From Theory to Practice: An Ecologically Informed Approach to Designing, Implementing, and Evaluating AI Literacy in First-Year Writing
Dissertation Proposal Title: From Theory to Practice: An Ecologically Informed Approach to Designing, Implementing, and Evaluating AI Literacy in First-Year Writing
Dissertation Committee: Dr. Betul Czerkawski (Chair), Dr. Julieta Fernandez, Dr. Shelley Staples, Dr. Chris Tardy
This will be a private dissertation proposal presentation. Thank you in advance for respecting the student's privacy.
Abstract: As generative AI technologies become increasingly integrated into higher education, educators worldwide are being called to rethink traditional pedagogical approaches and develop new literacies that prepare students for AI-mediated communication environments. However, current AI literacy frameworks tend to focus on technical skills or ethical considerations in isolation, often overlooking the complex, situated nature of students’ learning experiences—especially in non-STEM contexts such as first-year writing (FYW) courses. Grounded in Ecological Systems Theory, this proposed study introduces a novel AI literacy framework designed to address this gap by promoting critical, reflective, and ethically grounded engagement with AI, while attending to the multiple, interconnected contexts that shape students’ academic, professional, and everyday experiences.
To empirically test the framework, the researcher will design and implement an 8-week AI-integrated writing module within FYW courses, aligning the module content with existing course learning outcomes. The module includes a structured sequence of instructional activities and discussions that encourage students to engage with AI tools in meaningful and critical ways.
Using a mixed-methods approach, the study will gather data from pre- and post-course surveys, weekly student diaries, and post-module interviews. Quantitative analysis will include descriptive and inferential statistics from survey data and may involve computational text analysis techniques—such as sentiment analysis or topic modeling—to examine patterns in student diaries, while qualitative data will undergo thematic analysis using a hybrid inductive and deductive coding approach. Overall, this research seeks to contribute a theoretically grounded and practically replicable model for AI literacy instruction in writing education. It also seeks to inform broader conversations about how higher education can foster ethical and sustainable approaches to teaching and learning with AI.
