Generative Artificial Intelligence in Academic Writing: Opportunities, Challenges, and Implications for English Language Education

Abstract

Generative artificial intelligence is transforming academic writing and English language education by providing adaptive support across brainstorming, outlining, drafting, revision, feedback, and assessment. This review examines its conceptual foundations, technological evolution, applications, pedagogical opportunities, effects on second-language writing development, implications for learner agency, ethical risks, teacher roles, and assessment practices. Current evidence indicates that generative AI can improve linguistic accuracy, coherence, confidence, engagement, autonomy, and access to individualized feedback, particularly for English as a foreign or second language learners. Its value depends on purposeful instructional design, critical evaluation, transparent disclosure, and sustained teacher guidance. Major concerns include hallucinated information, fabricated references, bias, privacy risks, unequal access, weakened independent reasoning, uncertain authorship, and academic integrity violations. Detection-focused responses remain insufficient because AI classifiers may produce inconsistent or inaccurate judgments. More defensible educational approaches include process-based assessment, staged drafting, reflective commentaries, prompt documentation, source verification, and oral defense of submitted work. Teachers require technological, pedagogical, ethical, and assessment-related competencies to integrate AI responsibly. Institutions also need clear governance frameworks that distinguish legitimate assistance from intellectual substitution. Generative AI should function as a scaffold rather than an autonomous author. Its long-term educational contribution will depend on equitable access, evidence-based policy, validated AI-literacy measures, and longitudinal research across diverse linguistic, cultural, and institutional contexts.