Safe and Responsible Use of AI
Generative AI has made headlines for its potential to transform the way we work. But there are also concerns about how we use AI in safe, responsible, and ethical ways. From protecting private information, to preparing for the potential for bias, examining some of the limitations and challenges surrounding AI will help us be prepared to leverage the potential of these new tools.
Protecting student privacy
LCPS does not yet provide students access to generative AI tools like ChatGPT. If you find a tool that you are interested in using with students, you must submit that tool for review before asking students to create accounts or creating accounts for them. Talk to you school’s IFT to learn more about the review process.
Using AI tools with students
AI offers immense potential for supporting student learning. For example, students may be able to use AI tools to get just-in-time help on assignments. AI could help students practice skills by generating question sets or writing prompts. AI can give students feedback on their writing to support revision, or surface videos or resources to help students learn certain topics.
While AI brings opportunities, it also brings challenges. Until we are able to provide students with a safe space to explore AI tools, teachers should not encourage students to use any tool that requires creating an account or sharing personal information. If you are wondering if an AI tool might be safe for your students to use, you must have that tool reviewed before using it with students. Talk to your school’s IFT for more information about the review process.
Citing the use of AI
MLA and APA have both created guidelines for how to cite AI-generated content in scholarly works.
The potential for bias in AI
The responses produced by AI are based on the data that is used to train the model. Sometimes, the data that underlies those models is flawed or incorrect. These flaws and incorrect assumptions can show up in the responses generated by the AI. Bias in data comes in many forms, including sampling bias, confirmation bias, or historical bias.
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