I think it would be useful for WaniKani to track answers you frequently give and request alternatives if you keep giving the same one, especially for entries that use the same synonym as other entries. I think it would help in learning the nuances in meaning between similar words, as there are often subtle differences that WK doesn’t always clearly explain.
I think a good example is the crop of 女, 女性, 少女, 女の子, 女の人, and 女子. One of “woman” or “girl” works for all of these, but in English, “girl” can be reasonably used to describe anyone from a toddler to someone in her early 20s. “Woman”, on the other hand, only covers from the late teens onward.
This can lead to confusion if you’re just plugging in “girl” or “woman” into these entries, and not learning the subtle differences between the Japanese terms. Heck, 女子 has “girl” and “woman” as acceptable answers, and one of the example sentences is 「わたしは女子ではなく、一人の女せいです。」(I am not a girl but a woman.)
I used vocabulary for this example, and I know vocabulary’s foremost purpose is to reinforce kanji rather than to actually teach vocabulary, but I also think that if you’re going to do something, it should be done as well as possible! There are also a few kanji that share synonyms – though they’re obviously much less common than vocabulary – so, the kanji learning would see some benefit, as well.
I also used meaning for this example, but it definitely applies to reading as well, since there’s a ton of kanji with multiple on’yomi readings that it would be good to learn.
I considered for a while that this might be a feature a userscript could solve, but I think it would need to have a database of what answers you’ve given, and how often. I imagine it would be a lot easier for WK to store that as it checks through the list of readings/meanings, and just ask for a different answer if (psedocode warning) 少女.meanings(2).frequency was over 70% or something. That way you’re not just saying “girl”, but learning “young lady” or “maiden” as well, which both give 少女 some of its nuance that “girl” lacks.