Assessing the Potential and Risks of AI-Based Tools in Higher Education: Results from an eSurvey and SWOT Analysis

Denecke, Kerstin; Glauser, Robin Paul; Reichenpfader, Daniel (2023). Assessing the Potential and Risks of AI-Based Tools in Higher Education: Results from an eSurvey and SWOT Analysis Trends in Higher Education, 2(4), pp. 667-688. 10.3390/higheredu2040039

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Recent developments related to tools based on artificial intelligence (AI) have raised interests in many areas, including higher education. While machine translation tools have been available and in use for many years in teaching and learning, generative AI models have sparked concerns within the academic community. The objective of this paper is to identify the strengths, weaknesses, opportunities and threats (SWOT) of using AI-based tools (ABTs) in higher education contexts. We employed a mixed methods approach to achieve our objectives; we conducted a survey and used the results to perform a SWOT analysis. For the survey, we asked lecturers and students to answer 27 questions (Likert scale, free text, etc.) on their experiences and viewpoints related to AI-based tools in higher education. A total of 305 people from different countries and with different backgrounds answered the questionnaire. The results show that a moderate to high future impact of ABTs on teaching, learning and exams is expected by the participants. ABT strengths are seen as the personalization of the learning experience or increased efficiency via automation of repetitive tasks. Several use cases are envisioned but are still not yet used in daily practice. Challenges include skills teaching, data protection and bias. We conclude that research is needed to study the unintended consequences of ABT usage in higher education in particular for developing countermeasures and to demonstrate the benefits of ABT usage in higher education. Furthermore, we suggest defining a competence model specifying the required skills that ensure the responsible and efficient use of ABTs by students and lecturers.

Item Type:	Journal Article (Original Article)
Division/Institute:	School of Engineering and Computer Science > Institute for Patient-centered Digital Health School of Engineering and Computer Science
Name:	Denecke, Kerstin0000-0001-6691-396X; Glauser, Robin Paul and Reichenpfader, Daniel0000-0002-8052-3359
Subjects:	L Education > L Education (General) Q Science > Q Science (General)
ISSN:	2813-4346
Language:	English
Submitter:	Kerstin Denecke
Date Deposited:	08 Dec 2023 14:16
Last Modified:	08 Dec 2023 14:16
Publisher DOI:	10.3390/higheredu2040039
Related URLs:	Publisher Publication
Uncontrolled Keywords:	Artificial intelligence SWOT Technology-enhanced learning Hgher education
ARBOR DOI:	10.24451/arbor.20627
URI:	https://arbor.bfh.ch/id/eprint/20627