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Artificial Intelligence, Plagiarism, and Academic Dishonesty: Best Practices, Model Policies, and Legal Implications
Joshua A. Engel, Managing Partner at Engel & Martin LLC, will be presenting on Artificial Intelligence, Plagiarism, and Academic Misconduct at the 2024 Education Law Association Conference[1]
A copy of the Presentation is here.
The rapid proliferation of artificial intelligence (AI) is revolutionizing many fields, including education. While AI holds immense potential, it also presents serious challenges in maintaining academic integrity, especially concerning plagiarism and misconduct. This article outlines the implications of AI in education, focusing on best practices for academic institutions, legal considerations, and how AI impacts the concept of academic dishonesty.
The AI Cheating Problem
The emergence of AI in education has had a dramatic impact on academic dishonesty. The first year of AI implementation in higher education revealed widespread cheating, leaving faculty and institutions ill-prepared. Traditional tools for detecting computer-generated content were inadequate, and adjudicating such cases fairly became increasingly difficult.
For instance, data from Turnitin, a widely-used plagiarism detection software, reported that out of 200 million papers, around 11% showed at least 20% AI writing, and 3% had over 80% AI involvement. This trend has sparked an urgent need for educators to differentiate between legitimate and unethical uses of AI. As AI continues to evolve, educators and institutions must adopt new frameworks and policies to address these challenges.
Defining Plagiarism and Academic Misconduct in the AI Era
Traditionally, plagiarism is defined as using someone else’s work or ideas without proper attribution. In the AI era, this definition needs expansion. AI-generated content presents new challenges for attribution and originality, blurring the lines of what constitutes plagiarism. Using AI without proper citation is considered plagiarism, while citing AI appropriately, according to major academic styles like MLA and APA, is crucial.
It is also essential to differentiate plagiarism from broader academic misconduct. AI might generate original text that is not directly copied, but if students use it without proper disclosure, it can still be considered misconduct. Therefore, understanding when AI use becomes misconduct hinges on whether it aligns with the educational goals of acquiring skills or knowledge.
Proper AI Citation and Model Policies
Major academic styles have begun to address the proper citation of AI-generated content. The Modern Language Association (MLA) and American Psychological Association (APA) have offered guidelines on citing AI sources. The MLA recommends citing both the prompt used and the secondary sources provided by the AI, while the APA focuses on citing the AI tool itself and providing details about the prompt in the text.
Institutions must develop clear policies to guide students and faculty in the ethical use of AI. A proposed model policy includes several key elements:
- Academic Freedom: Faculty should retain the freedom to determine when and how AI may be used in their courses.
- Disclosure: The use of AI must be disclosed, and any use without proper citation should be prohibited.
- Graded Assignments: AI should not be used to complete tasks where learning specific skills or demonstrating understanding is essential.
- Consequences for Misuse: A tiered approach for addressing violations includes allowing students to redo assignments for the first and second offenses. Further violations would be treated similarly to other forms of academic dishonesty.
Legal Implications and Due Process
The legal landscape surrounding academic dishonesty is evolving, especially with AI’s increasing role. One significant area is how due process is handled in academic misconduct cases. Students have a substantial interest in maintaining their academic reputations, and the amount of due process afforded to students often depends on whether a case is treated as academic or disciplinary misconduct.
The U.S. legal system makes a key distinction between academic and disciplinary dismissals. Academic dismissals, related to a student’s skills and performance, are typically beyond judicial review, whereas disciplinary actions, such as cheating or plagiarism, often involve fact-finding and are more likely to receive scrutiny in court.
The Nexus Between Academic Success and Misconduct
The central legal question when considering AI-related misconduct is whether the behavior in question impacts the student’s prospects in their field of study. Courts often use the “nexus test” to decide if a dismissal is academic. If a university argues that AI misuse compromises a student’s professional abilities, the case may be treated as academic misconduct, and the due process afforded will differ from that for purely disciplinary issues.
Conclusion: Adapting to the AI Landscape
The rise of AI is reshaping education, challenging notions of creativity, originality, and academic integrity. Educational institutions must adapt, ensuring that they preserve academic freedom, protect due process, and maintain clear guidelines for AI use. Proper training, the development of robust policies, and the willingness to re-evaluate approaches continuously are critical steps in navigating this new landscape. Only then can institutions uphold the integrity of education in the age of artificial intelligence.
[1] This entire article was written by Chat GPT based on the PowerPoint presentation. The prompt was, “please turn this PowerPoint presentation in a 3-5 page article.”
Chat GPT included this note: “This article encapsulates the core ideas of your presentation, turning them into a structured narrative for readers. It emphasizes the impact of AI on academic integrity, while also considering legal frameworks and institutional policies.”
