How can UNIMORE safeguard academic integrity in an AI-driven world?

As AI tools become integral to higher education, universities face the challenge of maintaining academic integrity while leveraging these technologies.

With the proliferation of AI-powered writing assistants and code generators, concerns about plagiarism in particular have evolved. Universities must now tackle "AI-generated plagiarism," where students may use AI to complete assignments or research papers without proper attribution. Institutions are addressing these challenges by refining plagiarism detection systems, drafting clear policies, and fostering a culture of responsible AI use.

UNIMORE is taking a cautious yet constructive approach to the challenges posed by AI in maintaining academic integrity. Recognizing both the opportunities and risks of generative AI, the university is focusing on awareness, education, and adaptation to ensure the ethical use of AI across academic practices.

At UNIMORE, the first step toward ensuring academic integrity has been educating both professors and students about AI's potential and its limitations.

Faculty members are experimenting with tools like ChatGPT to demonstrate their capabilities while highlighting the inaccuracies and ethical implications of relying on AI-generated content. This approach equips students with the skills to critically assess AI outputs.

The challenges of AI detection

UNIMORE has long used plagiarism detection tools, but generative AI introduces a new set of challenges. Traditional tools struggle to detect AI-generated content, particularly in fields like humanities, where work often synthesizes existing sources.

In response, the university emphasizes oral defenses and presentations for theses, ensuring that students can articulate their ideas and demonstrate mastery of their work, even if AI tools were involved in its creation.

Adapting academic practices

To uphold integrity, UNIMORE is exploring new ways to design assignments and assessments that minimize opportunities for misuse while preserving their educational value. Examples include:

• Hands-on activities that require critical thinking and engagement, making it difficult for AI tools to replace student effort.
• Practical tasks such as interviews and data collection that go beyond the scope of generative AI capabilities.