Revolutionizing Undergraduate Learning: CourseGPT and Its Generative AI Advancements

• URL: http://arxiv.org/abs/2407.18310v1
• Date: Thu, 25 Jul 2024 18:02:16 GMT
• Title: Revolutionizing Undergraduate Learning: CourseGPT and Its Generative AI Advancements
• Authors: Ahmad M. Nazar, Mohamed Y. Selim, Ashraf Gaffar, Shakil Ahmed,
• Abstract summary: 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. The paper demonstrates the application of CourseGPT using the CPR E 431 - Basics of Information System Security course as a pilot.
• Score: 1.949927790632678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Artificial Intelligence Driven Course Generation: A Case Study Using ChatGPT [0.0]
  The study aims to elaborate on using ChatGPT to create course materials. The main objective is to assess the efficiency, quality, and impact of AI-driven course generation. The study highlights the potential of AI to revolutionize educational content creation.
  arXiv  Detail & Related papers  (2024-11-02T21:59:02Z)
• Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants [175.9723801486487]
  We evaluate whether two AI assistants, GPT-3.5 and GPT-4, can adequately answer assessment questions. GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions. Our results call for revising program-level assessment design in higher education in light of advances in generative AI.
  arXiv  Detail & Related papers  (2024-08-07T12:11:49Z)
• Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers [6.161370712594005]
  We have 742 student responses ranging over 75 courses in a Computer Science department. For each course, we synthesise a summary of the course evaluations and actionable items for the instructor. Our work highlights the possibility of using generative AI to produce factual, actionable, and appropriate feedback for teachers in the classroom setting.
  arXiv  Detail & Related papers  (2024-07-01T13:29:55Z)
• CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education [1.052788652996288]
  This poster introduces CourseAssist, a novel LLM-based tutoring system tailored for computer science education. Unlike generic LLM systems, CourseAssist uses retrieval-augmented generation, user intent classification, and question decomposition to align AI responses with specific course materials and learning objectives.
  arXiv  Detail & Related papers  (2024-05-01T20:43:06Z)
• Improving the Validity of Automatically Generated Feedback via Reinforcement Learning [50.067342343957876]
  We propose a framework for feedback generation that optimize both correctness and alignment using reinforcement learning (RL) Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO)
  arXiv  Detail & Related papers  (2024-03-02T20:25:50Z)
• Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses [0.0]
  GPT models evolved from completely failing the typical programming class' assessments to confidently passing the courses with no human involvement. This study provides evidence that programming instructors need to prepare for a world in which there is an easy-to-use technology that can be utilized by learners to collect passing scores.
  arXiv  Detail & Related papers  (2023-06-15T22:12:34Z)
• UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
  The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. We introduce UKP-SQuARE as a platform for QA education. Students can run, compare, and analyze various QA models from different perspectives.
  arXiv  Detail & Related papers  (2023-05-31T11:29:04Z)
• Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
  We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
  arXiv  Detail & Related papers  (2022-11-24T13:18:27Z)
• An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder [1.329950749508442]
  This paper studies the answers to the standardized university evaluation questionnaires of foundational computer science courses which recently introduced autograding. We hypothesize how the autograder might have contributed to the significant changes in the data, such as, improved interactions between tutors and students, improved overall course quality, improved learning success, increased time spent, and reduced difficulty. The autograder technology can be validated as a teaching method to improve student satisfaction with programming courses.
  arXiv  Detail & Related papers  (2021-10-28T14:09:44Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Pre-Trained Models: Past, Present and Future [126.21572378910746]
  Large-scale pre-trained models (PTMs) have recently achieved great success and become a milestone in the field of artificial intelligence (AI) By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch.
  arXiv  Detail & Related papers  (2021-06-14T02:40:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.