Tailoring Education with GenAI: A New Horizon in Lesson Planning
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Abstract (arXiv)
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
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 9 of its 39 references that are in the map. First posted on arXiv 12 Feb 2024.
Builds on
9 of 39 references.
References · 9
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- 2023A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Modelscited by 39
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- 2023Towards social generative AI for education: theory, practices and ethicscited by 5
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- 2023Legal and ethical considerations regarding the use of ChatGPT in educationcited by 2
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaborationcited by 4
- 2024New Curriculum, New Chance -- Retrieval Augmented Generation for Lesson Planning in Ugandan Secondary Schools. Prototype Quality Evaluationcited by 1
- 2025Enhancing Higher Education with Generative AI: A Multimodal Approach for Personalised Learningcited by 0
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practicescited by 0
- 2026Implicit Manipulation for Skill Selection in LLM Agents with Semantic Matchingcited by 0
- 2026Implicit Manipulation for Skill Selection in LLM Agents with Semantic Matching2 Sep 2026
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices13 Sep 2025
- 2025When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration5 Jun 2025
- 2025Enhancing Higher Education with Generative AI: A Multimodal Approach for Personalised Learning11 Feb 2025
- 2024New Curriculum, New Chance -- Retrieval Augmented Generation for Lesson Planning in Ugandan Secondary Schools. Prototype Quality Evaluation14 Aug 2024