User feedback is essential for the success of mobile apps, yet what users report and what developers need often diverge. Research shows that users often submit vague feedback and omit essential contextual details. This leads to incomplete reports and time-consuming clarification discussions. To overcome this challenge, we propose FeedAIde, a context-aware, interactive feedback approach that supports users during the reporting process by leveraging the reasoning capabilities of Multimodal Large Language Models. FeedAIde captures contextual information, such as the screenshot where the issue emerges, and uses it for adaptive follow-up questions to collaboratively refine a rich feedback report with the user that contains information relevant to developers. We implemented an iOS framework of FeedAIde and evaluated it using a local gym’s app with its users. Compared to the app’s simple text-field form, participants rated FeedAIde as easier and more helpful for reporting feedback. An assessment by two industry experts of the resulting 54 reports showed that FeedAIde improved the quality of both bug reports and feature requests, particularly in terms of completeness. The findings of our two-fold study demonstrate the potential of context-aware, GenAI-powered feedback reporting to enhance the experience for users and increase the information value for developers. Our results align with prior work suggesting that feedback collection is shifting from static input forms toward more interactive experiences.