This article summarizes a discussion held by the Soki community on May 25th concerning the essence of Incremental Reading. It reflects the general sentiment of the group, prompted by a key question from a user and host of the event, Zander, though it does not necessarily represent all individual opinions expressed during the event.
The rise of AI in learning has prompted a fascinating question. Consider a learner using a custom app that automatically creates flashcards from reading highlights. The system sends these questions to a spaced repetition software (SRS) for optimized scheduling. Despite this sophisticated setup, the user might feel they are no longer truly practicing "Incremental Reading." This scenario raises a critical point: What is the essence of IR? If a highly automated workflow uses all its key components, why might it feel like something different? To find the answer, we must deconstruct the core principles of Incremental Reading.
An automated process differs from the classic IR method in one significant way: the delegation of flashcard creation. Instead of manually rereading highlights and creating Q&A flashcards, the learner delegates this task to an AI. The workflow becomes: reading, highlighting (extracting), and later, triaging the AI-generated flashcards by reviewing, editing, or deleting them. The core of the doubt lies here: Is the manual, deliberate act of creating a flashcard an essential part of the IR process? Or is it merely friction that technology can now eliminate?
To answer this, we must separate the core philosophy of IR from its tools.
1. Prioritization: Essential
Incremental Reading is a method for navigating vast amounts of information. Prioritization is the engine that drives this process. It allows a learner to decide that one topic is more important than another, or that one passage in an article is more critical than the rest. This focus on relevance is crucial for managing learning effectively, especially for adults with limited time. Extracts and highlights are simply the physical manifestation of these priority choices.
2. Active Recall: Essential
The goal of processing information is to convert it from a passive form (like text in an article) into an active, testable format that strengthens memory. Flashcards are a common tool for this, but the underlying principle is active recall. How one arrives at the active recall item, whether by manually typing a cloze deletion or by reviewing an AI-generated one, is a detail of implementation, not essence.
3. Spaced Repetition: Essential
For knowledge to be retained long-term, it must be reviewed at increasing intervals. Spaced repetition is the mechanism for this, making it a foundational component of any serious learning workflow. IR can be seen as a superset of Spaced Repetition, meaning the essence of SRS is also part of the essence of IR. While a specific algorithm like SM-18 or FSRS isn't mandatory, a system of spaced intervals based on successes and failures is non-negotiable.
The most subtle question is whether automating flashcard creation shortcuts the learning process. Manually creating a card forces a deep engagement with the material; it's an act of "sensemaking."
However, an AI-driven flow creates a new kind of engagement:
Based on this analysis, a learner using an AI-powered system is still practicing Incremental Reading in its essential form. The feeling that the process is different likely stems from equating the traditional, manual method with the core principles of the process. The "friction" of manual work, such as creating flashcards or using pen and paper, is a consequence of the available tools at the time, not a required feature of the IR concept itself. An AI-powered workflow can successfully implement the three essential pillars: Prioritization, Active Recall, and Spaced Repetition, in a more streamlined and efficient manner.
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