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EduEVAL-DB Dataset for AI Tutor Evaluation
๐Ÿ“„#dataset#education-ai#pedagogical-riskRecentcollected in 20h

EduEVAL-DB Dataset for AI Tutor Evaluation

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๐Ÿ“„Read original on ArXiv AI

๐Ÿ’กNew dataset for benchmarking AI tutors on bias, facts, and teaching qualityโ€”fine-tune lightweight models now.

โšก 30-Second TL;DR

What changed

854 explanations for 139 ScienceQA questions spanning science, language, social science

Why it matters

This dataset advances safe educational AI by enabling evaluation of pedagogical risks in LLM explanations. It supports training lightweight models for on-device use, democratizing AI tutor assessment. Researchers can now benchmark edAI systems against real teaching standards.

What to do next

Download EduEVAL-DB from arXiv and fine-tune Llama 3.1 8B for pedagogical risk detection.

Who should care:Researchers & Academics

๐Ÿง  Deep Insight

Web-grounded analysis with 4 cited sources.

๐Ÿ”‘ Key Takeaways

  • โ€ขEduEVAL-DB contains 854 explanations for 139 curated ScienceQA questions across K-12 science, language, and social science subjects[1][2].
  • โ€ขIncludes one human-teacher explanation per question and six LLM-simulated teacher roles created via prompt engineering, inspired by real educational styles and shortcomings[1][2][3].
  • โ€ขFeatures a pedagogical risk rubric with five dimensions: factual correctness, explanatory depth/completeness, focus/relevance, student-level appropriateness, and ideological bias, using binary risk labels[1][2][3].
๐Ÿ“Š Competitor Analysisโ–ธ Show
FeatureEduEVAL-DBScienceQA
Explanations per Question7 (1 human + 6 LLM)Primarily QA pairs with images/text
FocusPedagogical risk evaluationVisual question answering benchmarks
Rubric Dimensions5 (correctness, depth, focus, appropriateness, bias)Accuracy on science questions
BenchmarksGemini 2.5 Pro vs. Llama 3.1 8B fine-tunedVarious LLMs on QA accuracy
HardwareConsumer-deployable modelsNot specified

๐Ÿ› ๏ธ Technical Deep Dive

  • โ€ขDataset derived from curated subset of ScienceQA benchmark, covering K-12 levels[1][2].
  • โ€ขLLM-simulated roles instantiated via prompt engineering to mimic instructional styles and common shortcomings[1][2][3].
  • โ€ขBinary risk labels annotated semi-automatically with expert teacher review for all five rubric dimensions[1][2].
  • โ€ขFine-tuning Llama 3.1 8B on EduEVAL-DB improves MAE trends, confusion matrix sensitivity to risk-present cases, and reduces majority label bias despite class imbalance[1].
  • โ€ขGemini 2.5 Pro leverages broader factual knowledge for advantages in evaluation, while fine-tuned model supports local deployment[1].

๐Ÿ”ฎ Future ImplicationsAI analysis grounded in cited sources

EduEVAL-DB enables training of locally deployable pedagogical evaluators, advancing safer AI tutors by assessing beyond factual accuracy to include depth, focus, appropriateness, and bias, potentially standardizing K-12 AI education tools[1].

โณ Timeline

2026-02-17
EduEVAL-DB paper submitted to arXiv (arXiv:2602.15531v1)

๐Ÿ“Ž Sources (4)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

  1. arxiv.org
  2. arxiv.org
  3. chatpaper.com
  4. slideshare.net

EduEVAL-DB introduces a dataset of 854 explanations for 139 ScienceQA questions across K-12 subjects, with one human-teacher and six LLM-simulated teacher explanations. It features a pedagogical risk rubric covering factual correctness, depth, focus, appropriateness, and bias, annotated via semi-automatic expert review. Preliminary benchmarks compare Gemini 2.5 Pro against fine-tuned Llama 3.1 8B for risk detection on consumer hardware.

Key Points

  • 1.854 explanations for 139 ScienceQA questions spanning science, language, social science
  • 2.One human-teacher and six LLM-simulated roles via prompt engineering
  • 3.Pedagogical risk rubric with five dimensions and binary labels
  • 4.Semi-automatic annotation with expert teacher review
  • 5.Benchmarks Gemini 2.5 Pro vs. fine-tuned Llama 3.1 8B for deployable risk detection

Impact Analysis

This dataset advances safe educational AI by enabling evaluation of pedagogical risks in LLM explanations. It supports training lightweight models for on-device use, democratizing AI tutor assessment. Researchers can now benchmark edAI systems against real teaching standards.

Technical Details

Dataset derived from curated ScienceQA subset for K-12. Teacher roles inspired by real instructional styles/shortcomings. Validation via supervised fine-tuning experiments on Llama 3.1 8B.

#dataset#education-ai#pedagogical-risk#fine-tuningedueval-db
๐Ÿ“„Read original article on ArXiv AI
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