Can AI help predict a learner’s needs? Lessons from predicting student satisfaction : WestminsterResearch

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Title	Can AI help predict a learner’s needs? Lessons from predicting student satisfaction
Type	Journal article
Authors	Parapadakis, D.
Abstract	The successes of using artificial intelligence (AI) in analysing large-scale data at a low cost make it an attractive tool for analysing student data to discover models that can inform decision makers in education. This article looks at the case of decision making from models of student satisfaction, using research on ten years (2008–17) of National Student Survey (NSS) results in UK higher education institutions. It reviews the issues involved in measuring student satisfaction, shows that useful patterns exist in the data and presents issues involved in the value within the data when they are examined without deeper understanding, contrasting the outputs of analysing the data manually, and with AI. The article discusses risks of using AI and shows why, when applied in areas of education that are not clear, understood and widely agreed, AI not only carries risks to a point that can eliminate cost savings but, irrespective of legal requirement, it cannot provide algorithmic accountability.
Keywords	artificial intelligence, algorithmic accountability, national student survey (NSS), higher education, decision making
Journal	London Review of Education
Journal citation	18 (2), pp. 178-195
ISSN	1474-8479
Year	2020
Publisher	UCL Press
Publisher's version	lre18020003 - final.pdf License CC BY 4.0 File Access Level Open (open metadata and files)
Digital Object Identifier (DOI)	https://doi.org/10.14324/LRE.18.2.03
Web address (URL)	https://www.scienceopen.com/document?vid=cf22bec3-114b-4305-aee9-570c9eca6f11
Published	21 Jul 2020

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Can AI help predict a learner’s needs? Lessons from predicting student satisfaction

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