Better tools for people who find emotion hard to read.

I started this project because reading emotion is harder than the tools that claim to help with it usually admit — and the people most affected by that gap are often the ones already finding emotion hard to read in the first place. Autistic learners. Anyone for whom social cues don't land as cleanly as the textbooks describe. The original intent was simple: build something that helps.

I wasn't sure yet what that something would be. A wearable, an app, a classroom tool. The shape would come later. The point was the people.

Before building anything, I wanted to read what already existed. I spent most of a year on a systematic literature review of AI tools designed to help autistic users build emotional intelligence. The result was published in 2025 in the Curieux Academic Journal.

Four tools across very different designs — a wearable, a classroom platform, a recognition engine, a social robot — kept running into the same four problems. The annotation bottleneck. Weak transfer to the real world. Overdependence on AI feedback. Unresolved ethics around emotion data. The recommendation: AI should complement human-mediated learning, not replace it.

The obvious next step was a fifth tool. I started sketching: a wearable bracelet that would pick up physiological signals and translate them into emotional state — I called it Emo-Sense in my notes. A classroom app for structured exercises. A conversational AI tutor.

I wasn't pretending to be done with the paper's question yet. I was trying to take the next swing at it. The sketches got more elaborate. I started writing them up like product specs.

A few weeks into the spec, I noticed something I couldn't unsee. Every sketch I drew was going to inherit the exact same data substrate my own review had spent thirty pages critiquing. The annotation bottleneck. The transferability gap. The single-label assumption. None of those go away when you add a new interface on top — they get inherited.

I'd been about to build the fifth tool on the same broken foundation as the previous four.

So the project changed shape. Not abandoned — restructured. Instead of building a single tool right now, I committed to a multi-year project that begins by building the substrate the field is missing. Plural human readings of the same moments, captured carefully enough that the tools that eventually come out of this work can rest on something solid.

That's what MindLens Lab is. The wearable can come later, when there's a foundation under it. For now: the data.

Phase 1 of MindLens Lab is open right now. Participants watch short curated social moments, share how they read them, and see how others read the same ones. The plural readings become the dataset. The methodology is pre-registered — what we're testing and how, committed in advance.

The dataset is small at the moment. That's honest, not a failure. The project is multi-year by design, and Phase 1 grows one reader at a time.

Phase 2 uses the dataset to design validated learning materials — co-designed with educators who actually work with neurodiverse learners. Phase 3 is when the original intent comes back into focus: adaptive tools, possibly including the wearable I started sketching back in Phase 0, but grounded in real plural-reading data instead of assumed agreement.

The arc bends back to the original question: helping the people the project was designed to serve. Just by a longer, more honest route than I first imagined.