Publication: BEYOND CHEATING: CULTIVATING AI LITERACY THROUGH WRITING AND ASSESSMENT FRAMEWORKS
| dc.contributor.author | Roderick, Ryan | |
| dc.date.accessioned | 2025-07-17T19:29:21Z | |
| dc.date.issued | 2025-04-17 | |
| dc.description.abstract | As generative AI technologies become increasingly integrated into educational environments, it is essential to consider how they shape human learning, creativity, and decision-making. While generative AI presents significant opportunities for augmenting knowledge creation, it also introduces challenges around ethical use, assessment, and pedagogy. To address these complexities, this poster presentation focuses on the evolving concept of AI Literacy—a broad and multifaceted skill set that includes prompt engineering, human-AI co-creation, ethical AI engagement, and AI-augmented higher order thinking. Drawing on frameworks from rhetoric and composition studies, I define AI Literacy as a social and rhetorical competency: the ability to critically interact with AI systems, understand their embedded biases, and navigate their use within specific contexts. Rather than treating AI as a replacement for human cognition, I consider how it can supplement and extend it by building on Bloom’s Taxonomy. A widely adopted framework in educational assessment, Bloom’s Taxonomy offers a starting point for exploring how learners collaborate with AI through iterative prompting, evaluation, and adaptation. By examining these interactions, we uncover strategies for recognizing and cultivating AI Literacy in educational settings. This work aims to support educators in designing learning experiences that promote critical, ethical, and effective engagement with generative AI. | |
| dc.identifier.uri | https://dspace.husson.edu/handle/20.500.14298/317 | |
| dc.language.iso | en_US | |
| dc.title | BEYOND CHEATING: CULTIVATING AI LITERACY THROUGH WRITING AND ASSESSMENT FRAMEWORKS | |
| dc.type | Poster | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | a25338e0-9fe7-4c7d-a950-796408251a27 | |
| relation.isAuthorOfPublication.latestForDiscovery | a25338e0-9fe7-4c7d-a950-796408251a27 |