A Model for Integrating Generative AI into Course Content Development
- URL: http://arxiv.org/abs/2308.12276v3
- Date: Wed, 3 Apr 2024 04:24:43 GMT
- Title: A Model for Integrating Generative AI into Course Content Development
- Authors: Ethan Dickey, Andres Bejarano,
- Abstract summary: "GAIDE" is a novel framework for using Generative AI (GenAI) to enhance educational content creation.
It aims to streamline content development, encourage the creation of dynamic materials, and demonstrate GenAI's utility in instructional design.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces "GAIDE: Generative AI for Instructional Development and Education," a novel framework for using Generative AI (GenAI) to enhance educational content creation. GAIDE stands out by offering a practical approach for educators to produce diverse, engaging, and academically rigorous materials. It integrates GenAI into curriculum design, easing the workload of instructors and elevating material quality. With GAIDE, we present a distinct, adaptable model that harnesses technological progress in education, marking a step towards more efficient instructional development. Motivated by the demand for innovative educational content and the rise of GenAI use among students, this research tackles the challenge of adapting and integrating technology into teaching. GAIDE aims to streamline content development, encourage the creation of dynamic materials, and demonstrate GenAI's utility in instructional design. The framework is grounded in constructivist learning theory and TPCK, emphasizing the importance of integrating technology in a manner that complements pedagogical goals and content knowledge. Our approach aids educators in crafting effective GenAI prompts and guides them through interactions with GenAI tools, both of which are critical for generating high-quality, contextually appropriate content. Initial evaluations indicate GAIDE reduces time and effort in content creation, without compromising on the breadth or depth of the content. Moreover, the use of GenAI has shown promise in deterring conventional cheating methods, suggesting a positive impact on academic integrity and student engagement.
Related papers
- Transforming Teachers' Roles and Agencies in the Era of Generative AI: Perceptions, Acceptance, Knowledge, and Practices [0.7416846035207727]
This paper explores the transformative impact of Generative Artificial Intelligence (GenAI) on teachers' roles and agencies in education.
It presents a comprehensive framework that addresses teachers' perceptions, knowledge, acceptance, and practices of GenAI.
arXiv Detail & Related papers (2024-10-03T21:59:01Z) - Shaping Integrity: Why Generative Artificial Intelligence Does Not Have to Undermine Education [0.0]
The paper argues that generative artificial intelligence (GAI) can enhance digital literacy, encourage genuine knowledge construction, and uphold ethical standards in education.
This research highlights the potential of GAI to create enriching, personalized learning environments that prepare students to navigate the complexities of the modern world ethically and effectively.
arXiv Detail & Related papers (2024-07-26T21:07:33Z) - Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
We shed light on the intersectional studies of generative AI and adaptive learning.
We argue that this union will contribute significantly to the development of the next-stage learning format in education.
arXiv Detail & Related papers (2024-02-02T23:54:51Z) - The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment [0.0]
We outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment.
The AI Assessment Scale (AIAS) empowers educators to select the appropriate level of GenAI usage in assessments.
By adopting a practical, flexible approach, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education.
arXiv Detail & Related papers (2023-12-12T09:08:36Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - Generativism: the new hybrid [0.0]
It is clear that the future of education, as all industries, is collaboration with GenAI.
This article presents an approach to designing education in collaboration with GenAI, based on digital education frameworks adapted for this new hybrid of the AI age.
arXiv Detail & Related papers (2023-09-21T20:23:58Z) - Innovating Computer Programming Pedagogy: The AI-Lab Framework for
Generative AI Adoption [0.0]
We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses.
By identifying and rectifying GenAI's errors, students enrich their learning process.
For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
arXiv Detail & Related papers (2023-08-23T17:20:37Z) - Semantic Communications for Artificial Intelligence Generated Content
(AIGC) Toward Effective Content Creation [75.73229320559996]
This paper develops a conceptual model for the integration of AIGC and SemCom.
A novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information.
The framework can adapt to different types of content generated, the required quality, and the semantic information utilized.
arXiv Detail & Related papers (2023-08-09T13:17:21Z) - Guiding AI-Generated Digital Content with Wireless Perception [69.51950037942518]
We introduce an integration of wireless perception with AI-generated content (AIGC) to improve the quality of digital content production.
The framework employs a novel multi-scale perception technology to read user's posture, which is difficult to describe accurately in words, and transmits it to the AIGC model as skeleton images.
Since the production process imposes the user's posture as a constraint on the AIGC model, it makes the generated content more aligned with the user's requirements.
arXiv Detail & Related papers (2023-03-26T04:39:03Z) - 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) - Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI)
Curriculum [58.86139968005518]
The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education.
A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum.
The co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.
arXiv Detail & Related papers (2021-01-19T11:26:19Z)
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.