Artificial intelligence in education (AIEd) is an established research and industry field focused on intelligent tutoring, adaptive learning, educational data mining, automated assessment, language technologies, learning analytics, and personalized recommendations. A large bibliometric review of 4,519 publications from 2000–2019 identified intelligent tutoring for special education, natural-language processing for language learning, educational robots, performance prediction, emotion detection, and recommender systems as major research themes. (Chen, 2022)

Short article summaries

• Luckin and Holmes, Intelligence Unleashed: An argument for AI in Education (2016). This article explains how AI can make knowledge and learning processes more explicit and support personalized, flexible, inclusive learning. Its central argument is that AI should augment teachers rather than replace them, through tools such as one-to-one tutoring, group-learning support, adaptive assessment, and lifelong learning companions. (Luckin, 2016)

• Woolf, AI and Education: Celebrating 30 Years of Marriage (2015). Woolf presents personalization and the teaching of twenty-first-century skills as major educational challenges for AI. The article describes intelligent instructional systems that personalize teaching, improve student experiences, and generate data that researchers can use to study learning behavior. (Woolf, 2015)

• Luckin and Cukurova, Designing educational technologies in the age of AI: A learning sciences-driven approach (2019). The authors argue that educational AI should be designed jointly by AI developers, educators, and learning-science researchers. Their framework emphasizes using educational theory to interpret rich student data and provide more individualized support. (Luckin, 2019)

• Chen, Zou, Xie, Cheng, and Liu, Two Decades of Artificial Intelligence in Education (2022). This review maps the field’s growth and research collaborations using bibliometric analysis. It is useful for identifying research directions, including intelligent tutoring, educational data mining, discourse analysis, affective computing, and personalized learning. (Chen, 2022)

• Kelkar, Between AI and Learning Science: The Evolution and Commercialization of Intelligent Tutoring Systems (2022). Kelkar traces intelligent tutoring systems, which model a learner’s progress and provide interventions during educational tasks. The article shows how these systems evolved from student-facing aids into tools intended to support both teachers and students, enabling wider school adoption. (Kelkar, 2022)

Researchers and companies to study

Important researchers and authors to investigate include Rosemary Luckin, Wayne Holmes, Beverly Park Woolf, Mutlu Cukurova, Shreeharsh Kelkar, and the review team of Xieling Chen, Di Zou, Haoran Xie, Gary Cheng, and Caixia Liu. Their work covers the foundations of AI in education, learning-science-informed design, intelligent tutoring systems, personalization, and the development of the research field. (Luckin, 2016) (Woolf, 2015) (Luckin, 2019) (Kelkar, 2022) (Chen, 2022)

Companies identified in the literature include iFLYTEK, which is discussed as a major Chinese AI-in-education enterprise involved in smart classrooms, smart examinations, speech assessment, adaptive learning, and intelligent educational terminals. (Wan, 2021) New Oriental Group, Tomorrow Advancing Life (TAL), and Squirrel AI are also examined as prominent private education companies participating in China’s educational-AI market. (Knox, 2020) These companies illustrate the commercial side of the field, while the cited researchers provide useful academic foundations for building an educational AI system.

For your project, the strongest starting point is to study intelligent tutoring systems, adaptive learning, and learning-science-informed design together. Reviews also emphasize that privacy, ethics, teacher roles, and collaboration between educators and developers must be addressed alongside technical performance. (Zhang, 2021) (Zhai, 2021)