Generative Artificial Intelligence on the Threshold of Educational Transformation: A Systematic Review of Language Model Applications, Opportunities, and Risks in Education (2010–2021)

By early 2022, a decade of rapid progress in generative language modeling — from early sequence-to-sequence neural networks through the transformer architecture, GPT-1/GPT-2/GPT-3, and code- and image-generation systems such as Codex and DALL-E — had positioned generative artificial intelligence (AI) at the edge of mainstream educational adoption, shortly before conversational large language model interfaces would bring the technology to a mass audience. This paper presents a systematic review and bibliometric synthesis of research on generative AI and large language model applications in education published between 2010 and 2021, capturing the field's foundational decade prior to that inflection point. Following PRISMA-guided screening of 3,057 records identified across Scopus, Web of Science, the ACL Anthology, and IEEE Xplore, 134 studies met inclusion criteria and formed the analytic corpus. Bibliometric mapping of publication trends, leading venues, contributing countries, and keyword co-occurrence networks was combined with thematic synthesis of application categories, opportunities, and risks. Results show accelerating output after the 2017 introduction of the transformer architecture and a further inflection following GPT-3's 2020 release, concentrated in a relatively small set of computational-linguistics and educational-technology venues. Automated writing evaluation and essay scoring, dialogue-based conversational tutoring agents, and automatic question generation were the most frequently studied application categories. The most consistently reported opportunities concerned scalable feedback and personalized practice generation, while the most consistently reported risks concerned academic integrity, bias in generated content, and factual inaccuracy (“hallucination”). The review synthesizes these findings into an integrated conceptual map of generative AI's pre-2022 educational footprint and outlines a forward-looking research agenda for the conversational large-language-model era that followed. All literature synthesized and cited in this review was published before 2022, providing a historically bounded account of generative AI in education on the eve of its mainstream arrival.