Cultivating AI literacy is an essential part of understanding how and why to use AI in the classroom. Your course content and personal preferences will determine to what extent you will want to cultivate AI literacy to achieve certain teaching goals. Created by IMATS at Barnard College and modified by Sheridan Center, the following list can help you determine your level of literacy.
Understand AI
- Define the term AI, as well as machine learning, large language models, and neural networks
- Identify and explain differences between various types of AI, as defined by their capabilities and computational mechanisms. Be able to identify the tools most relevant to your course content
- Recognize the benefits and limitations of AI tools. Also, know which tools are supported by Brown
Application Examples
- Writing an AI policy that uses common AI terminology and is grounded in foundational AI knowledge
- Discussing the benefits and limitations of AI with your students
Use and Apply AI
- Successfully utilize generative AI tools to get desired responses.
- Experiment with prompting techniques and iterate on prompt language to improve AI -generated output. (This will enable you to better guide and understand student use of AI for assignments.)
- Review AI-generated content with an eye towards potential errors, bias, fabrications, and incorrect reasoning. Identify patterns that you can share with students
Application Examples
- Using an ethical framework to make decisions about whether to use AI for a task and how to do so
- Showing students examples of ways AI can be utilized in your course
Evaluate and Analyze AI
- Examine AI in a broader context, bringing in knowledge from your own discipline and/or interests
- Critique AI tools and offer arguments in support of or against their creation, use, and application
- Analyze ethical considerations in the development of AI and its deployment in specific situations, including in the classroom
- Assess your own willingness to engage with AI in the classroom, as well as in general
Application Example
- Creating lessons that tie AI into your discipline to evaluate its impact (e.g., in an economics course, a lesson on AI’s impact on workforce productivity and its implications)
Create with AI
- Successfully synthesize your learnings to conceptualize or create new ideas, technologies, or structures that relate to AI. Examples of reaching this level of literacy could include:
- Building software that leverages AI technology
- Proposing theories about AI
- Conceiving of novel uses for AI
- Developing assessments that incorporate AI in a meaningful way
Application Examples
- Advising students on senior thesis projects that involve AI, especially in fields such as computer science or cognitive science
- Develop a discipline-specific LLM to assist with research
Of course, this is only one way to evaluate AI literacy. There are other frameworks you can use, depending on your discipline.
- A Framework for AI Literacy, EDUCAUSE
- AI Literacy for All: A Universal Framework, Leo Lo, current president of the Association of College and Research Libraries
- AI Literacy Framework, The Digital Education Council