Many readers struggle to retain key insights from books, even when they take notes or highlight passages. This is especially frustrating for students, professionals, and lifelong learners who invest time in reading but later find they can't recall or apply what they learned. While tools like Kindle highlights help capture information, they don't actively reinforce memory, leaving valuable knowledge unused.

Turning Highlights into Lasting Knowledge

One way to address this could be an app that connects to a user's Kindle account, analyzes their book highlights, and automatically creates personalized learning tools like flashcards, quizzes, or mind maps. These materials would use spaced repetition—a technique that schedules reviews at optimal times to combat forgetting. For example, if someone highlights a passage about the "Dunning-Kruger effect," the app might generate a flashcard asking them to define it, then remind them to review it a few days later, then a week after that, and so on.

The app could benefit:

• Professionals who read to stay current in their field but lack time to organize notes
• Students using Kindle textbooks who need to retain complex concepts for exams
• Book clubs or online courses that want to help members engage more deeply with material

How It Could Work Alongside Existing Tools

Unlike general note-taking apps, this would focus specifically on transforming Kindle highlights into active learning. Current options like Readwise organize highlights but don't test recall, while tools like Anki require manual flashcard creation. By integrating directly with Kindle and automating the process, the app could reduce setup time while making review sessions more effective.

A simple starting point might be a basic version that:

1. Syncs with Kindle highlights via API
2. Generates straightforward flashcards (even without AI at first)
3. Tracks when users last reviewed each concept

Over time, features like AI-generated quizzes, progress analytics, or gamification (e.g., review streaks) could be added based on user feedback.