FreeLearn is a token mechanism we have worked on at Kernel since 2021. The goal is: set learning free, because student debt is iniquitous. However, educators play a critical cultural role and deserve much broader recognition (both financial and social). Can we balance both these insights?

No model can help you confront this, because the question presupposes intention, human freedom, choice, and responsibility. It is not a question of parameter optimisation. This is not to say models are unnecessary - we will present one later in this section - it is simply that it is these kind of trade-offs which epitomise token engineering. Here is a graph which hopefully illustrates how big the problem we're trying to take on really is (in the US alone):

Source: https://lex.substack.com/p/long-take-jpms-frank-175mm-m-and

The FreeLearn case study is a little more in depth than some of the others in this section due to the extended period over which we have discussed and altered the original idea. We will try to present all the various stages it has been through, and the learnings we have been gifted by each, as a way of illustrating what it means to be patient and to have a long perspective when applied to token design. Once deployed, these contracts will live on for as long as Ethereum does, which means we can afford to take our time getting them right.

If you are interested in learning what the contracts look like today and how they work, you can go directly to the technical spec.