Media Framing of Artificial Intelligence in Education: A Content Analysis of US News Coverage

Purpose: To examine how mainstream US media framed generative artificial intelligence (AI) in education during its early diffusion, assessing whether coverage emphasized problem definition and moral evaluation over pedagogical solutions and institutional innovation. Method: A content analysis was conducted using data from the Dow Jones Factiva database. A total of 2,369 items published between January 2023 and September 2025 were screened, resulting in 542 articles with substantive focus on education after applying exclusion criteria. Coding was performed using Dedoose (Version 10), enabling thematic categorization and iterative comparative analysis. In total, 3,469 coded mentions were analyzed. Findings: Risk-oriented framing was most prominent (21.6%), including themes such as misinformation, hallucinations, privacy, and academic integrity. This was followed by public perception and debate (15.0%) and industry/education technology (13.4%). Coverage consistently highlighted both opportunities (e.g., tutoring, drafting efficiency, productivity gains) and the need for precaution, with greater emphasis on governance and safety than on pedagogy or professional learning. Conclusion: Early media discourse framed AI in education primarily as a policy and risk management issue, with pedagogical transformation remaining secondary. Understanding these framing patterns can support educators and policymakers in balancing precaution with innovation adoption. Originality/Value: This study provides empirical evidence on media framing patterns during a critical phase of generative AI diffusion, contributing to discussions on governance, public perception, and the integration of emerging technologies in education.