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 arrive at different enactments of AI literacy instruction based on their attitudes?
- Setting and data: Two English teachers, Fiona and Margot; time and location not stated in the abstract
- Method: Comparative case study using qualitative methods and Davis's technology acceptance model
Why it matters
Educators hold diverse beliefs about generative AI, yet many acknowledge its growing influence and the pressing need for greater AI literacy.
Key findings
- One teacher, Fiona, held negative attitudes toward generative AI, was primarily concerned with cheating, and taught a standalone lesson on preventing cheating.
- Another teacher, Margot, held conflicted attitudes, identified various risks and possibilities of AI, and developed a unit addressing its societal impacts.
- The two teachers' differing beliefs and attitudes toward generative AI led to distinct approaches to enacting AI literacy instruction.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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