The academic case for MindLens Lab.

MindLens Lab is the follow-up to a literature review published as “Artificial Intelligence in Emotional Intelligence Training for Autism” (Kim, 2025, Curieux Academic Journal; also on the UNESCO Learning Planet Institute Youth Fellow platform). The review evaluated four AI tools designed to help autistic users build emotional intelligence. Across very different designs — wearable, classroom platform, recognition engine, social robot — the same four limitations kept appearing: an annotation bottleneck, weak transfer to real-world settings, risk of overdependence on AI feedback, and unresolved ethical concerns around emotion data.

The natural next step felt like building a fifth tool. Several were sketched — a wearable, a classroom platform, a conversational AI tutor. Each sketch inherited the same data substrate the review had spent thirty pages critiquing. So Phase 1 of MindLens Lab shifted: instead of another tool on top of single-label-per-moment training data, build the substrate from the ground up.

Researchers and practitioners broadly accept that emotion reading is layered and contextual; people misread each other, and many viewers see more than one thing at once. But the data emotion AI trains on is still labelled one-emotion-per-moment, and the benchmarks grade against single answers. The concept and the substrate don't match — and the tools built on that substrate inherit the mismatch, no matter how sophisticated they become.

MindLens Lab Phase 1 builds the missing substrate: many readers, the same short social moments, structured carefully enough that later phases — validated study materials and adaptive tools for the people the project was designed to serve — can rest on it.

Two formal documents anchor the project. The first names what the field is missing. The second commits, in advance, to exactly what this lab is going to test about it.