Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answers
Open accessAbstract
Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them untrustworthy, raising ethical concerns and limiting their practical utility. Furthermore, the evaluation of autograding is typically confined to small, monolingual datasets for a specific question type. This study uses a large dataset consisting of about 10 million question-answer pairs from multiple languages covering diverse fields such as math and language, and strong variation in question and answer syntax. We demonstrate the effectiveness of fine-tuning transformer models for autograding for such complex datasets. Our best hyperparameter-tuned model yields an accuracy of about 86.5%, comparable to the state-of-the-art models that are less general and more tuned to a specific type of question, subject, and language. More importantly, we address trust and ethical concerns. By involving humans in the autograding process, we show how to improve the accuracy of automatically graded answers, achieving accuracy equivalent to that of teaching assistants. We also show how teachers can effectively control the type of errors made by the system and how they can validate efficiently that the autograder’s performance on individual exams is close to the expected performance.
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“This lack of transparency is problematic as it obscures the understanding of AI functionalities and potential flaws from those directly impacted by these technologies [77].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [77]
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- Authors
- Schneider, J., Richner, R., & Riser, M.
- Affiliations
- University of Liechtenstein (LI)
- Corresponding author
- Schneider, Johannes
- ORCID
- Schneider, Johannes
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 33
- Issue
- 1
- Pages
- 88-118
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W4220966949
- Cited by
- 48
- References
- 52
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 14 · 2023: 8 · 2022: 3
- FWCI
- 10.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- University of Liechtenstein
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- automated grading, question answering dataset, transformer fine-tuning, online learning, teaching assistants
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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