Nemobot: Crafting Strategic Gaming LLM Agents for K-12 AI Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) permeates modern society and is poised for further integration across various domains. However, there exists a notable deficiency in equipping K-12 students with foundational AI understanding. This paper introduces a novel learning framework that leverages large language models (LLMs) and strategic gaming to teach K-12 students about the inner workings of AI. The framework consists of a chatbot programming and testing IDE that enables K-12 students to construct AI from scratch, engage in strategic gameplay to generate instant training data, and improve the AI heuristics with a data-driven learning mechanism. With a tiered curriculum catering to diverse proficiency levels and fostering synchronous collaboration, this framework efficiently adapts learning experiences to suit various groups of students, thereby facilitating learning at scale. Preliminary experiments validate the feasibility and vast potential of this approach, promising to revolutionize AI education in K-12 education.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it