Long-form reading fosters readings skills, comprehension, critical thinking, and empathy but also demands sustained attention, making interruptions common and disruptive. Existing supports try to mitigate the disruptive effects, yet few focus on long-form content, and personalization remains limited. With advances in AI, new opportunities for adaptive, engaging support emerge. Among different types of long-form reading, this project focuses on scientific paper reading—an especially demanding context due to its dense language, complex logic, and task-driven nature.
This project addresses the question: “How can we design interactions that leverage AI to better support readers in managing interruptions during long-form reading?” Using a user-centred, Double Diamond process, I combined a diary study, co-creation workshop, and user testing to investigate user needs and prototype solutions.
The outcome is PaperCast, an AI-powered podcast tool integrated into scientific paper reading. User tests indicate that PaperCast can increase motivation and help re-engagement after interruptions. And in order to build trust, while highlighting the importance of ensuring AI outputs remain source-traceable and carefully balanced to prevent information overload.
Beyond the design itself, the project shows how AI’s uncertainty, adaptability, and generative capacity can be treated as design materials. Insights include a framework linking interruptions and reading experience from user study, strategies for constraining AI outputs, and lessons for integrating AI into design. Future work should iterate the prototype, assess effects on reading comprehension , and explore broader contexts and modalities.