Modern Language Association of America. “Student Guide to Ai Literacy.” MLA Style Center, 2024, style.mla.org/student-guide-to-ai-literacy/. This guide was written by participants of the Critical AI Literacy for Reading, Writing, and Languages Workshop, an initiative of the MLA-CCCC Task Force on Writing and AI. The guide reflects the perspectives of the authors; it has not gone through the CCCC Executive Committee or MLA Executive Council approval processes, and thus does not necessarily reflect CCCC, NCTE, or MLA policy or recommendations.
While GenAI (generative artificial intelligence) tools appear to work instantaneously, almost like magic, they rely on immense amounts of data and computing functions.
GenAI is the product of humans’ work in advanced mathematics, statistics, computer science, engineering, linguistics, and more, and GenAI tools perform tasks that typically require human decision-making, such as understanding natural language, recognizing patterns in data, making predictions, and even learning from experience.
The “intelligence” of AI refers to its ability to simulate human cognitive functions, including learning from new and preexisting knowledge to solve problems.
However, users of GenAI observe that its processes and outputs often require the supervision and intervention of critically thinking humans.
Ethical and effective use of GenAI technologies is emerging as an essential skill that students must develop in order to live, learn, and work.
Yet GenAI comes with potential pitfalls for students–from the risk of being accused of academic misconduct to missing out on foundational skills in reading, writing, research, and learning.
Developing literacy with a tool means becoming a more skilled and thoughtful user of that tool.
For example, developing literacy in reading means being able to reread, tackle increasingly difficult texts, and do research in order to further build your capability as a reader.
Literacy also assumes you have enough knowledge to question and evaluate what you are studying.
Similarly, developing AI literacy requires that you learn certain basics about how GenAI works, how to use it, and how to evaluate its output.
You should also learn when not to use it.
Developing GenAI literacy should be your starting point for using this technology.
When you build skills and habits for using GenAI ethically and effectively you will establish yourself as a thoughtful creator and consumer of GenAI content as technologies change over time.
You are becoming a literate user of GenAI when you can do the following:
Download a PDF of this guide.
This guide was written by participants of the Critical AI Literacy for Reading, Writing, and Languages Workshop, an initiative of the MLA-CCCC Task Force on Writing and AI.
The guide reflects the perspectives of the authors; it has not gone through the CCCC Executive Committee or MLA Executive Council approval processes, and thus does not necessarily reflect CCCC, NCTE, or MLA policy or recommendations.
© 2024 Modern Language Association of America
AI Literacy
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As you consider the use of AI in the writing classroom, what are the places where the use of generative AI writing tools with your students aligns with — or potentially undermines — your own pedagogical values?
As you consider the use of AI in the writing classroom, we will first step away from AI.
Courtesy of Brené Brown, consider your own values from this list and identify two or three key pedagogical values.
What are your core values and why are they central to your teacherly identity?
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In our work with schools around my county, we have been encouraging them to anchor first in their community values as articulated in their portrait of a graduate or school pillars or similar articulations of those community values. This has been the most generative move we’ve made in our work. For more on how we approach this with teams of teachers, admin, and students, please visit this link: https://bit.ly/GenAIOS
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There are many frameworks, policy documents, and lists of questions to consider as it relates to integrating AI in writing instruction. For today, we will focus on one, the MLA/CCCC statement, “Student Guide to AI Literacy.” Mentioned as one of many, many resources here, you may also be interested in this recent webinar from the SUNY Center for Professional Development (session slides from Anna Mills) as well as the ever-growing list of syllabi statements on AI, curated by Lance Eaton.
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Complex notion — articulating how simulation happens, what underpins a simulated voice, understanding or assertion. Presumes an understanding of the domain expertise that is being simulated…Interesting way to ground discussion going into this doc and topic.
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Audrey Watters had a post a while back about the way ed tech for-profit companies use literacy as almost an Orwellian discourse move. She writes, "As complex and contentious as “literacy” might be, arguably it’s safer to frame your AI sales-pitch as a such than labeling it “job training,” even though that’s often what these literacy programs entail. It’s certainly what most tech literacy initiatives imply: students’ future job prospects depend on their professional, “productive” usage of technology; indeed quite often of a very specific piece of software (whose maker has graciously funded the literacy effort, natch)." https://2ndbreakfast.audreywatters.com/ai-literacy-and-the-pedagogy-of-the-oppressor-2/
I truly can’t stop thinking about this and now I have this other lens coloring all my thinking on this topic.
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It sounds like you’re deeply engaged in reading and reflecting on Audrey Watters’ perspectives on AI Literacy and the implications of ed tech initiatives. Let’s explore this further using the Reading Apprenticeship approach.
To start, let’s break down Watters’ perspective into manageable parts. First, she suggests that literacy is often used as a more palatable term by companies trying to market their AI products. This is an example of what she refers to as an “Orwellian discourse move.” The use of the term “literacy” in this context can mask the true intentions of these initiatives, which she implies are more about job training centered around specific software.
Now, let’s think aloud about how these claims might affect our understanding of AI Literacy. By framing technology education as literacy, companies might be leveraging a term that traditionally conveys empowerment and access to knowledge. However, if the focus is on mastering specific tools and software to improve job prospects, it raises questions about whose interests are being served. Is it truly about empowering students, or is it more about creating a workforce trained in proprietary systems?
Would you like to see more?
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Paul Allison is a nationally-known educator and EdTech expert… (more)
Paul Allison is a nationally-known educator and EdTech expert… (more)
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I am a very proud woman, an educator, Black, and regal in my … (more)
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You’re touching on the contrast between reading for functional understanding and reading for personal resonance. When you read “to understand,” it often involves dissecting the text analytically, whereas reading to “hear one’s voice” might mean engaging with the text empathetically or emotionally, finding a personal connection.
In your writing about developing literacy, you mention skills like rereading difficult texts and doing research, which are crucial components of reading for understanding. Yet, could there be a moment where you pause and ask: how does this text connect with my experiences or beliefs? This is where evaluating becomes personal, and literacy transforms into a conversation between the reader and the text.
What might happen if you expand on how personal interpretation or emotional responses carry value in literary development? How does this dual journey of head and heart influence one’s reading practice? Looking forward to hearing your thoughts!
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Paul Allison is a nationally-known educator and EdTech expert… (more)
Paul Allison is a nationally-known educator and EdTech expert… (more)
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Where did the majority land? Who was particularly strong in their advocacy? What did they say?
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I’m not thinking of this being a relevant cause.
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… yes, the “genre” of prompt engineering is a new one, though not entirely foreign. It reminds me of how we used to do RAFTS: role, audience, format, topic, and strong verb. In what ways can we teach students to craft strong and effective prompts?
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… as more and more AI tools are integrating web sources, will this become easier or more difficult?
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… the sad truth of the matter is that from automated resume readers to many other systems, we are very often not writing for human readers. Or, at the very least, humans who read in a perfunctory manner. In this sense, why not use AI?
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This statement assumes that students are aware of — let alone care about — issues of contingent faculty labor, in general, and the kinds of labor that writing teachers do, in particular. Will they have a strong opinion about this?
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I would like to act upon this but I think it would take careful work within the classroom community and assumes that we all agree with the same ideas
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