Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies

The rapid development of artificial intelligence (AI) tools, particularly generative models, has introduced significant challenges in academic assessment. Students increasingly rely on AI to complete assignments, complicating the evaluation of their true understanding and effort. This paper examines the limitations of AI detection tools, the inadequacies of traditional teaching methods in this context, and the potential for responsibly integrating AI into educational practices. Drawing on insights from educators and recent developments in AI, the paper proposes strategies for adapting assessment methods to ensure academic integrity while embracing technological advancements. The findings underscore the need for a balanced approach that leverages AI’s benefits while mitigating its risks.

Authors

Publication Details

Journal
Journal of Data Science
Published
2025-11-14
DOI
https://doi.org/10.6339/25-jds1208
Citations
10
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
24.81

Funders

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article

Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies

Yujia Zhai, Lin Xu, Junchen Liu, Shuyi Wang
10 citations
Journal of Data Science
Intelligent Tutoring Systems and Adaptive Learning
24.81
article

Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies

Yujia Zhai, Lin Xu, Junchen Liu, Shuyi Wang
article en
10 citations

Abstract

The rapid development of artificial intelligence (AI) tools, particularly generative models, has introduced significant challenges in academic assessment. Students increasingly rely on AI to complete assignments, complicating the evaluation of their true understanding and effort. This paper examines the limitations of AI detection tools, the inadequacies of traditional teaching methods in this context, and the potential for responsibly integrating AI into educational practices. Drawing on insights from educators and recent developments in AI, the paper proposes strategies for adapting assessment methods to ensure academic integrity while embracing technological advancements. The findings underscore the need for a balanced approach that leverages AI’s benefits while mitigating its risks.

Journal of Data Science
National Office for Philosophy and Social Sciences
Openalex Percentile: Top 1%
Intelligent Tutoring Systems and Adaptive Learning
24.81
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