short-paper

Revealing Factors Influencing Students' Perceived Fairness: A Case with a Predictive System for Math Learning

Authors:

Wanli XingAuthors Info & Claims

L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale

Pages 409 - 412

https://doi.org/10.1145/3491140.3528293

Published: 01 June 2022 Publication History

Abstract

Educational researchers have examined artificial intelligence (AI) to automatically support students' learning at a large scale. However, it has been broadly identified that AI models can suffer from algorithmic bias. A recent educational research initiative has shifted to evaluate and address algorithmic bias. Nonetheless, few studies have examined students' perceived fairness towards AI-enhanced systems in education. This study aimed to explore and understand factors that shape students' perceived fairness towards an AI system for math learning at the college level. Specifically, we have conducted a between-subjects randomized experiment with 395 participants to reveal factors that influenced participants' perceptions of fairness. The results showed that students' math anxiety levels and majors could influence participants' reported perceived fairness. In contrast, the outcome favorability of the predictive system did not affect students' perceptions of fairness. Future investigations have been planned to further understand the relationships between the designed manipulations and perceived fairness.

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Cited By

Ly RLy B(2025)Ethical challenges and opportunities in ChatGPT integration for education: insights from emerging economyAI and Ethics10.1007/s43681-025-00667-yOnline publication date: 2-Feb-2025
https://doi.org/10.1007/s43681-025-00667-y
Xing WLi C(2024)Fair Artificial Intelligence to Support STEM EducationUses of Artificial Intelligence in STEM Education10.1093/oso/9780198882077.003.0023(522-546)Online publication date: 21-Nov-2024
https://doi.org/10.1093/oso/9780198882077.003.0023
Kim SOh PLee J(2024)Algorithmic gender bias: investigating perceptions of discrimination in automated decision-makingBehaviour & Information Technology10.1080/0144929X.2024.230648443:16(4208-4221)Online publication date: 31-Jan-2024
https://doi.org/10.1080/0144929X.2024.2306484
Show More Cited By

Index Terms

Revealing Factors Influencing Students' Perceived Fairness: A Case with a Predictive System for Math Learning
1. Applied computing
  1. Education
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction

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cover image ACM Other conferences

L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale

June 2022

491 pages

ISBN:9781450391580

DOI:10.1145/3491140

General Chair:
René F. Kizilcec
Cornell University, USA
,
Program Chairs:
Katie Davis
University of Washington, USA
,
Xavier Ochoa
New York University, USA

Copyright © 2022 ACM.

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Published: 01 June 2022

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University of Florida AI Catalyst Grant

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L@S '22

L@S '22: Ninth (2022) ACM Conference on Learning @ Scale

June 1 - 3, 2022

NY, New York City, USA

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Cited By

Ly RLy B(2025)Ethical challenges and opportunities in ChatGPT integration for education: insights from emerging economyAI and Ethics10.1007/s43681-025-00667-yOnline publication date: 2-Feb-2025
https://doi.org/10.1007/s43681-025-00667-y
Xing WLi C(2024)Fair Artificial Intelligence to Support STEM EducationUses of Artificial Intelligence in STEM Education10.1093/oso/9780198882077.003.0023(522-546)Online publication date: 21-Nov-2024
https://doi.org/10.1093/oso/9780198882077.003.0023
Kim SOh PLee J(2024)Algorithmic gender bias: investigating perceptions of discrimination in automated decision-makingBehaviour & Information Technology10.1080/0144929X.2024.230648443:16(4208-4221)Online publication date: 31-Jan-2024
https://doi.org/10.1080/0144929X.2024.2306484
Li CXing WLeite W(2022)Using fair AI to predict students’ math learning outcomes in an online platformInteractive Learning Environments10.1080/10494820.2022.211507632:3(1117-1136)Online publication date: 28-Aug-2022
https://doi.org/10.1080/10494820.2022.2115076

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