LessonPlanner: Assisting Novice Teachers to Prepare Pedagogy-Driven Lesson Plans with Large Language Models

Abstract

Preparing a lesson plan, e.g., a detailed road map with strategies and materials for instructing a 90-minute class, is beneficial yet challenging for novice teachers. Large language models (LLMs) can ease this process by generating adaptive content for lesson plans, which would otherwise require teachers to create from scratch or search existing resources. In this work, we first conduct a formative study with six novice teachers to understand their needs for support of preparing lesson plans with LLMs. Then, we develop LessonPlanner that assists users to interactively construct lesson plans with adaptive LLM-generated content based on Gagne's nine events. Our within-subjects study (N=12) shows that compared to the baseline ChatGPT interface, LessonPlanner can significantly improve the quality of outcome lesson plans and ease users' workload in the preparation process. Our expert interviews (N=6) further demonstrate LessonPlanner's usefulness in suggesting effective teaching strategies and meaningful educational resources. We discuss concerns on and design considerations for supporting teaching activities with LLMs.

20 pages

References in corpus (8)

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities
• Co-Writing with Opinionated Language Models Affects Users' Views
• Graphologue: Exploring Large Language Model Responses with Interactive Diagrams
• "What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
• Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
• Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion
• RetAssist: Facilitating Vocabulary Learners with Generative Images in Story Retelling Practices

Cited by in corpus (2)

• PaperBridge: Crafting Research Narratives through Human-AI Co-Exploration
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