Amplifying Rural Educators’ Perspectives: A Qualitative Study on the Impacts of Generative AI in Rural U.S. High Schools

Paper Title

Amplifying Rural Educators’ Perspectives: A Qualitative Study on the Impacts of Generative AI in Rural U.S. High Schools

Publication Info

• Topic area: Generative AI adoption and its impacts in rural high school education.
• Keywords: Generative AI, rural education, high school educators, AI literacy, educational technology, critical rural theory, digital divide, professional development, inclusive design, systemic inequities.

Background and Problem

• Problem / challenge: Rural high schools face unique challenges in integrating Generative AI (GenAI) due to limited infrastructure, resource disparities, and a lack of AI literacy training. Existing research has largely overlooked rural contexts, focusing instead on urban and suburban schools.
• Significance: Nearly 10 million U.S. K-12 students attend rural schools, making it critical to address the inequities in educational technology access and integration to avoid widening the digital divide.
• Motivation and related work: Prior studies have explored GenAI’s potential in education, such as personalized learning and administrative support, but these assume adequate resources and infrastructure. Rural schools, with their distinct constraints, remain underrepresented in these discussions, necessitating a focused investigation into their unique needs and perspectives.

Solution

• Proposed approach: A qualitative study involving surveys and interviews with 31 rural high school educators across Arizona, Maine, and North Carolina to explore their experiences, challenges, and visions for GenAI integration.
• Novelty:
  1. First U.S.-based study to focus on rural high school educators’ perspectives on GenAI.
  2. Application of critical rural theory to analyze systemic inequities in GenAI adoption.
  3. Open-sourced methodological toolkit for replicability and future research.
  4. Exploration of speculative GenAI applications tailored to rural contexts.
• Procedure and key techniques:
  • Conducted an online survey with multiple-choice and open-ended questions, followed by semi-structured interviews.
  • Used grounded theory-informed coding and critical rural theory for data analysis.
  • Developed a codebook to identify themes across survey and interview responses.
  • Analyzed educators’ speculative visions for GenAI applications in rural schools.

Results

• Concrete findings:
  • 80.6% of participants use GenAI primarily for administrative and teaching support, such as curriculum development and grading.
  • 31% expressed concerns about GenAI’s potential to inhibit student autonomy and creativity.
  • 79.3% reported low confidence in learning GenAI skills, and 75.9% found it difficult to keep up with new technologies.
  • Participants envisioned GenAI applications like adaptive learning systems, AI-powered curricula incorporating local knowledge, and tools addressing staffing shortages.
• Advantage over baselines:
  • Highlights rural-specific barriers (e.g., unreliable internet, limited devices) and opportunities (e.g., location-based learning, reduced workloads) not addressed in urban-suburban studies.
  • Provides actionable insights for designing inclusive GenAI tools tailored to under-resourced contexts.
• Experiments / evaluation:
  • Survey (N = 29) and interviews (N = 6) with rural educators.
  • Used validated frameworks like the Technology Acceptance Model (TAM) and Computer Anxiety Rating Scale (CARS) to assess perceptions and anxieties.
  • Analyzed qualitative data through thematic coding and critical rural theory.
• Limitations and future work:
  • Limited geographic scope (three states) and small sample size due to recruitment challenges.
  • Potential self-selection bias among participants familiar with GenAI.
  • Future work should expand to include more diverse rural regions, non-STEM educators, and perspectives from students, parents, and community members.

Summary

This study examines the integration of Generative AI in rural U.S. high schools, highlighting systemic inequities, infrastructure barriers, and educators’ low confidence in AI literacy. Despite these challenges, rural educators envision transformative GenAI applications, such as adaptive learning systems and tools for personalized instruction. The findings emphasize the need for rural-specific design approaches, professional development, and inclusive decision-making processes. By centering rural educators’ voices, this research provides actionable insights for creating equitable and contextually relevant educational technologies.