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

TL;DR

FeedAIde, a context-aware, interactive feedback approach that supports users during the reporting process by leveraging the reasoning capabilities of Multimodal Large Language Models, demonstrates the potential of context-aware, GenAI-powered feedback reporting to enhance the experience for users and increase the information value for developers.

Abstract

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.

Figures (4)

• Figure 1: Overview of $FeedAIde$. When a user triggers the feedback process, $FeedAIde$ collects contextual data, such as a screenshot (1). $FeedAIde$ then predicts possible feedback, such as a bug report, from which the user can choose or enter their own (2). Next, $FeedAIde$ generates $i$ context-aware follow-up questions (3), asks the user for answers to gather additional details (4), combines all information into a rich feedback report (5), and sends it to the developers (6).
• Figure 2: $FeedAIde$ iOS framework guiding users through a context-aware feedback flow. In this example, a user reports that their daily streak in a language learning app has unexpectedly reset to one (Screen 1). Once triggered, e.g. via shake-to-report, $FeedAIde$ detects the context and proposes possible feedback (Screen 2). After the user selects "My daily streak suddenly reset", the system asks adaptive follow-up questions to collect valuable information for the developers (Screens 3–4). It then creates a rich feedback report and confirms its submission (Screen 5).
• Figure 3: UML class diagram of the feedback report generated with $FeedAIde$.
• Figure 4: Existing feedback mechanism in the PPEmployee app, where users can submit feedback via the FAQ section by tapping the light bulb icon.