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Journal of Data Science


Discussion of “Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies”✩
Volume 24, Issue 1 (2026): Special Issue: Statistical aspects of Trustworthy Machine Learning, pp. 282–285
Pub. online: 11 February 2026      Type: Education In Data Science      Open accessOpen Access

Published
11 February 2026

References

 
Adams R (2025). UK universities warned to ‘stress-test’ assessments as 92% of students use AI. The Guardian. Available at: https://www.theguardian.com/education/2025/feb/26/uk-universities-warned-to-stress-test-assessments-as-92-of-students-use-ai, Accessed 2025-12-02.
 
Bittle K, El-Gayar O (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4): 296. https://doi.org/10.3390/info16040296
 
Galeano S, Hodge N, Ruder A (2025). By degree(s): Measuring employer demand for AI skills by educational requirements. (2025-01). Center for Workforce and Economic Opportunity, https://doi.org/10.29338/wc2025-01.
 
Jin Y, Yan L, Echeverria V, Gašević D, Martinez-Maldonado R (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8:100348.
 
Wang S, Xu L, Liu J, Zhai Y (2025). Addressing the challenges of AI-generated assignment submissions in education: Insights and strategies. Journal of Data Science, 24(1): 1–7. https://doi.org/10.6339/25-JDS1208
 
Williams A (2025). Integrating artificial intelligence into higher education assessment. Intersection: A Journal at the Intersection of Assessment and Learning, 6(1): 128–154.

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Copyright
2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
academic integrity AI literacy assessment strategies emerging skills generative AI higher education

Funding
This material is based upon work supported by the National Science Foundation under Grants No. 2449870 (AZ) and 2015320 (OH). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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