Artificial intelligence in education (often abbreviated as AIEd) is a subfield of educational technology that studies how to use artificial intelligence (AI) to create learning environments. The field draws on computer science, education, and psychology to design systems that support teaching and learning, ranging from early computer-based instruction to modern large language model (LLM) chatbots. Key considerations include data-driven decision-making, AI ethics, data privacy, and AI literacy, while concerns involve potential cheating, over-reliance, equity of access, reduced critical thinking, and the perpetuation of misinformation and bias.
AIEd has evolved alongside broader advances in artificial intelligence and machine learning. Its history reflects shifts from rule-based systems to data-driven approaches, and more recently to generative models. The field's theoretical foundations integrate educational theory with technical methods, and its applications span both educator and student use cases, often raising debates about appropriate integration and societal impact.
History
Efforts to integrate AI into educational contexts have often followed technological advancement in the history of artificial intelligence. In the 1960s, educators and researchers began developing computer-based instruction systems, such as PLATO, developed by the University of Illinois. PLATO was an early system that offered interactive lessons and was used in various educational settings, laying groundwork for later AI applications.
In the 1970s, AI techniques were added to computer-assisted instruction (CAI) to form intelligent tutoring systems (ITS). These systems aimed to provide adaptive feedback and personalized learning paths. The LISP Tutor, for example, was evaluated in a Carnegie Mellon University mini-course in the fall of 1984, demonstrating that ITS could improve learning outcomes compared to traditional instruction.
The International Artificial Intelligence in Education (AIED) Society was founded in 1993, comprising researchers with backgrounds in computer science, education, and psychology. The society produces the International Journal of Artificial Intelligence in Education (IJAIED), which publishes research on AIEd theory and applications. This institutionalization helped consolidate the field and foster interdisciplinary collaboration.
Coinciding with the AI boom of the 2020s, the use of large language models (LLMs) in the global north has been promoted and funded by venture capital and big tech. Companies creating AI services have targeted students and educational institutions as customers. Similarly, pre-AI boom educational companies have expanded their use of AI technologies. These commercial incentives for AIEd use may be related to a potential AI bubble. In the U.S., bipartisan support of AI development in K-12 education has been expressed, but specific implementations and best practices remain contentious.
Platforms
Modern AIEd platforms often rely on generative AI chatbots, which can engage in natural language conversation and assist with various tasks. Notable examples include OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini. These tools have been rapidly adopted in educational settings, though their integration has been uneven and debated.
ChatGPT
ChatGPT was released by OpenAI in November 2022, and its adoption in education was rapid. Within months, students and educators began using it for writing assistance, problem-solving, and content generation. However, the tool was banned by several institutions, particularly in K-12 schools and some universities, due to concerns about academic integrity.
Students have generally reported positive perceptions of ChatGPT, finding it useful for brainstorming, summarizing information, and providing quick answers. However, specific views from educators and students vary widely. Opinions are especially varied on what constitutes appropriate use of ChatGPT in education, with some seeing it as a legitimate learning aid and others viewing it as a shortcut that undermines skill development.
Efforts to ban chatbots like ChatGPT in schools focus on preventing cheating, but enforcement is hampered due to AI detection inaccuracies and widespread accessibility of chatbot technology. Many AI detection tools have been shown to produce false positives, incorrectly flagging original student work as AI-generated. Educators have also expressed concern that overreliance on the tool may foster superficial learning habits, erode critical thinking, and propagate misinformation. Some educators have proposed ways to integrate generative AI (GenAI) into assessments, such as having students critique AI-generated outputs or use AI for initial drafts followed by human revision.
Claude
Claude is a series of LLMs developed by Anthropic. Its AI-based chatbot was released in March 2023. Claude has been positioned as a safer and more interpretable alternative to other chatbots, with a focus on reducing harmful outputs. In educational contexts, Claude has been used for tutoring, feedback, and content creation.
In July 2026, Anthropic launched a version of Claude for teachers. Colleges and universities that offer "Claude for Education" include Stanford University, Columbia University, and others. This initiative provides tailored features such as lesson planning assistance, grading support, and student-facing tutoring, aiming to integrate Claude into academic workflows.
Gemini
Gemini is a GenAI chatbot and virtual assistant developed by Google in December 2023. It was designed to compete with ChatGPT and Claude, offering multimodal capabilities that can process text, images, and audio. In educational settings, Gemini has been used for research assistance, writing support, and interactive learning.
In August 2024, Google launched Gemini Gems, a customizable version of the tool, for educators. Gems allow users to create specialized versions of Gemini tailored to specific tasks, such as quiz generation or lesson planning. It was added to "Google Workspace for Education" accounts in March 2025, making it accessible to many schools and universities. In August 2026, Google added Gemini to its Classroom app for select schools at the elementary through high school level, further embedding AI into everyday educational tools.
Theory
AIEd applies theory from education studies, machine learning, and related fields. The design of AI systems in education is informed by pedagogical principles, cognitive science, and technical considerations. A 2019 review of the previous decade of studies found that most research prioritized technological design over pedagogical integration, suggesting a gap between technical development and educational practice.
Ouyang and Jiao (2021) propose three paradigms for AI in education, which follow roughly from least to most learner-centered and from requiring least to most technical complexity from the AI systems:
- AI-directed, learner-as-recipient: AIEd systems present a pre-set curriculum based on statistical patterns that do not adjust to learner's feedback. This paradigm is common in early ITS and adaptive learning platforms, where the AI controls the learning path.
- AI-supported, learner-as-collaborator: Systems that incorporate responsiveness to learner's feedback through, for example, natural language processing, wherein AI can support knowledge construction. Here, the AI acts as a partner that responds to student input, facilitating dialogue and exploration.
- AI-empowered, learner-as-leader: This model seeks to position AI as a supplement to human intelligence wherein learners take agency and AI provides consistent and actionable feedback. The learner directs the learning process, with AI offering tools and insights to enhance their understanding.
Some scholars place AI in education within a socio-technical framework. This positions AI alongside other emerging educational technologies, such as computing, the internet, and social media. Such a perspective emphasizes that AIEd is not merely a technical tool but is embedded in social, cultural, and political contexts.
The framework of Tsao, Heinrichs and Camit (2025) draws on new materialism and posthumanism, specifically Donna Haraway's concept of sympoiesis (making-with). This perspective views learning as an entanglement of human and non-human actors (students, teachers, and AI algorithms), where knowledge is co-composed in contact zones between human context and algorithmic prediction. It challenges traditional views of learning as an individual cognitive process, instead seeing it as a distributed and relational phenomenon.
AI agents have been trained on biased datasets and thus continue to perpetuate societal biases. Since LLMs were created to produce human-like text, algorithmic bias can be introduced and reproduced. For example, if training data reflects historical inequalities, the AI may generate outputs that reinforce stereotypes or exclude certain groups. AI's data processing and monitoring reinforce neoliberal approaches to education rather than addressing inequalities, as they often prioritize efficiency, measurement, and individual accountability over systemic change.
Applications
Educators
Uses of generative AI chatbots in education have included assessment and feedback, machine translations, proof-reading, exam question generation, and copy editing, or as virtual assistants. For instance, teachers may use AI to generate practice problems, provide automated feedback on student essays, or translate materials for multilingual classrooms. AI can also assist in administrative tasks, such as drafting emails or creating syllabi.
Emotional AI in education is the study and development of systems that can detect learners' emotions or provide emotional support in learning. These systems might use facial recognition, physiological sensors, or text analysis to infer emotional states, with the goal of adapting instruction or offering encouragement. However, such technologies raise privacy and ethical concerns.
Broad and unplanned use of AI in education administration may lead to harms such as discrimination through superficial categorization of student records, biases in automated grading toward students whose work fits preplanned algorithmic definitions, and exclusion of students from AI training whose previous experiences have not prepared them with requisite computer literacies. For example, an AI grading system might penalize non-native English speakers or students with different writing styles. However, more structured uses have successfully incorporated generative AI into inquiry-based workflows in which university students use AI for exploration and critique while independently verifying claims against scholarly sources and incorporating expert feedback.
Students
A global survey from 2024 found that students primarily use generative AI for brainstorming, summarization, and research assistance, finding it effective for simplifying complex information but less reliable for factual accuracy and classroom learning. While students recognized its potential to enhance AI literacy and digital communication, professors highlighted significant limitations in critical thinking, interpersonal communication, and decision-making skills. In a global study, approximately 58% of students reported finding the AI tool useful in their daily lives. According to the survey, ChatGPT was particularly beneficial in helping students understand difficult concepts, generate ideas for assignments, and manage their time, but it was less effective for tasks requiring deep analysis or nuanced judgment.
The use of AI by students also raises questions about academic integrity and the development of independent learning skills. Some institutions have developed policies that allow AI use under certain conditions, such as requiring students to disclose AI assistance or to use AI only for specific stages of the writing process. Others have redesigned assessments to be more AI-resistant, such as incorporating oral presentations or in-class writing tasks.
Challenges and Future Directions
AIEd faces several ongoing challenges, including ensuring equity of access to AI tools, addressing algorithmic bias, and maintaining human oversight. There is also a need for teacher training to effectively integrate AI into pedagogy. Future research is likely to focus on developing more transparent and interpretable AI systems, exploring personalized learning at scale, and understanding the long-term effects of AI on cognitive development and social skills.
As AI technologies continue to evolve, the field of AIEd will need to adapt, balancing innovation with ethical considerations. The involvement of multiple stakeholders, including educators, students, policymakers, and technology developers, will be crucial in shaping the future of AI in education.