I would provide feedback by first using the feature in real scenarios, then clearly explaining what worked, what felt confusing, and where users might get stuck. I’d give specific examples, suggest concrete improvements, and frame feedback from a user’s perspective while staying respectful and solution-oriented. I also prioritize clarity and impact so the team can quickly understand and act on the feedback.

What excites me most about AI-driven creative technologies is how they lower barriers to creation while amplifying human thinking rather than replacing it. AI can turn ideas into tangible outputs faster—whether that’s studying more effectively, visualizing concepts, or experimenting with new ways to learn—so creativity becomes more about insight and intention than technical limits. I’m especially excited by tools that adapt to how people think and learn, making creativity and productivity more personalized and accessible.

Yes — I’ve tested both apps and academic tools before, and I approach it methodically. What worked well was using the product in real situations over time, noting how features affected efficiency and learning rather than just first impressions. Clear onboarding, intuitive design, and features that saved time made the biggest impact. What didn’t work were tools with cluttered interfaces, unnecessary features, or unclear value, which made them harder to adopt consistently. I shared specific, actionable feedback so improvements could be made quickly and effectively.