Humans and AI writing lectures together

Abstract: With the recent advancements in Generative Artificial Intelligence (GenAI) technologies, particularly Large Language Models (LLMs) like GPT4, there has been a significant shift in how information can be easily accessed, generated, and utilized. This study uses these advancements to create a tool where humans and AI generate complete lectures, encompassing the entire process from structure outlining and scriptwriting to slide creation and delivery via a digital avatar.The motivation behind this study comes from the challenges faced in the educational sector, including the time-consuming nature of lecture preparation and the potentially static nature of reused lectures. By integrating LLMs and other GenAI technologies such as image, video, and speech synthesis, the proposed solution aims to provide a dynamic and adaptable workflow that may speed up the lecture creation process and keep content up-to-date. We are interested in whether such a hybrid system of human experts and AI technologies can be helpful.To answer our research question, we developed a tool that combines multiple AI technologies into one easy-to-use interface. It allows educators to generate a lecture within minutes by simply entering a topic. As LLM’s are not yet fully trustworthy. Thus, we deemed it important that the system allows the user (educator) to step in at any point and make manual changes if needed.The tool creates a lecture in four steps:1. Outline: The process begins with generating a lecture outline, where users enter the lecture topic and specify the students’ proficiency level (beginner, intermediate, advanced). This chosen proficiency level is passed to the LLM, which helps to create a lecture tuned to the student’s level. Additionally, users can offer more context by indicating the students’ Existing knowledge and pinpointing specific areas they should learn more about. The user can edit the outline by changing the titles or adding and removing sections or chapters.2. Script: Based on the outline, a script for the lecture is generated. The script generation process went through several different iterations. Initially, the whole script was generated using a single-generation process. This worked to a certain extent; however, it is only a viable approach when creating a concise lecture.3. Slides: Based on the script, complementary slides are created. Each slide contains bullet points and an image. The slides are generated through a collaborative process involving a language model, an image generation model, and Google Images. First, the language model dissects the script into smaller chunks. The model has complete control over how to split up the text. We decided to give it complete control because this is a task that language models should excel at, and we want to evaluate its performance in finding the right balance between the number of slides and detail per slide.4. Avatar: A digital avatar is created by selecting a face and voice. This avatar will present the lecture. There is the option to use any custom image the user can upload.To evaluate the usability and effectiveness of the tool, a user study was conducted with 12 experienced educators from various fields and educational levels. The study revealed that the prototype achieved a mean System Usability Scale (SUS) score of 80,42, indicating a good level of usability. It was found that the tool increased workflow efficiency, with most participants agreeing that it made lecture creation faster and more streamlined. Most participants said they would integrate this tool into their workflow, but only a few believed it would improve the quality of their lectures.Overall, this research demonstrates the practical applications of GenAI technologies in an educational context. While the prototype shows promise in increasing educators’ productivity and streamlining the lecture creation process, it also highlights the need for expert oversight to make sure the content is accurate and qualitative. Our study found that most participants would integrate AI-generated lectures into their workflow, albeit to serve as a starting point or inspiration. This indicates that GenAI cannot educate people properly, but the thesis clarifies GenAI’s current capabilities in an educational context. Further advancements in large language models will make them more reliable and helpful in creating lecture content. For now, an expert educator is still needed to craft a quality syllabus and teach the content. Future work may focus on addressing the identified limitations and further refining the tool to better meet educators' needs.

Keywords: Generative AI, Education, Hybrid System

DOI: 10.54941/ahfe1005809

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