Are students using Generated AI fraud? The answer is complicated

The rise of generative AI has sparked widespread ethical concern among educators, with many fearing a surge in student misconduct. Some instructors have banned classroom tools and used detection software to act on police students, while others have decided to quit.

I believe this approach is reactionary and overlooks more pressing questions. Should the traditional definition of academic integrity – the idea of ​​fair play in the context of learning – be shaken up in the age of genai? Learning to use technology rather than oppose technology is a more sustainable path to advancement.

It suggests that plagiarism has not increased significantly since the popularity of genai tools. Stanford University research shows that fraud rates are actually stable, and the main motivation for fraud is not access to AI technology, but rather familiarity such as poor time management and overwhelming workloads. It’s a problem. However, many educators report growing distrust in students’ work. This may indicate a deep sitting tension between the long-standing concept of learning and the reality of today’s genai landscape.

Traditionally, education has been viewed as a unidirectional process. This allows knowledge to be accumulated through individual efforts. Rooted in the age of printing, this model assumes that bypassing certain steps, such as facing blank pages or drafting outlines, will undermine authentic learning.

Correspondingly, it is understood that assessments accurately represent students’ learning and, in turn, represent students’ abilities. Genai offers new ways to access and process information, as well as how all other educational technologies (printing machines, word processors, hand calculators, internet) have influenced education, and thus provide these assumptions. I’m trying to do it.

Despite being deployed in almost all disciplines, the 2022 Openai release of ChatGpt wrote the foundation for Genai in education. The ability to create written documents from scratch – essays, reports, manuals, stories – has long been the cornerstone of liberal arts education.

Genai is now able to create the first draft of coherent almost instantly, but this does not make your skills outdated. Instead, emphasis shifts from initial configuration to critical analysis, revision and “quick engineering” (the ability to effectively teach AI systems).

This shift not only maintains students’ opportunities to learn basic skills. It creates an environment in which new skills related to genai can appear in monitored contexts. This is a supervision that reinforces the notion that human expertise and surveillance remains a critical component of responsible Genai use.

In other words, core competencies such as critical thinking, creativity, and ethical reasoning can continue to remain relevant, but also focus on education and raise important questions about how to understand pedagogy and assessment. It has sex.

The challenge for educators is to bridge the gap between traditional and emerging skills. Rather than banning genai entirely and spending time policing student behavior (responses that are easier to understand two years ago), we need to explore ways in which technology enhances learning and promotes human knowledge . Prohibiting students from using genai in their classes deprives them of the opportunity to develop the skills needed to successfully utilize and evaluate genai production.

Some agencies are already leading the way. MIT has published guidelines that encourage teachers to responsibly incorporate AI tools into their curriculum, while Stanford’s AI + Education Initiative will allow educators to integrate AI literacy into existing subjects We are developing resources for

In Florida and Mississippi, public and private agencies work together to provide resources and guidance on how to relocate higher education to address AI-related challenges and leverage new opportunities. Topics considered include the devising institutional policies to promote AI flow ency, the identification and management of deepfakes, and the thoughtful integration of AI throughout the curriculum. Such an approach encourages teachers to modify and adjust legacy ideology and methods rather than simply responding to genai.

The transition to a new teaching paradigm is not easy. It requires rethinking assessment methods, updating academic integrity policies (not to mention the very concepts of what constitutes academic integrity in light of these tools), and investments in faculty training. However, sticking to the outdated concept of learning poses greater risks while technology advances.

So are students using AI fraud? In fact, if its use violates an explicit policy that prohibits it. Our point is that as educators, we must ask ourselves whether these policies actually serve students or reflect an increasingly obsolete education model.

From our perspective, the future of education is not banning genai, but exploiting the possibilities of creating more engaging, relevant and equitable learning experiences. To that end, our understanding of the integrity of our academic discipline must evolve with the technology that is quickly changing our world.

Dr. Sid Dobrin is professor and chair of the Department of English at the University of Florida. Dr. Bruce Fraser is the director of the Institute of Academic Excellence at Indian River State College.