AI-Relevant: NYSC Teacher & Assessor (AI annotation and evaluation tasks)
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Evaluated and annotated student written work, mirroring structured large-scale response annotation as performed in RLHF workflows. Designed and consistently applied rubric-based assessment criteria, providing calibrated, high-accuracy judgment across extensive qualitative data. Synthesized classroom observations into structured reports, translating qualitative insights into quantifiable training signals for AI models.
• Monitored and tracked student behavior and progress.
• Generated progressive analysis reports from written and observed data.
• Role required strict consistency, accuracy, and clear feedback loops.
• Used digital tools aligned with leading AI training platforms for annotation and evaluation.
Evaluated and annotated student written work, mirroring structured large-scale response annotation as performed in RLHF workflows. Designed and consistently applied rubric-based assessment criteria, providing calibrated, high-accuracy judgment across extensive qualitative data. Synthesized classroom observations into structured reports, translating qualitative insights into quantifiable training signals for AI models. • Monitored and tracked student behavior and progress. • Generated progressive analysis reports from written and observed data. • Role required strict consistency, accuracy, and clear feedback loops. • Used digital tools aligned with leading AI training platforms for annotation and evaluation.
2024 - 2025