# FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions

arXiv: https://arxiv.org/abs/2603.04244 · HTML: https://mlsanity.com/paper/2603.04244/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 60 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Unveiling Security, Privacy, and Ethical Concerns of ChatGPT](https://mlsanity.com/paper/2307.14192/) (2023)
- [Assessing the Quality of the Steps to Reproduce in Bug Reports](https://mlsanity.com/paper/1906.07107/) (2019)
- [What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys](https://mlsanity.com/paper/2205.10977/) (2022)
- [Release early, release often, and watch your users' emotions](https://mlsanity.com/paper/1906.06403/) (2019)
- [On the Automated Processing of User Feedback](https://mlsanity.com/paper/2407.15519/) (2024)
- [Does GenAI Make Usability Testing Obsolete?](https://mlsanity.com/paper/2411.00634/) (2024)
- [Requirements Elicitation Follow-Up Question Generation](https://mlsanity.com/paper/2507.02858/) (2025)
- [Extracting and Analyzing Context Information in User-Support Conversations on Twitter](https://mlsanity.com/paper/1907.13395/) (2019)
- [App Store 2.0: From Crowd Information to Actionable Feedback in Mobile Ecosystems](https://mlsanity.com/paper/1807.00518/) (2018)

## Abstract (arXiv)

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 with the user a rich feedback report that contains information relevant to developers. We implemented an iOS framework of FeedAIde and evaluated it on a gym's app with its users. Compared to the app's simple feedback 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 study demonstrate the potential of context-aware, GenAI-powered feedback reporting to enhance the experience for users and increase the information value for developers.