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Jill E. Stefaniak; Stephanie L. Moore – Online Learning, 2024
Generative AI presents significant opportunities for instructional designers to create content and personalize online learning environments. Alongside its benefits, generative AI also poses ethical considerations and potential risks, such as perpetuating biases or disrupting the learning process. Navigating these complexities requires an approach…
Descriptors: Artificial Intelligence, Inclusion, Electronic Learning, Technology Uses in Education
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Zixi Li; Chaoran Wang; Curtis J. Bonk – Online Learning, 2024
As generative AI tools are increasingly popular in today's teaching and learning process, challenges and opportunities occur at the same time. Self-directed learning has been regarded as a powerful learning ability that supports learners in informal learning contexts and its importance rises in salience when incorporating AI into learning. This…
Descriptors: Artificial Intelligence, Technology Uses in Education, Independent Study, Electronic Learning
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Bilal Khallel Younis – Online Learning, 2024
This study aims to investigate students' self-regulation skills, confidence to learn online, and perception of satisfaction and usefulness of online classes in three learning environments that integrates ChatGPT. In this study, a quasi-experiential design was used to compare three online learning environments that integrate ChatGPT (independent,…
Descriptors: Self Management, Self Esteem, Electronic Learning, Student Attitudes
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Picciano, Anthony G. – Online Learning, 2019
This article speculates on the future of higher education as online technology, specifically adaptive learning and analytics as infused by artificial intelligence software, develops and matures. Online and adaptive learning have already advanced within the academy, but the most significant changes are yet to come. These evolving technologies have…
Descriptors: Artificial Intelligence, Educational Trends, Futures (of Society), Electronic Learning
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Charlotte N. Gunawardena; Yan Chen; Nick Flor; Damien Sánchez – Online Learning, 2023
Gunawardena et al.'s (1997) Interaction Analysis Model (IAM) is one of the most frequently employed frameworks to guide the qualitative analysis of social construction of knowledge online. However, qualitative analysis is time consuming, and precludes immediate feedback to revise online courses while being delivered. To expedite analysis with a…
Descriptors: Models, Learning Processes, Knowledge Level, Online Courses