Amplifying Rural Educators' Perspectives: A Qualitative Study of Generative AI's Impact in Rural U.S. High Schools

Abstract

Recent breakthroughs in Generative AI (GenAI) are reshaping educational landscapes, presenting challenges and opportunities. While all contexts present unique challenges, rural schools are historically under-resourced, facing persistent technology-related barriers. To understand and reduce these barriers, we studied 31 rural high school educators across three U.S. states to examine their use of GenAI and understand how GenAI introduces new challenges, opportunities, and may exacerbate existing educational barriers. Results show while rural educators use GenAI to streamline teaching tasks, existing resource disparities restrict meaningful integration. Through rural educators' voices, we reveal issues like infrastructure barriers, resistance to adoption, and lack of AI literacy training create significant obstacles. Nonetheless, educators envision GenAI can support themselves and their students, but findings emphasize the need for rural-specific design approaches. As a community, embracing inclusive GenAI design and re-examining assumptions about technology adoption in under-served educational contexts is essential to reducing barriers rather than widening them.

Figures (6)

• Figure 1: Participant demographics for the online survey ($N=29$). Rectangle widths and internal numbers indicate the count of participants who self-reported each demographic category.
• Figure 2: Interview participants demographics ($N=6$). Two participants did not complete the survey, so only demographic information known for all interviewees is reported.
• Figure 3: Our codebook was developed by deductively coding the interview data. This codebook was then used to code the open-ended survey questions with the same nuanced context. We counted the number of times these codes were referred to in the survey open-ended responses, which is represented by bar charts.
• Figure 4: Distribution of responses to our modified Technology Acceptance Model (TAM) (left) davis1989perceived and Computer Anxiety Rating Scale (CARS) (right) heinssen1987assessing questionnaires. Participants rated their perceptions and attitudes on 7-point and 5-point Likert scales, respectively.
• Figure 5: The top four speculative GenAI use cases envisioned by participants for themselves and their students. These use cases drew on applications identified in non-rural contexts abolnejadian2024leveragingbelghith2024testinghan2024teachersoh2024exploringshahriar2023andtan2024moretaneja2024jillyang2023pair, survey open-ended responses, and interview insights. We include the number of participants who agreed with each use case.