Cheating has always been a problem for academic institutions, but the internet has increased access to a form of academic dishonesty known as contract cheating, or "homework for hire." When students purchase work online and submit it as their own, it cannot be detected by commonly-used plagiarism detection tools, and this troubling form of cheating seems to be increasing.

We present an approach to addressing contract cheating: an AI agent that poses as a contractor to identify students attempting to purchase homework solutions. Our agent, Jack Watson, monitors auction sites, identifies posted homework assignments, and provides students with watermarked solutions that can be automatically identified upon submission of the assignment.

Our work is ongoing, but we have proved the model, identifying nine cases of contract cheating through our techniques. We are continuing to improve Jack Watson and further automate the monitoring and identification of contract cheating on online marketplaces.

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Duncan ATang THuang YLuu JThakkar NJoyner DJoyner DKim MWang XXia M(2024)Forums, Feedback, and Two Kinds of AI: A Selective History of Learning @ ScaleProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3664667(376-382)Online publication date: 9-Jul-2024
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Adkins KJoyner DJoyner DKim MWang XXia M(2024)Newly Created Assignments and The First Repository Effect on Inter-Semester PlagiarismProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662043(211-220)Online publication date: 9-Jul-2024
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Volarić TMartinović GLjubić H(2023)Detecting Academic Fraud at Online Tests During COVID-19 Using Machine Learning-Based MethodsDigital Transformation in Education and Artificial Intelligence Application10.1007/978-3-031-36833-2_11(144-158)Online publication date: 11-Jul-2023
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Index Terms

Jack Watson: Addressing Contract Cheating at Scale in Online Computer Science Education
1. Applied computing
  1. Education

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L@S '19: Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale

June 2019

386 pages

ISBN:9781450368049

DOI:10.1145/3330430

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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https://dl.acm.org/doi/10.1145/3657604.3664667
Adkins KJoyner DJoyner DKim MWang XXia M(2024)Newly Created Assignments and The First Repository Effect on Inter-Semester PlagiarismProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662043(211-220)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3662043
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Adkins KJoyner D(2022)Scaling anti‐plagiarism efforts to meet the needs of large online computer science classes: Challenges, solutions, and recommendationsJournal of Computer Assisted Learning10.1111/jcal.1271038:6(1603-1619)Online publication date: 11-Jul-2022
https://doi.org/10.1111/jcal.12710
Duncan AJoyner D(2022)On the necessity (or lack thereof) of digital proctoring: Drawbacks, perceptions, and alternativesJournal of Computer Assisted Learning10.1111/jcal.1270038:5(1482-1496)Online publication date: 26-Jun-2022
https://doi.org/10.1111/jcal.12700
Joyner DEicher B(2022)Taking Stock of MOOCs and Credit Substitutability2022 IEEE Learning with MOOCS (LWMOOCS)10.1109/LWMOOCS53067.2022.9927806(217-222)Online publication date: 29-Sep-2022
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Manoharan SSpeidel U(2020)Contract Cheating in Computer Science: A Case Study2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)10.1109/TALE48869.2020.9368454(91-98)Online publication date: 8-Dec-2020
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Commercial aspects of contract cheating

An Observational Analysis of the Range and Extent of Contract Cheating from Online Courses Found on Agency Websites

The Application of Intelligent Context-Aware Systems to the Detection of Online Student Cheating

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