Generative Artificial Intelligence in Education, Research, and Professional Communication: Trends and Future Prospects

Abstract

Generative artificial intelligence (GenAI), most visibly large language models (LLMs) such as GPT-3, GPT-4, and their successors, has moved within a few years from a specialized research artifact to a tool embedded across teaching, scholarly research, and workplace communication. This paper reviews the trajectory of generative AI adoption across these three domains, synthesizing evidence on its instructional applications in education, its emerging and contested role in the research and publication pipeline, and its measurable productivity effects in professional writing and knowledge work. The review draws on foundational transformer and large-language-model literature, empirical productivity studies, and the rapidly developing academic-integrity and authorship-policy literature that has followed GenAI's adoption in scholarly publishing. Particular attention is given to the tension between GenAI's demonstrated capacity to accelerate routine writing and drafting tasks and the unresolved questions surrounding authorship attribution, academic integrity, overreliance, and epistemic reliability that have accompanied its adoption. Comparative tables are used throughout to summarize reported use cases, benefits, and risks across the three domains. The paper concludes that GenAI adoption trends are converging toward augmentation rather than replacement across all three domains, and identifies policy standardization, verification tooling, and longitudinal skill-impact research as the central future prospects for the field.