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Towards the Generation of Learning Objects with Generative Artificial Intelligence
Book chapter   Peer reviewed

Towards the Generation of Learning Objects with Generative Artificial Intelligence

Aiden Black, Guillermo Francia and Eman El-Sheikh
Applied Cognitive Computing and Artificial Intelligence, Vol.2251, pp.343-355
Communications in Computer and Information Science, 2251, Springer Nature Switzerland
03/30/2025
DOI: https://doi.org/10.1007/978-3-031-85628-0_25
Web of Science ID: WOS:001483820100025

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Abstract

This conference paper was published in the proceedings for CSCE 2024. This paper describes ongoing research on the use of Generative Artificial Intelligence (GenAI) in generating learning objects. Learning Objects are digital or non-digital artifacts, which can be used, re-used or referenced to augment or enhance the learning process. Examples of these are presentation slides, images, text, surveys, quizzes, and hands-on exercises. The unprecedented availability and capability of GenAI tools in recent years brings us to consider how their technical capacities and abilities can bring about effective and useful learning objects. We first explore the published literature to survey work that has been reported in the field of applied GenAI to generate learning objects. Next, we provide a review of their technical features and closely look at the distinctive features of the tools used in various GenAI models. The focus of this research is to develop a method of utilizing freely available GenAI tools to expedite the generation of learning objects and to evaluate their effectiveness. Specifically, we seek to optimize the utilization of these AI-generated learning objects for active-learning applications and learning best practices.

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