Colombia · Chía · See the Colombia profile
Evidence: Descriptive / self-reported Top 89% 27/100 · Ask Evidence Copilot about this practice
A team from Universidad de La Sabana taught a NAO humanoid robot to teach and check basic Colombian Sign Language vocabulary, hitting 91.6-93.8% sign-recognition accuracy in lab tests — a promising prototype the authors admit has not yet been tried with deaf students.
A team from Universidad de La Sabana's CAPSAB control-and-automation research group in Chia, Colombia, programmed a NAO H25 humanoid robot to teach and check basic Colombian Sign Language (CSL) vocabulary, starting with 11 colour signs, in response to a shortage of qualified CSL teachers - fewer than 0.2% of Colombian educational institutions offer bilingual instruction for deaf pupils.
Build and technically validate an AI-driven sign-language tutoring system before any classroom or learner testing.
The system extracts hand and torso coordinates from video using MediaPipe and classifies signs with a recurrent (LSTM) neural network trained on 20 signers, tested via either a webcam or the robot's own camera communicating over TCP/IP.
In technical testing, the system reached an F1 score of 91.6% (webcam) and 93.8% (NAO camera), with response times of 85 milliseconds and 2.3 seconds respectively; the sign for 'purple' was recognised with 100% accuracy.
This is early-stage, technology-readiness evidence rather than classroom evidence: the paper reports sign-recognition performance only, and the authors state plainly that the system has not yet been tested with deaf learners - an educational-effectiveness trial is still needed before any claim of learning impact would be warranted.
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