Wang, Z. orcid.org/0000-0003-1522-2071, Ma, Q., Somjit, N. orcid.org/0000-0003-1981-2618 et al. (2 more authors) (2023) Prospects for Artificial Intelligence and Learning Analytics in Engineering Higher Education. In: 2023 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C). 2023 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), 24-25 Aug 2023, Bangkok, Thailand. IEEE , pp. 23-28. ISBN 9798350330618
Abstract
Digital technologies and artificial intelligence (AI) are rapidly transforming education and will prove more disruptive to the higher education sector than many people expect. A lot of attention has focused on the problem of how to deter and detect the use of online tutoring and essay-writing tools by students for coursework. The COVID lockdown raised awareness of the problem of potential malpractice in online assessments. Then, just as universities hoped things would start returning to normal, new tools like ChatGPT appeared on the scene and their remarkable capabilities pose even greater challenges to the status quo in academia. There is serious concern about how the rigor of coursework assessments can be guaranteed. However, this paper aims to show that there is a more positive side to how AI, combined with learning analytics, can be used to scale-up higher education to make it more accessible, and potentially improve student outcomes as well. The quality function deployment (QFD) approach is applied to consider how suitable tools could serve the needs of three key stakeholder groups: students, academics, and managers.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Artificial intelligence, Adaptive learning, Educational technology |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Pollard Institute (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Feb 2024 11:50 |
Last Modified: | 26 Feb 2024 11:50 |
Published Version: | https://ieeexplore.ieee.org/document/10356009 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ri2c60382.2023.10356009 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209571 |