Fine-tuning for Lesson Planning

Abstract

Purpose: Although AI has been in the public eye since the last century, the increasing popularity of generative AI tools has brought about renewed interest in exploring how these technologies can enhance educational practices, including lesson planning. Therefore, this thesis aims to investigate teachers’ perceptions and use of a fine-tuned AI assistant for lesson planning. To uncover potential advantages and shortcomings of fine-tuned AI assistants for teachers, the thesis reports on a study that contrasts the use of a generalpurpose AI model with a fine-tuned version for lesson planning. Theory: This study employs the Technology Acceptance Model (TAM) and the Technological Pedagogical Content Knowledge (TPACK) framework. TAM is implemented as a framework to analyse how teachers perceive a fine-tuned AI assistant for lesson planning in terms of ease of use and perceived usefulness and how these factors influence the future integration of the tool and TPACK allows for an extensive analysis of the teachers’ evaluation of the fine-tuned AI assistants’ generated lesson plans and materials. Method: To answer the research question, the study adopted an interpretative stance while following a crossover intervention study design. The participants were tasked to create a lesson plan and worksheet with the general-purpose AI model ChatGPT-4 and the fine-tuned AI assistant for lesson planning. Subsequently, the study interviewed nine teachers regarding their perceptions and use of a fine-tuned AI assistant for lesson planning. The interview transcripts were analysed with a qualitative content analysis suggested by Kuckartz (2018). Results: Overall, the study strengthens the idea that the implementation of fine-tuned AI assistants can relieve novice teachers of their workload and scaffold the lesson preparation process for more experienced teachers by providing inspiration and differentiating content. These findings also complement those of other studies that indicate that fine-tuned LLMs outperform general-purpose LLMs in particular tasks. As the demands of lesson planning can be broken down into certain tasks such as creating detailed lesson plans and worksheets, this can inform the LLMs system prompt. Nevertheless, in order to be generally applicable to all teachers of all subjects, the level of fine-tuning will still create a leeway for hallucinated answers. Thus, critical evaluation and working iteratively with the AI-assisted tool remains of great importance.