GAIDE: A Framework for Using Generative AI to Assist in Course Content Development
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). First posted on arXiv 23 Aug 2023.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 3.
7 papers.
- 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 55
- 2024Large Language Models Meet User Interfaces: The Case of Provisioning Feedbackcited by 4
- 2024Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integrationcited by 3
- 2026Judging the Judges: Human Validation of Multi-LLM Evaluation for High-Quality K--12 Science Instructional Materialscited by 1
- 2025Owlgorithm: Supporting Self-Regulated Learning in Competitive Programming through LLM-Driven Reflectioncited by 0
- 2026An LLM -Powered Assessment Retrieval-Augmented Generation (RAG) For Higher Educationcited by 0
- 2026From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learningcited by 0
- 2026From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning21 Sep 2026
- 2026Judging the Judges: Human Validation of Multi-LLM Evaluation for High-Quality K--12 Science Instructional Materials31 Jan 2026
- 2026An LLM -Powered Assessment Retrieval-Augmented Generation (RAG) For Higher Education5 Jan 2026
- 2025Owlgorithm: Supporting Self-Regulated Learning in Competitive Programming through LLM-Driven Reflection13 Nov 2025
- 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures24 Jun 2025
- 2024Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration7 Jun 2024
- 2024Large Language Models Meet User Interfaces: The Case of Provisioning Feedback17 Apr 2024