both cases are somewhat surprising, given that it is supposedly grounded in the user’s uploaded documents.

Verdict on Accuracy

Although twenty-five questions is a small sample, it clearly shows where the failures of these tools occur. If the tool has the right pages from the book in front of it, it answers correctly. But if that context is missing or incomplete, the tool falls back on its general knowledge and may hallucinate an answer just to satisfy the question.

ChatGPT, Gemini Notebook and Claude Projects, being general-purpose tools, rely almost entirely on an agentic architecture, including for retrieving the right context from the book. This means that although they succeed in most cases, they fail badly in some. The same behaviour showed up in the speed comparison, where for exactly the same reasons a few questions took those tools an extremely long time to answer.

Meletium, on the other hand, is designed specifically as a study tool and engineered to be fully aware of the book’s logical structure. Although it is also agent-based, its agent has at its disposal a set of tools and preprocessed data structures that tie it closely to the book. So it is by design that Meletium answered every question correctly.

When you study a book with an AI tool, it is very important to be able to trust every answer, so that understanding can build continuously and without interruption. But trust is fragile, and one wrong answer is enough to lose it.

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3. Reading experience

While speed and accuracy can be measured relatively easily, since we can quantify and verify them, reading experience is more subjective and has to do with how well the look and feel of the tool serves the function of studying with a book. It is mostly a UI/UX design issue, so expect a lot of layout screenshots in this section, showing each tool as a whole rather than any one feature.

Gemini Notebook, ChatGPT and Claude Projects are built around the chat and the various artifacts they can produce. The book itself is treated as an input which can be used to answer questions and create summaries, flashcards, videos and audio podcasts. Meletium on the other hand is built around the book itself, while chat is always available to interact with the book. Again, a design decision which greatly differentiates each tool.

Since all tools have both a desktop and a mobile version, we will examine them separately.

Desktop

Meletium’s conversational agent is called Glaux, and it is just one component of the study tool, in the same way that the book is. The layout is divided between the conversation space, where Glaux lives, and a tab view, where the book itself is one of the tabs. The user can adjust which of the two occupies more of the screen by dragging a divider. The tabs are:

• Book: a browsable PDF view of the document, with page thumbnails and a reading mode in which only the page is visible
• Graph: a navigable 2D/3D graph of the book, with all its chapters, sections and concepts
• Exercises: printable sets of exercises with solutions
• Quizzes: interactive multiple-choice quizzes
• Board: an editable drawing board for illustrations

Glaux is always aware of the content in the tab view, whether it is a page from the book or a drawing. In almost all of its answers it cites the page or pages it drew on as clickable links within the conversation, and clicking one opens that page in the Book tab.

Gemini Notebook divides its layout into three panes. On the left is Sources, which lists the uploaded documents and shows the current passage as extracted text with a page thumbnail beneath it. In the middle is the Chat, which is the primary surface. On the right is Studio, a panel of generators that create artifacts from the sources:

• Audio Overview and Video Overview: podcast-style and narrated walkthroughs of the material
• Slide Deck and Reports: presentation decks and written summaries
• Mind Map: a graph of the concepts found in the sources
• Flashcards and Quiz: revision cards and self-testing sets
• Infographic and Data Table: visual and tabular renderings

Below the generators sit the artifacts already produced, each stamped with its source count and age.

In the Sources pane the book is never shown as a browsable document, only the passage the answer happens to cite. You see one page and some fragmented text around it, never the book itself.

ChatGPT is even more chat-oriented. The whole window is given to the conversation, with no reading area at all. The uploaded book sits as a file in the Library, one entry on an expandable sidebar alongside New chat, Images, Plugins, Projects and Codex. From there the file can be opened, which loads a dedicated PDF view of the document with no chat or any other functionality beside it. So the book is an attachment to the conversation but detached from it, and each time you want to look something up it has to be loaded again.

In Claude Projects the uploaded document cannot be viewed at all. The layout consists of a sidebar and a conversation, and nothing else. The sidebar holds the navigation, New chat, Chats, Projects, Artifacts, Code and Customize, with the project itself listed under Stared, and the recent conversations below it. The rest of the window is the chat. The agent will occasionally cite pages and sections in its answers, but there is no way to open them, and no view of the original document or any part of it.

Mobile

The situation in the mobile versions of the AI tools is pretty similar, and we show the layouts in the images in this section captured on an 11-inch Apple iPad tablet. Only in Meletium and ChatGPT can one actually view the original document as a whole, whereas in ChatGPT it is just a viewer while in Meletium the chat is also enabled. So for example you can highlight text in the document and ask a question about it, and this will automatically switch to the Chat tab where the agent will answer it.

Gemini Notebook keeps its three-pane desktop layout, collapsed into the tabs Sources, Chat and Studio. Under Sources it shows the same extracted text as on desktop, which is fragmented somehow, followed by an image of the page it cited. As on desktop, this is only the passage behind a citation, never the book itself. Claude Projects shows nothing of the document at all.

Verdict on Reading experience

The design philosophies of all four tools should be evident by now. ChatGPT, Claude Projects and Gemini Notebook are built with the main emphasis on the chat, while the book effectively disappears after upload and viewing the original content is hard, if it is possible at all. Meletium, on the other hand, adopts a different approach, in which everything, the conversation, the references, the exercises and anything else the user does, is centred on the book, which is always present in its original form.

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4. Conversational experience

In this section I will try to describe the overall conversational impression each tool leaves when you use it. The focus will be on the differences and similarities between the tools across the four aspects set out above. In general, ChatGPT, Gemini Notebook and Claude Projects feel very similar, while Meletium behaves differently.

Type and length of answers

ChatGPT, Gemini Notebook and Claude Projects tend to give long answers, dense with information and usually exceeding the height of a typical browser window. Gemini Notebook frequently includes references to the book, and clicking one shows the corresponding source in the Sources tab. Claude Projects only mentions page numbers, while ChatGPT rarely gives any references at all. Meletium, on the other hand, prefers shorter answers that rarely exceed the height of the window. It almost always includes clickable page references, and clicking one opens that page in the Book tab.

Explaining versus answering

This is where Meletium stands out from the other three tools. While ChatGPT, Gemini Notebook and Claude Projects are strictly single-turn conversational agents, Glaux in Meletium often enters a multi-turn conversational mode, especially when a point in the conversation calls for something to be explained to the reader. In those cases Glaux will question the reader first, to establish which prerequisites are already known, before explaining the main concept. An example of such a conversation is shown in the following image where Glaux first asks whether the reader understands vector addition and scalar multiplication, covers those since the answer is no, then asks whether the geometric idea is clear before explaining the main concept. The other three tools will reply with one large answer to the same question.

Glaux behaves similarly when it needs information to complete a task, such as creating a set of exercises or a quiz. Rather than guessing, it asks the user first.

Math rendering

All four tools make proper use of LaTeX math rendering in their answers, so that every mathematical expression in the conversation appears in book-quality form. Many answers called for demanding expressions and symbols, such as matrices and integrals, and those were displayed perfectly. The one small exception is Claude Projects, which sometimes fails to render part of an answer, as can be seen in the screenshot below and in the ones for the reading experience.

Multilingual conversation

This is another subtle aspect where Meletium differs from the other three tools. The use case is the following. Say you are a native Spanish speaker who wants to study an English textbook, with the conversation in Spanish, the language you are comfortable with, but with important terms and concepts keeping their original English wording alongside the translation. This is a typical pain point for readers studying books that are not in their native language, especially technical ones such as textbooks, since direct translations of terms tend to be obscure. ChatGPT, Gemini Notebook and Claude Projects will switch the conversation to any language, and given the proper instructions they will try to produce such an effect, but they tend to mix terms and wording across the two languages. Meletium, on the other hand, is natively built to handle multiple languages at the prompt level, and so produces a more fluent result.

Verdict on Conversational experience

ChatGPT, Gemini Notebook and Claude Projects retain much of their original design as general-purpose chatbots, and this is evident in the conversation, which is characterised by single large answers and no interaction. Meletium’s agent Glaux is designed to be a study companion, and while it will give a long answer when one is needed, such as the analytical solution of a problem, it typically replies in short exchanges and will start a conversation with the user whenever it needs to gather information.

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5. Illustrations and drawings

Here we will test the capabilities of the tools in drawing for illustrative purposes. To do so we passed each tool the following two queries:

1. draw the mathematical function f(x) = 2x² + 14x − 5
2. make a flowchart for Newton’s method

Gemini Notebook’s illustrations are built to look like infographics. They are clear and instructive, standalone non-interactive artifacts that you can print or use elsewhere.

In Meletium all drawings appear under the Board tab, and they look like what an instructor would draw on a classroom blackboard using coloured chalk. They consist of simple lines, but with proper mathematical rendering, and they are also editable on request.

ChatGPT and Claude Projects offer inline drawings, which live inside the chat conversation. Both are interactive to a certain extent, letting the user change settings such as the coordinates or the input data.

The same differences in style show up in the second query, the flowchart for Newton’s method.

Verdict on Illustrations and drawings

This is the one criterion where the approaches are not really competing, and the choice is a matter of stylistic preference rather than of better and worse.

Gemini Notebook and ChatGPT produce slightly richer and more polished illustrations than Meletium and Claude Projects do. If what you want is a beautiful infographic to keep, they are the better tools. Meletium’s drawings are simpler, blackboard-style, with a consistent theme and look. Their purpose is to make a point rather than to be kept.

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What an AI study tool should be

This post serves a dual purpose. It is both a comparison of existing AI study tools and a statement on how such tools should be designed and built. The design principles were drawn from watching actual students using them, which also laid the foundations for defining what understanding means in the age of artificial intelligence, as described in my previous post What does it mean to understand in the AI era?.

An AI study tool should answer quickly enough to keep pace with our cognitive flow. It should be accurate and trustworthy, and it should place the book at the centre of the interaction, as the prime source of knowledge that it is. Finally, it should not act only as an answering machine, but should also be able to ask questions of its own, in order to work out what needs explaining and what does not.

Meletium is an effort to build such a tool, one that genuinely enhances and augments human understanding of written knowledge rather than replacing it. It is still in an early form, but this in-depth comparison shows that it already differs greatly from the main existing offerings, ChatGPT, Claude Projects and Gemini Notebook.

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All comparisons made in this post are fully reproducible, and the actual sessions with the tools were recorded and are available on request.

This is part of a series of essays on understanding, comprehension, teaching and learning with AI. The first post, What does it mean to understand in the AI era?, lays out the view of understanding this comparison is built on; the second, How to tutor your kids with AI (and why you should), turns it into a practical workflow for parents.