Hey everyone,

I’m an online student currently gathering feedback and perspectives around student support in online learning, especially how people feel about the growing use of AI tools/services in online education. I’m using the responses for my MSc thesis.

I’d really appreciate hearing from online learners/graduates, whether your experiences have been positive, negative, mixed, or if you’re still unsure about AI in education.

Survey link:
Online Learning Support Survey

Who can participate:

• 18+
• Current or recent online learning experience
• Open worldwide

Time required: about 10 minutes

Privacy/ethics:

• Participation is completely voluntary
• You can skip any question
• You may stop anytime before submitting
• Responses are anonymous
• Data is stored securely and handled according to GDPR guidelines

Thanks in advance to anyone willing to share their experience; it will help build a better understanding of what online students actually need from support systems.

I keep coming back to this idea while building SkillNet.

In an ideal world, education could be one teacher working with one student. The teacher would understand that person, notice where they struggle and adapt the explanation, pace and practice.

In reality, a teacher may be responsible for 20 or more students. The relationship is one to many, and creating a different experience for every person is almost impossible.

Personalization has traditionally been something expensive. Many things we consider premium are made specifically for the person, like a tailored suit or a personal trainer.

I think AI could change this in education. It could help bring some parts of the one-to-one experience into a one-to-many system.

The source material and learning objective could remain the same, while the explanation, interface, practice and feedback adapt to the person.

That is the idea I’m developing through SkillNet, an open-source project:

https://skillnet.es

https://github.com/ANFAIA/SkillNet

Not replacing teachers, but giving teachers and learners something that can understand what is helping and adapt the experience around the same knowledge.

What parts of education should become more personal, and what should remain the same for everyone?

*Researchers at Utah State University's School of Computing are conducting a study on how software professionals evaluate programming activities performed with AI assistance compared with programming activities performed without AI assistance.*

*Software professionals are invited to complete a short online calibration survey. Participants will rate programming activities according to how representative they are of:*

* *Programming performed with AI assistance*
* *Programming performed without AI assistance*

*The survey takes approximately 10 minutes.*

*Participation is entirely voluntary. You may discontinue participation at any time before submitting your responses without penalty or consequence. Your decision to participate or not participate will have no effect on your grades, employment, or academic standing.*

*Survey and informed consent form:* [*https://usu.co1.qualtrics.com/jfe/form/SV\\\_dm4yjBsRrUDgcKi\*\](https://usu.co1.qualtrics.com/jfe/form/SV\_dm4yjBsRrUDgcKi)

*This study has been reviewed and approved by the Utah State University Institutional Review Board: IRB #16067.*

***Questions about the study:*** *Dr. John Edwards, Principal Investigator — john.edwards@usu.edu Rubash Mali, Student Researcher — rubash.mali@usu.edu*

*Thank you for considering participating.*

I have an idea about an application that would help students by forcing them to think critically about the problems that they solve in their daily life. the issue to be addressed here is the steps that they encounter while thinking of solving any problem, the why, when, how and how these three connect to each other then elaborating the connection through their learning. I want AI to help them sharpen this knowledge of the concept to the extent where the connection of these 3 becomes easily understandable. The AI that we would offer would ask questions of concepts that would give the student an idea of what they are trying to solve. What are your vies on this?

From August 2026, the EU AI Act introduces new transparency requirements around AI-generated content.

What I find more interesting than the regulation itself is what happens when these signals reach universities.

For text, watermark detection is unlikely to work like a binary “AI / not AI” test. The signal can be probabilistic, can weaken after editing, translation or rewriting, and human-written text may potentially trigger it as well.

So imagine a professor runs a student's paper through a detector and gets a 17% probability that a watermark is present.

What exactly should happen next?

Nothing? A conversation with the student? Additional assessment? At what probability does it become evidence of academic misconduct?

There is another problem I think is getting less attention: students may start adapting their writing to detectors.

One student I interviewed described a group assignment where they kept checking and rewriting paragraphs until eventually they were no longer writing in their natural style. The detector had effectively become another audience for the essay.

That seems like a bad direction for assessment. Students should be optimizing for reasoning, evidence and clarity, not for whether an algorithm considers their prose sufficiently “human.”

It also risks collapsing very different uses of AI into one category. There is a meaningful difference between using AI to organize material or reduce routine work and outsourcing the actual reading, synthesis and reasoning.

I’m curious how people working in higher education would approach this. Should watermark signals ever be treated as evidence on their own, or only as a reason to examine the student's actual understanding?

Full article and interview context: https://canvas-assistant.com/blog/ai-watermarks-are-coming-what-will-universities-do-with-them

I wrote an article about how generative AI may be changing the learning process itself, based partly on interviews I conducted with students and a university lecturer.

What interested me most was the contradiction in their responses.

Students described genuinely valuable uses: accessibility support, explanations when teachers or parents are unavailable, practice questions, help structuring projects, and support with unfamiliar material.

But the same students also talked about thinking less, searching less, trusting AI too easily, and sometimes allowing it to do work they felt they should have done themselves.

One observation I did not explore in the article, because it was already getting rather long, was how reluctant some students were to talk openly about their AI use. My impression was that there was a degree of shame or discomfort around it.

Two things seemed to contribute to this. One was environmental guilt, which some students mentioned explicitly. The other was harder to define: an intuitive feeling that even when AI use did not clearly qualify as cheating, something about outsourcing too much of the cognitive work still felt wrong.

A lecturer I interviewed described another pattern: stronger, more reflective students can use AI to deepen their work, while students who have not yet developed independent academic habits may use it to substitute for that process.

The article asks whether AI should be understood not simply as another educational tool, but as a medium that increasingly participates in the cognitive process itself.

I’m the author, and I’m sharing it here because I’d be interested in how educators and students here are experiencing the same shift.

Where do you think the boundary lies between AI that supports learning and AI that substitutes for it?

Article: https://canvas-assistant.com/blog/the-medium-is-the-mind-what-ai-is-doing-to-education

With another school year starting, I've been thinking about the debate around AI in education.

Students can now ask AI to summarize a chapter, explain a concept, generate an essay outline, or even create an entire study plan in seconds.

That's useful.

But there's a difference between getting a well-structured answer and building that structure in your own head.

One workflow I've found interesting is:

1. Ask AI questions and use it to explore the topic.
2. Close the chat.
3. Open a blank mind map.
4. Rebuild the topic from memory.
5. Connect the ideas that seem related.
6. Mark the parts you can't explain clearly.
7. Go back to AI, your textbook, or other sources only for those gaps.

The mind map becomes less of a note-taking tool and more of a test:

Can I actually reconstruct what I just learned?

You can do this in Mindomo, but the interesting part isn't really the software. It's forcing yourself to turn information into relationships instead of just collecting more information.

AI is incredibly good at producing answers.

I'm increasingly convinced that the valuable skill for students will be knowing how to question, organize, connect, and challenge those answers.

How are you using AI for studying without letting it think for you?

I am a teacher and have been thinking about how much harder it is to judge individual contribution when students are collaborating digitally and using AI. I have been developing an AI-assisted, educator-facing tool that looks at contribution history in shared Google Docs and gives the teacher additional evidence to work with. I would be interested to hear how others are approaching this problem. If anyone wants to test what I have built, feel free to DM me.

Hey guys,

So wanted to ask about your opinions on using AI in research and how much is unethical? I'm a master's degree student in ECE engineering.

Schools spend a lot of money adopting EdTech platforms, but getting teachers to consistently use them seems to be a completely different challenge.

Is it poor onboarding, too many features, lack of training, additional workload, a complicated UI, or simply that the product doesn't solve a real problem?

What have you seen actually cause teachers to abandon an EdTech tool?

With AI becoming so capable at writing code, what programming skills should children focus on today?

Should they still learn Scratch and Python first, or should they learn how to work with AI from the beginning?

I believe that the utopia of AI in education, is serving as a tutor and facilitator for students who are struggling with a concept or problem. This tutor can serve as an expert in that field, giving a step by step break ground of the solution, and why it's the solution.

AI has access to virtually infinite knowledge and datasets, so one would think that it would follow that this would create an automated machine, that helps those struggling with a concept be able to communicate to that machine, and receive a world-class explanation of that topic. However, there are both sort-term and long-term problem with this utopia coming into reality.

As per short-term problems, most AI models are generally designed to receive positive feedback from the end-user. This is done primarily through 2 ways. The first of which being agreeing to whatever the student has to say, without giving any opposition, and the second being having heavy confidence within their answer, even when it's incorrect. This is problematic because it risks the miseducation of individuals. In both of these ways, AI is confidently assuring the student that an incorrect solution is correct, and is giving confident assertions as per the logic as why that is the case. I have had instances where I have asked AI a question, and it so confidently gave me the incorrect answer, and later when I emailed my instructor they said the exact opposite of what the AI told me. This lead me to be really skeptical of AI's responses, and if it's reliability is poor, then that's not a tool that should be heavily utilized, or used without regulation, and that is creating a large-scale risk of hurting student's learning towards a subject. This is a short term problem, because AI can be retrained to not have this level of arrogance, and programmed to not just give out new information.

As per the long term applications, I don't believe that AI will ever have the empathy and contextualization abilities that a real, human tutor can give. I don't believe that AI will ever be able to truly understand the nuance of one's understanding, the same way an individual can, as it lacks empathy and context. AI will never be able to grasp the misconception and student's difficulty understanding a topic, and give a personalized response and feedback. It will instead give universal and generic explanations, and not be able to accommodate for the student. AI will never be ablet to replace the empathy that a real person can bring.

Overall, I don't really believe in the potential of AI in education in the future, and I don't believe anything will ever be able to replace human tutoring and educating.

Hello everyone. So, I am an assistant at a university and this year we plan to open a new lecture about the fundamentals of Artificial Intelligence. We plan to make an interactive lecture, like students will prepare their projects and such. The scope of this lecture will be from the early ages of AI starting from perceptron, to image recognition and classification algorithms, to the latest LLMs and such. Students that will take this class are from 2nd grade of Bachelor’s degree. What projects can we give to them? Consider that their computers might not be the best, so it should not be heavily dependent on real time computational power. 

Also, I’m thinking about a lecture on “how to use AI properly”. Like, it blows my mind how terrible some students use AI to write code. Antigravity is free for them, and surely they will be using some kind of AI tool to write code either way. I’m using Claude Code for like a year now, and spending at least one hour to write the first prompt to start working everyday. Yet, students usually give the exact text of the homework as prompt. What would you people recommend me to check out and refer to students as tutorials on how to use AI tools for beginners? 

I learned programming before AI and thought myself how to use AI. The tutorials I watched on Claude Code and stuff were basically tips and tricks for me. So I’m not sure how I can teach what I do to students without making it look like witchcraft, which it isn’t really. 

For AI homeworks, My first idea was to use the VRX simulation environment and the Perception task of it. Which basically sets a clear roadline to collect dataset, label them, train the model and such. Any other homework ideas related to AI is much appreciated. 

Not gonna lie, I catch myself doing this way too more, stuck on a problem, tired, exam pressure and instead of actually sitting with it, I just paste it into ChatGPT or Claude and move on. Feels productive in the moment but I have this nagging feeling I'm not actually learning it, just getting through it. Curious if this is just me or if this is basically normal for everyone's now.