AI Data Annotator / AI Training Contributor (implicit in teaching, content creation, and evaluation work)
Over 13 years, evaluated complex student responses and scientific content, skills directly transferable to AI response evaluation and rating tasks. Assessed logical, factual and scientific accuracy, emulating the structured approach needed for training and validating AI models in STEM domains. Developed and marked curriculum-aligned scientific questions and explanations that align with data preparation and annotation for AI language models. • Consistently applied defined rubrics for detailed response quality assessment. • Identified logical inconsistencies and factual errors in academic writing, similar to LLM output review. • Created structured scientific content for diverse student levels, providing training data variety. • Demonstrated high reliability managing self-directed annotation and QA tasks remotely.