A student might finish a lesson and do well on practice questions, then struggle when asked to explain the same idea aloud. Understanding something privately and presenting it to others seem like different skills.
For people who have built or used education tools: have you found a way to help with that step? Would you use a short spoken explanation, audience questions, peer feedback, or something else to help a student practice?
I m especially curious how you would tell whether a student understands the topic better, rather than has simply memorised a polished answer.
I ve been thinking about this while working with an interview-practice platform that already runs as a web application.
The core experience works well on desktop, but the way people use the product on mobile is quite different. Interview practice can involve voice interaction, microphone access, short practice sessions, progress tracking, and returning to the platform regularly.
That makes me wonder - whether simply making the website responsive is enough for this kind of product.
At what point does it make sense to give users a dedicated mobile experience rather than continuing to optimize the browser version?
I've been spending some time using GPT-6 Astra, and i noticed something I find more interesting than usual "is the new model smarter?" discussion. I'm finding myself giving it more context before asking it to do something, rather than trying to break every task into small prompts. That got me wondering whether newer AI models are actually changing the way we work with AI, rather than simply improving the answers we get. For people who have tried Astra: Has a newer model actually changed the way you structure your prompts, workflows, or projects? I'm particularly interested in real examples rather than benchmark results.
