Lexplore, a Karolinska Institutet spin-off, uses AI eye-tracking to flag dyslexia risk in ~5 minutes, screening ~8,000 children since 2016. A 2026 independent study found a large effect in a small sample and flagged the algorithm's opacity.
~8,000 children
Children screened cumulatively (2016–2026)
95 %
Company-claimed accuracy
29 students
Independent validation-study sample (2026)
2.64 Cohen's d
Effect size, fixation-time difference (2026)
Details
Maturity
Established
Promoter
Lexplore AB
Period
2016–ongoing; independent US validation study published 2026
Lexplore was spun out of Sweden's Karolinska Institutet in 2016 to commercialise eye-tracking research into an AI-scored reading assessment. A child reads a short passage while a camera tracks eye fixations and regressions; a machine-learning model compares the pattern against a normed reference set to flag dyslexia and reading-difficulty risk in about five minutes, without requiring a specialist to administer it.
Results
The company reports having screened roughly 8,000 children across Sweden and the US and describes its own accuracy as 'an impressive 95%' — a company claim, not independently audited. In May 2026, researchers from the University of Central Florida and the University of Kansas published the first independent peer-reviewed validation in Frontiers in Education: studying 29 elementary students (12 with a reading disability, 17 without) at a single charter school, they found a statistically significant, very large difference in average fixation time between the two groups (t(27)=6.696, p<.001, Cohen's d≈2.64). The same study noted the sample was small and single-school, that 16 additional 'emerging readers' produced no usable eye-tracking data, and criticised the proprietary algorithm for lacking transparency about how it converts eye movements into a risk score.
Implementation
Indicative cost
Medium (€50k–€500k) — No public price disclosed; cost_band set to medium reflecting a commercial school-licensed diagnostic tool.
Time to results
Short (< 1 year) — Assessment itself takes about five minutes per student; timeline_band set to short for adoption at a new school.
Staffing & skills
No specialist required to administer the five-minute screening
Conditions for success
Camera-based eye-tracking hardware and a normed reference dataset
Independent peer-reviewed validation of the underlying signal (fixation-time difference)
Common failure modes
Proprietary algorithm criticised for lacking transparency in converting eye movements to a risk score
16 'emerging readers' in the validation study produced no usable eye-tracking data
Independent validation is small (single school, 29 students) relative to commercial scale (~8,000 screened)
Data sources
Where this practice's information was retrieved from, and when.
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