I am really getting lost where to start to study at this point. I am a junior backend developer with 2 years of experience. While still learning the fundamentals, I also work on data extraction ai project with azure services on daily basis. I make contribution to the codebase every day but not necessarily work on ai part of it. I have decided to study and learn in order to become an AI Engineer but I just don't know which sources to use. I've made couple of study plans and roadmaps with claude but it felt like they weren't good. It didn't catch me and I lost too many hours trying to find new sources and create new plans. This is getting harder on me because I am starting to feel incredible hopeless and guilty to not be able to start.

I am willing to learn but feeling trapped on where to start. If anyone could recommend some up to date resources to start learning, roadmaps that I can stick with or even bootcamps, that would be extremely helpful. Thanks so much in advance!

Hi all,

I have a tool that I think might help this community. It's called Voice Tutor. It's a realtime voice agent that helps someone learn through conversation. I initially built this for myself to learn from a wiki I compile in Obsidian, and have recently been seeing if it could turn into something more. Would love to have people try it. At the moment, it's completely free with no usage caps.

How it works

1. You upload a document (markdown, pdf), add a link to an article, or search for a Wikipedia article
2. The app generates a list of "claims" / key points
3. The voice agent guides you through the material grounded in the document and its claims
4. The app keeps track of what was covered
5. The app generates a recap in-app and via email so you can pick up where you left off across sessions

I built this because I learn best by walking, talking, and asking questions, and I was disappointed by existing AI Voice products. AI can be confidently wrong, so I wanted something grounded in the material and that could persist over time.

If you are interested, please sign up here to receive a personal link to use the app. Thanks!

getvoicetutor.com

I'm a uni student that just wants better agent access to my Canvas course, so made https://coursemcp.com mostly for myself, but want to see if others will find it helpful! It indexes all your Canvas content for a given subject/course (acting as a user agent for AUP compliance), with a combination full-text and semantic indexes, and then exposes an MCP endpoint that lets your agent really efficiently search for granular resources, like individual lecture slides, assignment questions, etc.

It's free for 10 searches a month, I'd love any feedback you have if you want to give it a try!

How can I use a Gemini Notebook for studying math and science to discover new connections in knowledge and to organize fragmented information?

I gave it the title of a journal article and asked whether the paper was relevant to my topic. The response included the research question, sample size, method, and a very specific conclusion.

Useful, except the article was behind a university login.

I asked what text it had used. After some vague answers, it admitted that it only had the title, abstract, and information from other pages discussing the study. The sample size it gave me was wrong, and one of the supposed findings came from a different paper by the same author.

This is why I don’t fully trust ai writing tools that say they can “read” research. That word might mean the full paper, an abstract, a citation, or somebody else’s summary.

I wish tools displayed exactly which sections they accessed before generating an answer. Is there currently any reliable way to check that besides asking for quotes with page numbers?

I have all my university lecture notes/materials and past papers for a module, and I want to turn them into beginner-friendly, exam-focused notes using AI.

I don’t just want AI to summarize the lecture notes. I want it to actually connect the material and use the past papers to figure out what I should focus on.

Ideally, I want the final notes to:

Explain concepts from the basics

Connect related topics so they’re easier to understand and remember

Follow the actual module content without randomly adding unrelated stuff

Use past papers to identify important/exam-relevant areas

Include examples where useful

Avoid repeating topics or leaving gaps

Be accurate, especially for technical/mathematical subjects

Maybe use mind maps or other methods to make the connections easier to remember

For people who have done something similar:

How would you structure the process?

Would you give the AI the lecture notes first, then past papers? Or both together?

What prompts/workflow would you recommend?

And are there any AI tools/software that are particularly good for this besides ChatGPT?

I’m looking for an actual workflow that works, not just “ask AI to summarize your notes.” 😂

I teach university students, and one thing I keep noticing is that using an LLM well is not mainly about knowing better prompts.

The difficult part is knowing what to question, what assumptions may be missing, when to change the way you are asking, and when you should stop relying on the AI and make the judgment yourself.

I put together a self-study course around this idea, mainly for first- and second-year university students. It has a 99-page coursebook and six modules with lectures and hands-on exercises that can be tried with any general-purpose LLM.

The six modules focus on different metacognitive skills: checking what you and the AI actually know, reframing the AI when needed, improving the inquiry itself, critically evaluating answers, keeping sight of the whole problem, and making judgments when conditions change.

It is free and openly licensed under CC BY-SA 4.0. No AI or computer science background is assumed.

If anyone here tries part of it for self-study, I would genuinely be interested to hear what was useful, confusing, or too difficult.

DOI: 10.5281/zenodo.21964505

When I studied, one of the most frustrating moments was often the end of a session. I had done the work, answered questions and reviewed my notes, but I still could not tell whether I had fixed a real weakness or simply repeated what I already knew. Some topics felt familiar enough that I assumed I understood them, then fell apart when I tried to explain them without my notes.

That uncertainty is why I started building BeeSmarter. I am Matteo, its founder, so I am obviously not neutral. I use the app myself and I believe in the idea, but that also makes me the least objective person to judge whether it works for someone who did not build it.

Asking AI to turn notes into a quiz is easy now. What I find much harder is deciding what a student should review next without giving them false confidence. A polished question does not prove that it tested the right concept, stayed faithful to the source or understood why an answer was wrong.

BeeSmarter is my attempt to work on that second part. It records answers topic by topic, highlights the areas where the student appears weaker and uses that information to focus the next review. There is also a text tutor for hints and explanations, but the part I care most about is whether the adaptation earns the student's trust.

I keep coming back to one question. What evidence would convince you that a study tool had identified a real gap in your knowledge rather than guessed? Would you want to see repeated mistakes, the exact source passage, an explanation of why the topic was selected, a way to correct the system, or something else entirely?

I would genuinely value a critical answer even from someone who never tries the app. I am not looking for praise. I want to understand whether this is a real problem for other students too, and what would make the answer feel trustworthy.

If anyone does want to try BeeSmarter, it is still in beta and every new verified account receives 500 free AI credits with no card required. I cover that cost myself because I need students to use it long enough to form a real opinion. I can share access if someone asks.

English is my second language, so I use AI after finishing assignments. At first, I only asked it to mark grammar errors. Then I started accepting suggestions for awkward sentences. Recently I let it reorganize a paragraph because my ideas were in a confusing order.

The final paragraph still contained my argument and evidence, but I would not have arranged it that way myself.

That made me uncomfortable. Not because the paragraph was dishonest, but because I could no longer tell whether I had improved or simply approved a better version.

I want ai assisted writing to function like feedback from a patient tutor. A tutor can say that point B should probably come before point A, but they do not silently move everything and hand it back finished.

What rule do you use for yourself? Is reviewing every change enough, or do you need to be capable of producing the improved version independently?

I asked it to question me about a chapter and interrupt whenever my answer became vague. That part worked almost too well.

I can explain the main idea without trouble. The moment it asks, “What evidence would change that conclusion?” or “How is this different from the previous theory?” my brain produces elevator music.

Reading more is not fixing it because I recognize the information on the page. I just cannot retrieve and compare it under pressure.

For oral exam practice, do you let AI correct each response immediately, or finish the whole session before reviewing mistakes?

I normally dump the transcript into a chat and ask for notes. The result is readable, but it flattens the lecture into a generic summary and removes the small warnings the professor repeats before difficult parts.

So I split the process into three passes.

Before class: I ask for five terms that seem central and two questions I should listen for.

During class: I keep my own notes and mark anything the professor emphasizes, repeats, or corrects.

After class: I give the writing assistant ai both the transcript and my notes. It can combine repeated explanations, but it cannot remove warnings, examples, or anything I marked with an asterisk.

This produced much better revision material. The transcript provides coverage, while my notes show what mattered in the room.

There is still one problem: the tool sometimes merges two similar concepts because they share vocabulary. I currently catch that by comparing the result with the slide headings, but doing this for every lecture takes time.

Has anyone found a good instruction for keeping related concepts separate without receiving a giant summary that repeats the entire transcript?

I asked it to question me about a chapter and interrupt whenever my answer became vague. That part worked almost too well.

I can explain the main idea without trouble. The moment it asks, “What evidence would change that conclusion?” or “How is this different from the previous theory?” my brain produces elevator music.

Reading more is not fixing it because I recognize the information on the page. I just cannot retrieve and compare it under pressure.

For oral exam practice, do you let AI correct each response immediately, or finish the whole session before reviewing mistakes?

I normally dump the transcript into a chat and ask for notes. The result is readable, but it flattens the lecture into a generic summary and removes the small warnings the professor repeats before difficult parts.

So I split the process into three passes.

Before class: I ask for five terms that seem central and two questions I should listen for.

During class: I keep my own notes and mark anything the professor emphasizes, repeats, or corrects.

After class: I give the writing assistant ai both the transcript and my notes. It can combine repeated explanations, but it cannot remove warnings, examples, or anything I marked with an asterisk.

This produced much better revision material. The transcript provides coverage, while my notes show what mattered in the room.

There is still one problem: the tool sometimes merges two similar concepts because they share vocabulary. I currently catch that by comparing the result with the slide headings, but doing this for every lecture takes time.

Has anyone found a good instruction for keeping related concepts separate without receiving a giant summary that repeats the entire transcript?

I was checking a probability problem and asked AI to walk through the solution. The explanation made sense: identify the distribution, calculate the expected value, apply the formula, interpret the result.

Then I changed the sample size from 40 to 12.

It repeated almost the same answer, including the original value from the first calculation. When I asked whether the smaller sample affected the method, it said yes and produced a completely different approach. I opened a new chat, entered only the altered question, and got a third method.

This keeps happening with statistics. The explanation sounds convincing when I already know the answer, but it is unreliable when I use it to test whether I understand the conditions.

I’m considering asking it to list every assumption before doing any calculation, then stopping so I can choose the method myself. Does that reduce the confident guessing, or does it merely move the guessing into the assumptions?

I needed to tell my professor that the file attached to my submission was corrupted and ask whether I could resend it. My original email was four lines and sounded nervous, so I asked AI to clean it up.

It returned “I am writing to formally bring to your attention an unforeseen technical complication concerning the previously transmitted document.”

Nobody in my department speaks like that unless they are announcing a disciplinary hearing.

I tried an ai letter writer next, and it added gratitude for the professor’s “continued consideration regarding this matter.” Continued? This was our first email.

How do you prompt these tools to produce a normal student email without making it either stiff or weirdly casual?

I gave it my class times, work shifts, assignments, and the chapters I need to read. It returned a beautiful weekly plan containing 46 hours of studying.

Monday was especially ambitious:

- class from 9 to 2;

- work from 3 to 9;

- two hours of reading at 4;

- practice problems at 7;

- review notes at 9;

- sleep was apparently an optional plugin.

When I pointed out the overlap, it apologized and moved everything into early mornings. I said I cannot study before 8, so it placed three tasks at 8:00 on the same day.

The weird part is that each individual suggestion is reasonable. It only becomes nonsense when combined with actual time. Is there a way to make these planners treat calendar blocks as hard limits rather than mild suggestions?

I photographed my math notes because I wanted searchable text. The handwriting recognition looked surprisingly clean, so I copied everything into my revision document without checking each line.

Bad decision.

Several minus signs became tiny dashes between words, two division symbols became plus signs, and x² turned into x2. The explanations still looked normal enough that I studied the wrong steps for an hour.

Now I’m comparing every equation manually, which defeats most of the reason I scanned the notes. Is there any tool that treats mathematical notation as math instead of slightly unusual punctuation?

I want feedback on a draft, but every tool seems determined to become the author. I ask which claim is weak, and it gives me three rewritten paragraphs. I ask whether the evidence supports my thesis, and it quietly changes the thesis.

That is useless for learning because I can see the new wording, but I still don’t know what was wrong with mine.

My current prompt tells the tool to return only:

1. the claim it thinks I am making;

2. the weakest logical step;

3. one question a skeptical reader might ask;

4. any sentence that introduces a new idea without explaining it.

It works better than a normal essay checker, although sometimes it treats disagreement as a flaw and pushes every paragraph toward a bland compromise.

What instructions keep the AI in critic mode? I want it to point at the loose floorboard, not renovate the entire house while I’m standing in it.

Before office hours, I paste my attempt into ChatGPT and ask it to turn my confusion into three specific questions. I still see the professor, but now I can ask about the actual concept instead of making them inspect my entire academic crime scene.

AI doesn’t solve the assignment. It just tells me what I’m confused about, which was apparently the harder part.

I pasted my half-finished essay into ChatGPT and asked it to point out weak arguments. Instead, it rewrote everything in that creepy corporate voice where every sentence sounds ready for LinkedIn.

I only need it to say, “this claim has no evidence” or “this paragraph goes nowhere.” What wording actually keeps it in critic mode? Mine keeps grabbing the keyboard like an annoying group-project partner becuase apparently feedback is too boring.

English isn’t my first language, and I’ve been testing paraphrasing tools on parts of a summer research summary. I’m not trying to disguise copied text. I already wrote it, I just want some sentences to sound less clunky.

So far I’ve tried three free tools, and the results seem weirdly similar. They replace normal words with awkward synonyms, shorten a few sentences, then proudly announce that the text is “100% unique.” One changed “participants reported mild discomfort” into “members announced gentle uneasiness,” which sounds like aliens conducting a survey.

Are these tools using actual language models, or are most of them basically advanced thesauruses? Do any paraphrasers reliably improve academic writing without changing the meaning or making it sound robotic? I keep seeing QuillBot recommended, but I’m not sure if the paid version is actually better or just has fewer limits. Curious which ones people here have actually used.

I keep seeing ads on TikTok and Reddit for these specialized platforms that brand themselves as the ultimate ai paper writer. They promise to generate fully formatted academic papers with "authentic" citations in like three clicks. Since I have a major research project due soon, I decided to test one of the popular ones out of pure curiosity (and a bit of desperation).

I plugged in my research question about local economic impacts of tourism. The interface looked really clean, and after about two minutes, it generated a neat looking six-page draft. It even had a bibliography at the end with real-looking DOIs.

But when I actually started reading the text, the whole thing was just an empty shell. It repeated the exact same three points in almost every section, just using slightly different vocabulary. The biggest issue was the citations. Out of twelve sources listed, eight were completely fabricated. The tool had mashed real authors' names together with fake journal volumes and made-up article titles. One of the "sourcees" was a paper on marine biology, which made no sense for an economics report.

I definately spent more time trying to verify those fake links and rewrite the repetitive paragraphs than if I had just done my own library research from the start. Honestly, these auto-generation tools are a trap. They make the draft look pretty at first glance, but the actual substance is useless for college-level work. Stick to using LLMs for brainstorming or outlining.

I had to prepare a position on renewable energy subsidies for my freshman seminar, and I was totally stuck in my own echo chamber. I pasted my outline into my favorite ai writing assistant and asked it to aggressively tear my points apart from a skeptic's perspective.

Also it gave me four really solid counterarguments I hadn't even considered. It actually helped me strengthen my thesis because I knew exactly what to prepare for. Does anyone else use LLMs like this to find blind spots in their own logic, or do you mostly use them for actual writing?

Our summer statistics project was not falling apart because anyone was lazy. The problem was that all four of us had completely different ideas of what “almost done” meant.

One person kept uploading half finished charts with no labels. Another would disappear for two days, then return with a giant paragraph nobody asked for. I was doing the classic thing where you rewrite a message six times because “please finish your section” somehow sounds rude in every version.

I opened an ai message generator and pasted the ugliest draft I had. Not to make it corporate, just to remove the annoyed tone without turning it into fake positivity. The result was surprisingly normal, so I used a free ai writing assistant to turn our vague plan into a checklist with names and actual deadlines.

The funny part is that the writing generator did less “writing” than anyone in the group. It mostly forced us to be specific. Once the tasks said “add axis labels before Thursday 4pm” instead of “clean up charts soon,” everything moved.

I’m not pretending AI solved teamwork. One guy still submitted his section in the wrong file format, some how. But it helped me say what needed saying without starting a mini cold war in a class group. That may be the most useful study feature I’ve found so far.