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Tailoring Education with GenAI: A New Horizon in Lesson Planning

Kostas Karpouzis, Dimitris Pantazatos, Joanna Taouki, Kalliopi Meli

TL;DR

The paper addresses the need for personalized education by leveraging Generative AI to tailor lesson planning through an interactive mega-prompt. It proposes the Learning Scenario Assistant, a dialogic framework that guides educators through position prompts, interactive prompts, structured follow-ups, iterative refinement, and final human-edited outputs. A mixed-method evaluation across multiple languages and education levels assesses cross-LLM performance, with both quantitative scoring and qualitative feedback, supplemented by linguistic analysis. Findings indicate significant time savings and enhanced adaptability to diverse learners, while emphasizing ethical considerations, data privacy, and the need for broader testing, including SEN contexts and randomized controlled trials. The work offers a practical, scalable approach to AI-assisted education and sets the stage for policy guidance and future research on scalable, responsible deployment.

Abstract

The advent of Generative AI (GenAI) in education presents a transformative approach to traditional teaching methodologies, which often overlook the diverse needs of individual students. This study introduces a GenAI tool, based on advanced natural language processing, designed as a digital assistant for educators, enabling the creation of customized lesson plans. The tool utilizes an innovative feature termed 'interactive mega-prompt,' a comprehensive query system that allows educators to input detailed classroom specifics such as student demographics, learning objectives, and preferred teaching styles. This input is then processed by the GenAI to generate tailored lesson plans. To evaluate the tool's effectiveness, a comprehensive methodology incorporating both quantitative (i.e., % of time savings) and qualitative (i.e., user satisfaction) criteria was implemented, spanning various subjects and educational levels, with continuous feedback collected from educators through a structured evaluation form. Preliminary results show that educators find the GenAI-generated lesson plans effective, significantly reducing lesson planning time and enhancing the learning experience by accommodating diverse student needs. This AI-driven approach signifies a paradigm shift in education, suggesting its potential applicability in broader educational contexts, including special education needs (SEN), where individualized attention and specific learning aids are paramount

Tailoring Education with GenAI: A New Horizon in Lesson Planning

TL;DR

The paper addresses the need for personalized education by leveraging Generative AI to tailor lesson planning through an interactive mega-prompt. It proposes the Learning Scenario Assistant, a dialogic framework that guides educators through position prompts, interactive prompts, structured follow-ups, iterative refinement, and final human-edited outputs. A mixed-method evaluation across multiple languages and education levels assesses cross-LLM performance, with both quantitative scoring and qualitative feedback, supplemented by linguistic analysis. Findings indicate significant time savings and enhanced adaptability to diverse learners, while emphasizing ethical considerations, data privacy, and the need for broader testing, including SEN contexts and randomized controlled trials. The work offers a practical, scalable approach to AI-assisted education and sets the stage for policy guidance and future research on scalable, responsible deployment.

Abstract

The advent of Generative AI (GenAI) in education presents a transformative approach to traditional teaching methodologies, which often overlook the diverse needs of individual students. This study introduces a GenAI tool, based on advanced natural language processing, designed as a digital assistant for educators, enabling the creation of customized lesson plans. The tool utilizes an innovative feature termed 'interactive mega-prompt,' a comprehensive query system that allows educators to input detailed classroom specifics such as student demographics, learning objectives, and preferred teaching styles. This input is then processed by the GenAI to generate tailored lesson plans. To evaluate the tool's effectiveness, a comprehensive methodology incorporating both quantitative (i.e., % of time savings) and qualitative (i.e., user satisfaction) criteria was implemented, spanning various subjects and educational levels, with continuous feedback collected from educators through a structured evaluation form. Preliminary results show that educators find the GenAI-generated lesson plans effective, significantly reducing lesson planning time and enhancing the learning experience by accommodating diverse student needs. This AI-driven approach signifies a paradigm shift in education, suggesting its potential applicability in broader educational contexts, including special education needs (SEN), where individualized attention and specific learning aids are paramount
Paper Structure (25 sections, 6 tables)