Designing a Modular University-Based Online Course Framework for Ethical and Efficient Utilization of Generative Artificial Intelligence

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dc.contributor.authorFan, Jing
dc.contributor.authorDu, Yuxin
dc.contributor.authorSiemon, Dominik
dc.contributor.authorHapponen, Ari
dc.date.accessioned2024-12-26T21:04:15Z
dc.date.available2024-12-26T21:04:15Z
dc.date.issued2025-01-07
dc.description.abstractRapid advancements in artificial intelligence (AI) are making fast multi-industry transformational changes. To accommodate the change, educators need to equip students with the knowledge and skills for ethical, and efficient use of AI to ensure they stay competitive on a global scale. Therefore, the educational approach needs to shift from restricting students’ use of AI to empowering them with proper guidance and supervision. The study includes a quantitative survey among 93 engineering students from 4 Finnish higher education institutions to investigate students' cognitive gaps and learning interests in generative artificial intelligence based on the AI education directions widely discussed by education scholars. Then, a course framework dedicated to teaching the effective and ethical use of generative AI was designed using the modular approach. The course framework developed in this paper is flexible, allowing students to participate in learning.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.011
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other1af7ddab-d1a3-43ef-8d92-d47650ed3d71
dc.identifier.urihttps://hdl.handle.net/10125/108847
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAdvances in Teaching and Learning Technologies
dc.subjectartificial intelligence, education, ethics, teaching development, university education
dc.titleDesigning a Modular University-Based Online Course Framework for Ethical and Efficient Utilization of Generative Artificial Intelligence
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage80

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