Date
Publisher
arXiv
Integrating Generative AI (GenAI) into educational contexts presents a transformative potential for enhancing learning experiences. This paper introduces CourseGPT, a generative AI tool designed to support instructors and enhance the educational experiences of undergraduate students. Built on open-source Large Language Models (LLMs) from Mistral AI, CourseGPT offers continuous instructor support and regular updates to course materials, enriching the learning environment. By utilizing course-specific content, such as slide decks and supplementary readings and references, CourseGPT provides precise, dynamically generated responses to student inquiries. Unlike generic AI models, CourseGPT allows instructors to manage and control the responses, thus extending the course scope without overwhelming details. The paper demonstrates the application of CourseGPT using the CPR E 431 - Basics of Information System Security course as a pilot. This course, with its large enrollments and diverse curriculum, serves as an ideal testbed for CourseGPT. The tool aims to enhance the learning experience, accelerate feedback processes, and streamline administrative tasks. The study evaluates CourseGPT's impact on student outcomes, focusing on correctness scores, context recall, and faithfulness of responses. Results indicate that the Mixtral-8x7b model, with a higher parameter count, outperforms smaller models, achieving an 88.0% correctness score and a 66.6% faithfulness score. Additionally, feedback from former students and teaching assistants on CourseGPT's accuracy, helpfulness, and overall performance was collected. The outcomes revealed that a significant majority found CourseGPT to be highly accurate and beneficial in addressing their queries, with many praising its ability to provide timely and relevant information.
Study at a glance
- Question: How well can a course-specific generative AI tool answer undergraduate students' questions and support their learning?
- Setting and data: An undergraduate information system security course (CPR E 431); course materials, former students, and teaching assistants.
- Method: Pilot test evaluating AI response accuracy and collecting feedback from former students and teaching assistants.
Why it matters
Generic AI models lack instructor control over responses and course-specific context. Educators need tools that enhance learning experiences, accelerate feedback, and streamline administrative tasks without overwhelming students with details.
Key findings
- In an undergraduate information security course, a significant majority of former students and teaching assistants found the AI assistant highly accurate and beneficial.
- A larger language model answered course-related questions with greater accuracy and faithfulness to materials than smaller models.
- Former students and teaching assistants praised the AI tool for providing timely and relevant information in response to inquiries.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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