The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future

2025-07-02

AI Summary

The rise of large language models (LLMs) is fundamentally reshaping educational technology paradigms, yet systematic analysis of their impact on learning interactions, user expectations, and pedagogical design remains fragmented. Method: Drawing on multi-context empirical studies since 2022 and an interdisciplinary approach—integrating LLM-driven NLP, generative AI, human–computer interaction design, and educational data modeling—we systematically map LLM applications in education, delineate their empirical efficacy boundaries, and identify core challenges. Contribution/Results: We propose the “natively natural-language-interaction” paradigm as a foundational shift in educational technology, redefining learner–system interaction logic. We further introduce a forward-looking design framework for the LLM era, explicitly articulating critical bottlenecks—including trustworthiness, adaptive personalization, and assessment alignment—and corresponding developmental pathways. Findings indicate that LLMs are rapidly becoming the default infrastructure for educational interaction, substantially raising baseline user expectations regarding system responsiveness, semantic comprehension, and contextual adaptability.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Interaction Techniques and Devices

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large language Models have only been widely available since 2022 and yet in less than three years have had a significant impact on approaches to education and educational technology. Here we review the domains in which they have been used, and discuss a variety of use cases, their successes and failures. We then progress to discussing how this is changing the dynamic for learners and educators, consider the main design challenges facing LLMs if they are to become truly helpful and effective as educational systems, and reflect on the learning paradigms they support. We make clear that the new interaction paradigms they bring are significant and argue that this approach will become so ubiquitous it will become the default way in which we interact with technologies, and revolutionise what people expect from computer systems in general. This leads us to present some specific and significant considerations for the design of educational technology in the future that are likely to be needed to ensure acceptance by the changing expectations of learners and users.

Problem

• Impact of LLMs on education and technology approaches
• Design challenges for effective LLM educational systems
• Future educational technology design considerations

Innovation

• Reviewing LLM use cases in education
• Analyzing LLM impact on learning dynamics
• Proposing future EdTech design considerations