Equity, Privacy, and Academic Integrity in Generative AI

Generative AI tools learn from patterns in their training data. Their outputs are generally the prediction of the most likely pattern based on your input. Because these tools don't have an understanding of the material they generate or a sense of true or false, the items they output may not be accurate. The outputs may also be convincing as plausible even though they are inaccurate. Users of generative AI tools still need a base level of understanding of the topics they are using with these tools to help them critically evaluate the outputs.

Due to the way that generative AI pulls information and learns, there are several ways in which AI can reinforce or amplify bias. At a basic level, generative AI is a prediction model based on a large set of data. It leverages frequently occurring patterns in that data, and certain patterns will occur less frequently than others because there is less data (e.g. certain populations are not represented at all or are represented less frequently). This can result in things being missing from the results or incorrect assumptions being made. If the tool pulls from sources that exhibit biased assumptions or are not diverse, then the biased result will be reflected in the output.

There is also the potential for privacy concerns with respect to personal data, intellectual property, and copyrighted data. Generative AI is trained on data, and for some tools, the prompts you use are being used by the tool's developer to train the model further. Content you put into the tool may become part of the tool. Once trained, the AI could respond to another user in the future with this information or very similar information, and without attribution to the original owner.

All RIT faculty, staff, and students have access to Microsoft Co-Pilot with commercial data protection. Using RIT’s protected version of Co-Pilot provides us with more protection than publicly-available tools like ChatGPT.

Some tools are currently in free public beta. As sponsoring organizations look towards monetization, tools may be available only to individuals with the financial means to use the tool. This creates an access issue that you will want to be aware of.

Generative AI models take a lot of computing power to train and a lot of computing power to run. The energy consumption, water use, and greenhouse gas emissions when developing and using these tools are some of the main topics of conversation in this area.

Some articles in the higher education press have suggested that assigning in-class, hand-written or oral work is the most effective way to bolster academic integrity within the context of generative AI. However, relying exclusively or excessively on many of the proposed low-tech, time-limited approaches may prevent non-native English speakers, deaf and hard of hearing learners, or students with disabilities requiring laptop access during class and other accommodations from RIT’s Disability Services Office from fully demonstrating their learning.

As generative AI tools become increasingly integrated into education, we encourage faculty to move beyond dependence on AI detection software as part of their pedagogy. Instead, faculty should consider taking a proactive approach that prioritizes academic integrity, ethical AI use, and critical thinking through the intentional design of assignments within a course.

Key Considerations

• AI detection tools are unreliable, prone to false positives, and lack transparency
• Students can easily modify AI-generated text to evade detection
• A focus on catching AI use is less effective than redesigning assessments and teaching responsible engagement

CTL Recommendations

• Design assignments that emphasize process, creativity, and personal reflection
• Teach students how to use AI as a learning aid rather than a shortcut
• Integrate AI into coursework to develop critical thinking and ethical literacy

Instead of policing AI, you can equip students with the skills to use it ethically and thoughtfully to help prepare them for success in the future.

For more information, review Generative AI: Syllabus Guidance.