Artificial Intelligence Driven Course Generation: A Case Study Using ChatGPT

• URL: http://arxiv.org/abs/2411.01369v1
• Date: Sat, 02 Nov 2024 21:59:02 GMT
• Title: Artificial Intelligence Driven Course Generation: A Case Study Using ChatGPT
• Authors: Djaber Rouabhia,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: This study explores Artificial Intelligence use, specifically ChatGPT, in creating educational content. 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, and to create a Multimedia Databases course as a case study. The study highlights the potential of AI to revolutionize educational content creation, making it more accessible, personalized, and efficient. The course content was generated in less than one day through iterative methods, using prompts for translation, content expansion, practical examples, assignments, supplementary materials, and LaTeX formatting. Each part was verified immediately after generation to ensure accuracy. Post-generation analysis with Detectia and Turnitin showed similarity rates of 8.7% and 13%, indicating high originality. Experts and university committees reviewed and approved the course, with English university teachers praising its language quality. ChatGPT also created a well-structured and diversified exam for the module. Key findings reveal significant time efficiency, comprehensive content coverage, and high flexibility. The study underscores AI's transformative potential in education, addressing challenges related to data privacy, technology dependence, content accuracy, and algorithmic biases. The conclusions emphasize the need for collaboration between educators, policymakers, and technology developers to harness AI's benefits in education fully.

Related papers

• Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Submissions using AI Detection Tool [0.0]
  This study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv. The statistical analysis shows that Originality.ai is very accurate, with a rate of 98%.
  arXiv  Detail & Related papers  (2024-02-09T17:20:48Z)
• ChatGPT as a Software Development Bot: A Project-based Study [5.518217604591736]
  This study examines the impact of generative AI tools, specifically ChatGPT, on the software development experiences of undergraduate students. Results showed that ChatGPT significantly addresses skill gaps in software development education, enhancing efficiency, accuracy, and collaboration.
  arXiv  Detail & Related papers  (2023-10-20T16:48:19Z)
• Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution [48.86322922826514]
  This paper defines a new task of Knowledge-aware Language Model Attribution (KaLMA) First, we extend attribution source from unstructured texts to Knowledge Graph (KG), whose rich structures benefit both the attribution performance and working scenarios. Second, we propose a new Conscious Incompetence" setting considering the incomplete knowledge repository. Third, we propose a comprehensive automatic evaluation metric encompassing text quality, citation quality, and text citation alignment.
  arXiv  Detail & Related papers  (2023-10-09T11:45:59Z)
• An Empirical Study of AI Generated Text Detection Tools [0.0]
  ChatGPT has great promise, but there are serious problems that might arise from its misuse. Six different artificial intelligence (AI) text identification systems, including "GPTkit," "GPTZero," "Originality," "Sapling," "Writer," and "Zylalab," have accuracy rates between 55.29 and 97.0%.
  arXiv  Detail & Related papers  (2023-09-27T12:44:12Z)
• Experimental Evidence on Negative Impact of Generative AI on Scientific Learning Outcomes [0.0]
  Using AI for summarization significantly improved both quality and output. Individuals with a robust background in the reading topic and superior reading/writing skills benefitted the most.
  arXiv  Detail & Related papers  (2023-09-23T21:59:40Z)
• The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie [0.196629787330046]
  This review paper delves deep into the rapidly evolving landscape of digital education by contrasting the capabilities and impact of OpenAI's pioneering text generation tools like Bing Chat, Bard, Ernie. The study underscores its role in democratizing education, fostering autodidacticism, and magnifying student engagement. However, with such transformative power comes the potential for misuse, as text-generation tools can inadvertently challenge academic integrity.
  arXiv  Detail & Related papers  (2023-09-05T08:15:00Z)
• Data-centric Artificial Intelligence: A Survey [47.24049907785989]
  Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle.
  arXiv  Detail & Related papers  (2023-03-17T17:44:56Z)
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT [63.58711128819828]
  ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC) The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
  arXiv  Detail & Related papers  (2023-03-07T20:36:13Z)
• Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation [43.903582264697974]
  This paper studies the problem of how to automatically, adaptively, and dynamically learn instance-level self-supervised learning strategies for each node. We propose a novel multi-teacher knowledge distillation framework for Automated Graph Self-Supervised Learning (AGSSL) Experiments on eight datasets show that AGSSL can benefit from multiple pretext tasks, outperforming the corresponding individual tasks.
  arXiv  Detail & Related papers  (2022-10-05T08:39:13Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)
• A Survey of Knowledge-Enhanced Text Generation [81.24633231919137]
  The goal of text generation is to make machines express in human language. Various neural encoder-decoder models have been proposed to achieve the goal by learning to map input text to output text. To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.
  arXiv  Detail & Related papers  (2020-10-09T06:46:46Z)

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.