mlsanityData to 7 Oct 2026

How Effective is GPT-4 Turbo in Generating School-Level Questions from Textbooks Based on Bloom's Revised Taxonomy?

arXiv 2406.15211 ↗First posted 21 Jun 2024cs.CL cs.AIcited by 8

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Abstract (arXiv)

We evaluate the effectiveness of GPT-4 Turbo in generating educational questions from NCERT textbooks in zero-shot mode. Our study highlights GPT-4 Turbo's ability to generate questions that require higher-order thinking skills, especially at the "understanding" level according to Bloom's Revised Taxonomy. While we find a notable consistency between questions generated by GPT-4 Turbo and those assessed by humans in terms of complexity, there are occasional differences. Our evaluation also uncovers variations in how humans and machines evaluate question quality, with a trend inversely related to Bloom's Revised Taxonomy levels. These findings suggest that while GPT-4 Turbo is a promising tool for educational question generation, its efficacy varies across different cognitive levels, indicating a need for further refinement to fully meet educational standards.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 4 of its 25 references that are in the map. First posted on arXiv 21 Jun 2024.

Builds on

4 of 25 references.

References · 4

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  2. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  3. 2024A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTcited by 19
  4. 2024Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applicationscited by 12

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Citing papers per year: 2024: 7; 2025: 1.

8 papers.