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

Authors:

Guanzheng Chen,

Zhenhui PengAuthors Info & Claims

UIST '24: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology

Article No.: 146, Pages 1 - 20

https://doi.org/10.1145/3654777.3676390

Published: 11 October 2024 Publication History

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.

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Cited By

Collie RMartin AGasevic D(2024)Teachers’ Generative AI Self-Efficacy, Valuing, and Integration at Work: Examining Job Resources and DemandsComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100333(100333)Online publication date: Nov-2024
https://doi.org/10.1016/j.caeai.2024.100333

Index Terms

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

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cover image ACM Other conferences

UIST '24: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology

October 2024

2334 pages

ISBN:9798400706288

DOI:10.1145/3654777

Editors:
Lining Yao
University of California, Berkeley
,
Mayank Goel
Carnegie Mellon University
,
Alexandra Ion
Carnegie Mellon University
,
Pedro Lopes
University of Chicago

Copyright © 2024 ACM.

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Published: 11 October 2024

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General Projects Fund of the Natural Science Foundation of Guangdong Province in China
National Natural Science Foundation of China
Young Scientists Fund of the National Natural Science Foundation of China

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UIST '24

UIST '24: The 37th Annual ACM Symposium on User Interface Software and Technology

October 13 - 16, 2024

PA, Pittsburgh, USA

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Overall Acceptance Rate 561 of 2,567 submissions, 22%

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Cited By

Collie RMartin AGasevic D(2024)Teachers’ Generative AI Self-Efficacy, Valuing, and Integration at Work: Examining Job Resources and DemandsComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100333(100333)Online publication date: Nov-2024
https://doi.org/10.1016/j.caeai.2024.100333

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