AI for education: Trends and insights

Artificial intelligence (AI) in education is experiencing a transformative shift, fueled by foundation models with unprecedented capabilities. These advancements are reshaping educational paradigms and addressing challenges such as diverse student needs, resource gaps, and engagement.1 This paper examines three key trends: the shift from perception to cognition, the transition from generalized to personalized learning, and the rise of multimodal systems, as shown in Figure 1. Together, these trends open up new opportunities to tackle persistent challenges in the education sector.

Evolution trends in AI for education

From perception to cognition

The transition of AI from perception-based to cognition-driven systems represents a significant milestone in the AI-for-education field.2 Early AI applications were primarily focused on perception: understanding basic interactions and responding to student queries. However, with the advent of foundation models, AI has developed higher-order cognitive abilities, such as context understanding, knowledge reasoning, and complex problem solving. Organizations like DeepSeek have introduced models capable of reasoning at a level comparable to that of human instructors. These models can understand complex contexts, perform multi-step reasoning, and analyze student queries in depth rather than simply providing straightforward answers. For example, consider the use of AI tutors like DeepSeek’s model in biology courses. While AI can generate detailed explanations of cellular processes, students often struggle to trace the reasoning behind intricate biochemical pathways, which can lead to a superficial understanding. This shift transforms AI from a basic informational tool into an intelligent mentor, fostering independent thinking and creative problem-solving skills. However, current research on cognitive AI faces significant challenges, particularly regarding model transparency. The lack of clear reasoning paths complicates students’ understanding of the "how" and "why" behind conclusions, a crucial aspect in educational contexts. This limitation hinders reflective learning and deep conceptual understanding. Moreover, current AI systems struggle to integrate diverse knowledge sources to support complex knowledge construction, thus limiting the depth of cognitive growth for learners.