Hands-on AI Pedagogy Showcase: Student Guide to AI Literacy

In the second installment of our Hands-on AI Pedagogy Showcase series, we turn to the pressing question of developing students’ AI literacy. As mentioned in our February teaching tip, students are using AI tools at very high rates: according to a 2024 global survey conducted by the Digital Education Council, 98% of students in higher-ed report using AI in their academic work, with 54% reporting daily or weekly use. While most instructors agree that understanding the benefits and harms of generative AI is essential, few of us have the time and resources to explore the subject in depth with our students. Meanwhile, many students turn to social media, where they become targets of aggressive marketing by AI companies. Corporate messaging from companies competing for the lucrative college market is not likely to promote thoughtful, critical, discipline-specific reflection on academic integrity or GenAI ethics. The 2024 Student Guide to AI Literacy can help faculty counter this messaging by promoting thoughtful, critical engagement (or non-engagement) with GenAI tools.

While all academic fields are affected by the GenAI revolution, some of us are affected in more obvious ways: if you are asking your students to write (either code or natural language), you are on the frontlines of this transformation. Given that Large Language Models (LLMs) were initially designed for natural language processing, it’s not surprising that the Student Guide to AI Literacy, was composed by a group of language- and literacy-focused scholars participating in the Critical AI Literacy for Reading, Writing, and Languages Workshop, an initiative of the MLA-CCCC Task Force on Writing and AI. The document defines seven dimensions of AI literacy, taking an expansive view of “literacy” as not only technical competence (#3: “You know how to prompt GenAI to produce useful outputs”) but also capacity for metacognitive reflection (#5 “You monitor your own learning as you use GenAI tools”) as well as broader social and ethical awareness (#2 “You understand the policies and frameworks for the ethical use of GenAI outlined by your instructors and institutions” and #7 “You understand the potential harms of GenAI, both those inherent to the technology and those that arise from misuse”).

In addition to identifying seven dimensions of GenAI literacy, the document provides a list of 3-5 specific competencies for each. For example, #4 (“You evaluate the relevance, usefulness, and accuracy of GenAI outputs”) is broken down into the following competencies:

• You select strategies appropriate to the context, purpose, and audience for a task.
• You can analyze GenAI outputs to determine whether the results align with the purpose of a task.
• You recognize when using GenAI is not appropriate for a writing or research task.
• You check the accuracy, correctness, and relevance of GenAI outputs against credible sources.

This list underscores the indispensability of skills like assessing the rhetorical situation, fact-checking, and critical analysis for the successful deployment of GenAI tools in academic work. The granular lists of competencies (either in their original form or tweaked to reflect specific learning goals) can be incorporated into assessment rubrics or simply used as checklists for helping students think through their engagement with GenAI tools.

The ready-to-use, two-page Student Guide to AI Literacy is an easy way to start in-class conversations, jumpstart collaborative assignments (e.g., by dividing students into seven groups responsible for researching each of the seven items), or prompt metacognitive and ethical reflection. For an excellent student-facing AI Literacy guide focused more acutely on questions of AI ethics, see Critical AI Literacies: A Guide for Students developed at Rutgers University under the Design Justice Labs initiative. We all have the responsibility to get the conversation started, even as we build our capacity for providing our students with comprehensive training in GenAI literacy.