Would this research outline be interesting? I would like to hear your opinions.

Epistemic status: This is a speculative idea based mainly on my personal experience. I have not conducted a systematic literature review. Very brief summary When I first started using spaced repetition with AnkiMobile, I thought it was extremely tedious to manually create cards by entering English words and their Japanese translations myself. So I had AI transcribe a vocabulary book and repeatedly copied and pasted the words one by one. I thought this was much easier. A few days later, while I was doing this work, the AI suggested at the end of an interaction, “Would you like me to output this in TSV format?” That was the first time I learned that TSV existed. I then learned that by pasting a vocabulary list into a text editor in TSV format and changing the file extension to .tsv, I could import all the cards at once. This was dramatically faster than my previous method, but I still had to make the AI output a large number of words and translations directly in the chat. As the amount of content being loaded became too large, my device sometimes froze, and I still had to repeatedly create files. I continued using this method for about five days. Later, when I tried to create another deck, I wanted to insert images. However, there was a limit to the number of images that could be handled in a single AI chat interaction, and inserting images using TSV files was also difficult. That was when I first learned that Anki had a file format called APKG. By simply sending the questions and images and having the AI create the file internally and output only the file, I no longer needed to load a long TSV text for copying. I only needed to directly import the file generated by the AI. Even then, however, it was difficult to complete an entire deck in a single interaction with AI. Especially for decks containing large numbers of images, dozens of interactions and imports were necessary. Then, one day, I happened to become curious about the features of the desktop version of Anki and simply asked about them. I was told that one characteristic of the desktop version was its large number of add-ons, and that by using AnkiConnect, data could be inserted programmatically in JSON format. At that moment, I realized that the hundreds of hours I had spent creating Anki decks might have been something I could simply have delegated to an AI agent. Now, for example, if I want to check the locations of countries that appear in my geography deck, I can instruct an agent to mechanically generate world maps, mark the relevant locations, and insert those images into existing cards, allowing me to edit thousands of cards at once. I will give another example. If I ask how to improve at shogi, the AI gives answers such as: solve tsume-shogi problems, review your own games, learn joseki as structures rather than as sequences of moves, look at many high-quality game records, and verbalize your evaluation of positions. These may certainly be effective. However, could an idea such as using a spaced-repetition tool to increase one’s knowledge of joseki also be included? What I want to focus on here is something common to both examples: throughout these conversations, I consistently had the goal of studying or acting more efficiently, yet even though the AI should have possessed knowledge that could help with that goal, it mostly presented concepts that were close to the context I had already entered. AI can answer when directly asked, “What is TSV?”, “What is AnkiConnect?”, or “What is spaced repetition?” But if the person asking the question does not know that the concept exists, they cannot ask that question in the first place. For example, there are concepts that may be useful for problem solving, such as spaced repetition, the 80/20 rule, Goodhart’s law, and deliberate practice. If the person does not know that these concepts exist, is there not a considerable amount of randomness involved in whether they ever encounter them? The two examples above may still involve concepts from relatively nearby fields. However, from my personal experience, before I knew about the context of rationality, there were many situations that, looking back now, make me think, “I should have been careful about Goodhart’s law here,” “I should have considered deliberate practice here,” or “I could have used spaced repetition here.” Nevertheless, concepts related to rationality were almost never spontaneously introduced in my conversations with AI. So I would like to build, on a small scale, an AI system that, when given a question motivated by a goal such as “I want to achieve a good outcome” or “I want to accomplish something,” does not rely only on the fields already known and entered by the questioner, but instead searches across different fields for concepts that may be useful for solving the problem, and proposes those concepts either individually or in combination, and compare it experimentally with an ordinary LLM. For example, instead of simply generating a