arxiv_cl 85% Match Research Paper Educators,Curriculum Developers,Educational Researchers,EdTech Companies 20 hours ago

How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes

large-language-models › multimodal-llms

📄 Abstract

Abstract: Question generation (QG) is a natural language processing task with an abundance of potential benefits and use cases in the educational domain. In order for this potential to be realized, QG systems must be designed and validated with pedagogical needs in mind. However, little research has assessed or designed QG approaches with the input from real teachers or students. This paper applies a large language model-based QG approach where questions are generated with learning goals derived from Bloom's taxonomy. The automatically generated questions are used in multiple experiments designed to assess how teachers use them in practice. The results demonstrate that teachers prefer to write quizzes with automatically generated questions, and that such quizzes have no loss in quality compared to handwritten versions. Further, several metrics indicate that automatically generated questions can even improve the quality of the quizzes created, showing the promise for large scale use of QG in the classroom setting.

Key Contributions

This paper introduces a method for teachers to use LLMs and Bloom's Taxonomy to create educational quizzes. It demonstrates through experiments that teachers prefer using automatically generated questions and that these quizzes maintain or even improve quality compared to handwritten ones, highlighting the potential for large-scale educational applications.

Business Value

Enables educational institutions and content creators to rapidly generate high-quality quizzes, saving time and resources while potentially improving learning outcomes. This can be integrated into learning management systems or standalone educational tools.

Paper Metadata

Innovation Type

Application of LLMs to a specific educational task with pedagogical grounding

Deployment Feasibility

High, as it leverages existing LLM technology and can be integrated into existing educational platforms or workflows.

Limitations Addressed

Lack of research on QG systems designed with pedagogical needs and teacher/student input; potential for AI-generated content to be lower quality than human-created content.

Performance Gains

No loss in quality compared to handwritten versions; potential improvement in quiz quality.

Technical Tags

question generationbloom's taxonomylarge language modelspedagogical needseducational quizzesnatural language processingteacher inputstudent inputautomatic question generationquiz quality

Research Topics

Educational AINatural Language GenerationPedagogy and AILLM ApplicationsAutomated Assessment

Methods & Architectures

Large Language Model (LLM) based Question GenerationBloom's Taxonomy IntegrationExperimental Validation with Teachers Large Language Models

Applications & Tasks

Education E-learning Automated Quiz CreationAssessing Pedagogical Effectiveness of AI-generated Content Generating educational quizzesEvaluating teacher preference for AI-generated questionsAssessing quality of AI-generated quizzes

Related Fields

Educational TechnologyNatural Language ProcessingArtificial Intelligence in EducationCognitive Science

Keywords

Large Language ModelsBloom's TaxonomyQuestion GenerationEducational QuizzesAI in EducationPedagogyNatural Language ProcessingAutomated Content CreationTeacher ToolsLearning ObjectivesAssessment DesignLLM ApplicationsEdTech

Academic Context

#Educational AI#Natural Language Generation#Pedagogy and AI#LLM Applications#Automated Assessment

Commercial Potential

Potential Products

Automated quiz generation platformAI-powered lesson planning assistantEdTech tool for curriculum design

Target Industries

EducationPublishingE-learning

Use Case Examples

Teachers creating end-of-chapter quizzesOnline course platforms generating practice questionsDeveloping standardized test preparation materials

Competitive Edge

Offers a more pedagogically grounded approach to AI-generated quizzes compared to generic text generation tools, by integrating Bloom's Taxonomy.

Market Opportunity

Large global education market, growing demand for digital learning tools.

Revenue Models

SaaS subscriptions for platformslicensing to educational institutions.

Resource Requirements

Compute Needs

Standard compute for LLM inference.

Data Requirements

Not explicitly mentioned, but likely requires access to LLM training data or fine-tuning datasets.

Deployment Constraints

Requires access to LLM APIs or local LLM deployment.

Scalability

Scalable with LLM infrastructure; can generate quizzes for any subject matter.

Regulatory Considerations

Data privacy for student information if integrated into learning platforms.

Production Readiness

Maturity Level

Research/Development

Time to Market

Short to medium, depending on integration effort.

View Full Paper Back to Papers