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View all- Cha SLoeser MSeo K(2024)The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making ProcessApplied Sciences10.3390/app1409367214:9(3672)Online publication date: 25-Apr-2024
Convincing the Expert: Reducing Algorithm Aversion in Administrative Higher Education Decision-making
Pages 215 - 225
Abstract
Algorithm aversion can be described as the tendency of human decision-makers to discount algorithmic recommendations more heavily than similar recommendations made by humans. It has been a phenomenon observed to be most acutely exhibited by domain experts. In our work, we focus on expert administrators in higher education making course credit equivalency decisions that affect the academic planning and potential degree progress of millions of prospective transfer students. Using human-centered design, we construct an AI-based platform for recommending matches to courses on a student's transcript to courses offered at another institution. We conduct a 2 x 2, between-subject experiment to investigate potential aversion mitigation techniques by manipulating the presence of outliers and allowing users to provide feedback to the algorithm. Our findings indicate that intentional, human-centered design and careful presentation of algorithm-based recommendations can help improve Human-AI interaction and productivity with implications for various domains of expertise.
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- Convincing the Expert: Reducing Algorithm Aversion in Administrative Higher Education Decision-making
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Published In
July 2023
445 pages
ISBN:9798400700255
DOI:10.1145/3573051
- General Chair:
- Daniel Spikol,
- Program Chairs:
- Olga Viberg,
- Alejandra Martínez-Monés,
- Philip Guo
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution-NoDerivatives International 4.0 License.
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Association for Computing Machinery
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Published: 20 July 2023
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View all- Cha SLoeser MSeo K(2024)The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making ProcessApplied Sciences10.3390/app1409367214:9(3672)Online publication date: 25-Apr-2024
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