The Impact of Large Language Models on Education and Research

The rise of large language models (LLMs), such as ChatGPT and similar AI tools, has triggered one of the most rapid and far-reaching changes in both education and research. These technologies, trained on vast amounts of human language data, can now generate coherent, fluent text on almost any topic. While this sounds like science fiction, it’s already a daily reality in classrooms, universities, and labs around the world. The question is no longer whether they’ll change how we learn and discover—but how we adapt to them.

In education, LLMs have opened up new forms of access. Students who once struggled to understand a concept can now receive instant explanations in simpler terms. They can ask follow-up questions without fear of judgment, work through problems at their own pace, or practice writing with guided feedback. For many, these tools feel like having a tutor available 24/7. This has the potential to level the playing field, especially for those in under-resourced schools or remote areas.

Teachers, too, have begun to embrace these tools. Some use LLMs to create worksheets or quizzes, while others rely on them to help brainstorm lesson plans or summarize dense texts. Rather than replacing educators, these models can act like creative partners—saving time on repetitive tasks so teachers can focus more on engaging students in discussion and critical thinking.

In research, the effects are just as significant. Graduate students and scholars can now scan through articles faster, extract summaries, and get help refining research questions. LLMs can assist with coding, drafting abstracts, and even translating technical content into more readable language. For interdisciplinary work, where the biggest breakthroughs often happen, these tools help bridge gaps between fields that traditionally spoke in very different "languages."

However, not all impacts are positive. One of the most pressing issues is academic honesty. Students can easily misuse LLMs to write essays or complete assignments without actually learning the material. This puts pressure on schools to rethink how they assess understanding. Relying solely on written work might no longer be enough; teachers may need to introduce more oral exams, in-class reflections, or creative projects that make cheating harder and understanding more visible.

There’s also the question of reliability. LLMs don’t “know” anything in the human sense. They generate responses based on patterns in data—not deep understanding. That means they can sometimes provide incorrect or misleading information, especially on niche or sensitive topics. Relying on them blindly can lead to misunderstandings or the spread of misinformation.

Another concern is bias. Since these models are trained on data from the internet, they reflect the same biases, stereotypes, and blind spots that exist online. Without awareness, users may unknowingly absorb these biases. It’s crucial that students and researchers continue to think critically about their sources, even when those sources sound convincing.

Ultimately, large language models are tools—and like any tool, their value depends on how we use them. If used thoughtfully, they can enhance learning, boost creativity, and speed up the research process. But if we rely on them too heavily or uncritically, we risk losing some of the deeper skills that education is meant to foster: questioning, analyzing, and forming our own ideas.

In this new era, the role of educators is more important than ever—not just to teach content, but to guide students in how to think clearly and ethically in an AI-saturated world. And for researchers, the challenge will be to use these tools to enhance insight, not replace it. The real power of LLMs lies not in what they can write for us, but in how they can help us think better for ourselves.

Works Cited

Bang, Yejin, et al. Do Language Models Have Deep Knowledge?. MIT AI Policy Lab, 2023, https://arxiv.org/abs/2301.00580

Berger, Kevin. “The AI Plagiarism Arms Race Has Begun.” The Chronicle of Higher Education, 2023, https://www.chronicle.com/article/the-ai-plagiarism-arms-race-has-begun.

Kasneci, Enkelejda, et al. “ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education.” Computers and Education: Artificial Intelligence, vol. 4, 2023, https://doi.org/10.1016/j.caeai.2023.100100

Stokel-Walker, Chris. “ChatGPT Listed as Author on Research Papers: Many Scientists Disapprove.” Nature, vol. 613, no. 7945, 2023, pp. 620–621,   https://www.nature.com/articles/d41586-023-00107-z

Stanford Human-Centered Artificial Intelligence (HAI). How Large Language Models Reflect and Amplify Bias. Stanford University, 2023, https://hai.stanford.edu/news/how-large-language-models-reflect-and-amplify-bias

UNESCO. Generative AI and the Future of Education: Guidance for Policymakers. United Nations Educational, Scientific and Cultural Organization, 2023, https://unesdoc.unesco.org/ark:/48223/pf0000386558