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Am I Wrong, or Is the Autograder Wrong? Effects of AI Grading Mistakes on Learning

Authors:

Tiffany Wenting Li,

Karrie KarahaliosAuthors Info & Claims

ICER '23: Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1

Pages 159 - 176

https://doi.org/10.1145/3568813.3600124

Published: 10 September 2023 Publication History

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Abstract

Errors in AI grading and feedback often have an intractable set of causes and are, by their nature, difficult to completely avoid. Since inaccurate feedback potentially harms learning, there is a need for designs and workflows that mitigate these harms. To better understand the mechanisms by which erroneous AI feedback impacts students’ learning, we conducted surveys and interviews that recorded students’ interactions with a short-answer AI autograder for “Explain in Plain English” code reading problems. Using causal modeling, we inferred the learning impacts of wrong answers marked as right (false positives, FPs) and right answers marked as wrong (false negatives, FNs). We further explored explanations for the learning impacts, including errors influencing participants’ engagement with feedback and assessments of their answers’ correctness, and participants’ prior performance in the class.

FPs harmed learning in large part due to participants’ failures to detect the errors. This was due to participants not paying attention to the feedback after being marked as right, and an apparent bias against admitting one’s answer was wrong once marked right. On the other hand, FNs harmed learning only for survey participants, suggesting that interviewees’ greater behavioral and cognitive engagement protected them from learning harms. Based on these findings, we propose ways to help learners detect FPs and encourage deeper reflection on FNs to mitigate the learning harms of AI errors.

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Am I Wrong, or Is the Autograder Wrong? Effects of AI Grading Mistakes on Learning

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ICER '23: Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 1

August 2023

520 pages

ISBN:9781450399760

DOI:10.1145/3568813

Copyright © 2023 ACM.

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Published: 10 September 2023

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ICER 2023: ACM Conference on International Computing Education Research

August 7 - 11, 2023

IL, Chicago, USA

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Cited By

Delello JWatters JGarcia-Lopez A(2024)Artificial Intelligence in EducationDisruptive Technologies in Education and Workforce Development10.4018/979-8-3693-3003-6.ch001(1-26)Online publication date: 30-Jun-2024
https://doi.org/10.4018/979-8-3693-3003-6.ch001
Vahid F(2024)AI in CS Education: Opportunities, Challenges, and Pitfalls to AvoidACM Inroads10.1145/367920515:3(52-57)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1145/3679205
Gabbay HCohen AJoyner DKim MWang XXia M(2024)Combining LLM-Generated and Test-Based Feedback in a MOOC for ProgrammingProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662040(177-187)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3662040
Smith DDenny PFowler MJoyner DKim MWang XXia M(2024)Prompting for Comprehension: Exploring the Intersection of Explain in Plain English Questions and Prompt WritingProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662039(39-50)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3662039
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Kooli CYusuf N(2024)Transforming Educational Assessment: Insights Into the Use of ChatGPT and Large Language Models in GradingInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2338330(1-12)Online publication date: 15-Apr-2024
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Dey IGnesdilow DPassonneau RPuntambekar S(2024)Potential Pitfalls of False PositivesArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky10.1007/978-3-031-64315-6_45(469-476)Online publication date: 2-Jul-2024
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