Responsible AI Use

Privacy and Data Security

Protect Sensitive Information

Never put sensitive or confidential information into an AI tool. This includes personally identifiable information (PII), proprietary research, confidential student data, or any other private information. Treat AI tools like public spaces; anything you input could be accessed by others.

Understand Data Usage

Be aware that the data you input might be used to train the AI model. This means your text, code, or other data could become part of the AI's future responses, potentially being shared with other users. Always review the AI tool's terms of service and privacy policy to understand how your data will be used, stored, and shared.

Verify and Attribute

AI can sometimes generate inaccurate or "hallucinated" information. Always verify the information and sources provided by AI tools. If you use AI-generated content in your work, remember that you are still responsible for the final output. If you're using AI for research or academic purposes, be sure to properly attribute the use of the tool in your citations.

Responsible Use of AI

Academic Integrity

Using AI tools to complete assignments in a way that misrepresents your own work can be a form of academic misconduct (plagiarism/cheating). Be transparent about your use of AI. Consult with your instructor (refer to your course syllabus) about their course policy on AI tools BEFORE engaging with any AI tools to assist you in your coursework. 

Bias and Inclusivity

AI systems are trained on vast datasets– predominantly collected from the internet– and therefore incorporate the biases and stereotypes embedded in those datasets. AI-generated content reflects dominant cultural norms and can pose the risk of marginalizing or misrepresenting, reinforcing harmful stereotypes and perpetuating inequality. 

For the same reason, the use of AI might also undercut the learning goals of intellectual vitality. Intellectual vitality encourages students to question assumptions and resist arriving at premature conclusions— two areas where AI-generated output often falls short.

Another consideration is how certain AI tools can disadvantage certain student groups. For instance, AI systems that process language may struggle with non-standard dialects or multilingual speakers, leading to inaccuracies or misunderstandings. Vigilance in recognizing and addressing AI’s limitations is essential in diverse classroom settings.

Your ability to think critically is your strongest asset when it comes to effectively evaluating AI-generated content for accuracy and bias: 

1. Look for inconsistencies and unsupported claims, as they may indicate underlying biases.

2. Consider the language used in the output — biased content may use loaded or inflammatory language.

3. Incorporate tools that specialize in evaluating AI-generated content (such as QuillBot) — especially those that assess AI accuracy in research by analyzing the data sources and methodologies used in the artificial intelligence’s training. 

Intellectual Property

The content generated by an AI tool may be derived from copyrighted material. Be cautious when using AI-generated content, especially for public-facing or commercial purposes. You are responsible for ensuring that your use of the content does not infringe upon existing copyrights or intellectual property rights.

Environmental Impact

Training and running large AI models require substantial computational power, which in turn consumes significant energy. This energy consumption is not unique to AI; many digital processes, such as video streaming and cloud storage, also demand significant resources. But as AI use expands in education, its contribution to carbon emissions and other environmental harms likewise grows.