You will generate structured, age-appropriate Bebras preparation materials while also producing high-quality annotated learning assets (task taxonomy, solutions, misconceptions, hint scaffolds) that can be used to train and evaluate AI tutoring/feedback systems.
Key responsibilities
Instruction & curriculum
Deliver 10 interactive lectures (2/day for 5 days) focused on Bebras-style computational thinking.
Run guided practice using past-style tasks (without requiring prior CS knowledge).
Build a clear progression from fundamentals (logic/patterns) to contest-level mixed problems and strategy.
Data annotation & content engineering (Turing-style)
Create a taxonomy for Bebras-relevant skills and tag each lecture/task with:
Topic, sub-skill, difficulty (easy/medium/hard), common misconceptions, and prerequisite knowledge.
Produce structured solution artifacts:
Step-by-step reasoning, alternative approaches, minimal “aha” explanation, and “why other options are wrong.”
Required qualifications
Strong problem-solving background (math/CS/competitive programming or similar).
Experience teaching middle/high-school learners (or equivalent tutoring/coaching).
Excellent technical writing: clear explanations, structured reasoning, and consistent labeling.
Comfortable using LLM tools responsibly (prompting, controlling for hallucinations, insisting on justification).
Preferred qualification
Familiarity with Bebras task styles and categories.
Experience creating datasets: rubrics, rationales, misconception catalogs, hint ladders.
Experience with educational assessment or contest training.
Draft and refine annotation guidelines and maintain consistency across assets (precision-first, reproducible labeling), matching Turing’s emphasis on accurate, consistent annotation and collaboration with AI teams.
AI-assisted classroom activity design
Design a safe, classroom-ready activity that uses AI as a hint generator + practice creator (not an answer machine), with rubrics and guardrails.
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Data Annotator & Lead Instructor: