Date
Publisher
Journal of Adolescence & Adult Literacy
Educators hold diverse beliefs and attitudes about generative artificial intelligence (AI). Irrespective of their stance, many acknowledge AI's growing influence and the pressing need for greater AI literacy. In this case study, we draw on Davis's (1989) technology acceptance model (TAM) to examine how two English teachers, Fiona and Margot, arrived at different enactments of AI literacy. Using qualitative methods, we found that Fiona was primarily concerned with cheating, held negative attitudes toward AI, and taught a standalone lesson on preventing cheating. Margot identified a range of AI risks and possibilities, held conflicted attitudes, and developed a unit on the societal impacts of AI. Our findings suggest that educators and professional learning designers should take concerns about cheating seriously, but also adopt a broader, dialectical orientation that prepares students to understand, critically evaluate, and use AI. As AI reshapes education, these skills are crucial to students' success and well-being.
Study at a glance
- Question: How do English teachers' attitudes toward AI influence how they teach AI literacy?
- Setting and data: Two English teachers (Fiona and Margot)
- Method: Comparative case study using qualitative methods
Why it matters
Educators hold diverse beliefs and attitudes about generative AI, but regardless of their stance, many acknowledge its growing influence and the pressing need for greater AI literacy.
Key findings
- In a case study of two English teachers, one educator with negative attitudes and cheating concerns taught a single lesson on preventing cheating.
- Another English teacher held conflicted attitudes, weighed both risks and possibilities, and developed a unit on generative AI's societal impacts.
- Regardless of their stance on generative AI, educators acknowledged its growing influence and reported a pressing need for greater AI literacy.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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