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The opportunity this OER forecasts is not choosing between two ways of reducing learning barriers, reacting deeply to one learner’s profile, or building broadly for every learner from the start.
It is funding the first design that refuses to choose and integrates the two.
Whichever venture you decided to fund, if any, you weighted depth, reach, or proof over the other two, because none of them deliver all three at once.
That gap, not any of the three ventures you just evaluated, is this OER’s actual forecast:
A tool that calibrates to a learner’s shifting cognitive capacity while also offering multiple ways into the same content, by design, available to anyone whether or not a cognitive capacity difference explains their need
This would fill every axis assessed on the venture radar
Every axis here is a projection. But Achievement Evidence is one step further removed: it can only be earned once a tool that fills the other four axes is implemented. The same is true of independence, this design would need to prove it builds understanding that lasts, not just narrows a gap in the moment.
Chart developed in Claude by Anthropic, 2026.
Questions to Take With You
If AI tools were designed for neurodivergent learners first, would they become better for everyone?
What evidence of real learning outcomes would you need, beyond one promising study, to trust a venture’s neuroscience claims?
Whatever you decide, the gap between what’s proven and what’s promised is still open.
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