## 3 Research about Impact of AI on Education
### Large-scale AI Applications in the Future of Education  
AI's potential in education extends beyond current applications. As technology continues to advance, the possibilities for AI in education are expanding. Future applications could include more sophisticated adaptive learning systems, AI-enabled virtual reality experiences, and AI-driven educational games. This section will discuss four examples of large-scale AI applications for education: personalized learning experiences, adaptive learning, predictive analytics, and chatbots for learning and research. Each of these four innovations can be seen as a longer-term application of AI, though some have already reached technological feasibility through developments including large language models, such as ChatGPT [50]. However, the feasibility of using these technologies to revolutionize higher education will only improve in years to come. Personalized learning experiences use AI to create customized learning content that caters to the learner's background and abilities, ensuring relevance [51; 52]. This innovation has the potential to challenge learners in their zone of proximal development while also providing culturally relevant examples that are beneficial for international students [53]. However, such a learning design would impact the instructional design considerations on the part of the educator, where the AI would design learning experiences (lightening the load on the educator) but would necessitate greater oversight for each student (increasing the need for personalized assistance) [54]. Consider two learners: one is an international student from India, and the other is a domestic student at a US university. Both could receive assignments on a common topic, such as the political theory of democracy, but with more relevant examples of their background and experience. Democracies may look different in different countries, and providing such personalized learning experiences can enhance their understanding. Additionally, there may be students from various disciplinary backgrounds, such as journalism and teaching majors, within the political science class. Examples of the political theory of democracy framed within the lens of these disciplines may better equip students with relevant knowledge. Finally, it may be possible to have undergraduate and graduate students in the same course, or majors and non-majors, where the challenge of the content can be reflective of the differences in these students, their current position as a student, and their future goals. Adaptive testing would allow for a more meaningful understanding of students' abilities and enhance the feedback provided to students [55; 56]. The complexity of questions could vary based on responses to prior questions to assess the knowledge and abilities of the student more fully [57]. Different aspects of the question could be evaluated separately, such as syntax and vocabulary in a writing sample. The ability to apply knowledge in disciplines or professions relevant to the student (such as journalism versus education) could be assessed. This functionality would allow educators and future employers to understand students holistically and prospective employees' strengths and weaknesses in various areas rather than just broad areas, for example with standard entrance exams, such as the SAT/ACT and GRE [58]. Additionally, it could allow test questions to be tailored to students' language abilities and cultural context [59]. Consider two learners again: one is an undergraduate journalism major, and the other is a doctoral-level literature major. These two learners come from vastly different academic backgrounds, and the assessment of their learning should reflect this diversity. In order to more accurately evaluate their abilities and knowledge, it is crucial to assess these students differently and understand whether they can apply course knowledge to their unique disciplinary contexts. Adaptive testing could be a powerful tool to help achieve this goal [60].