EduAgent: Generative Student Agents in Learning
Songlin Xu Xinyu Zhang Lianhui Qin
Student simulation in online education is important to address dynamic
learning behaviors of students with diverse backgrounds. Existing simulation
models based on deep learning usually need massive training data, lacking prior
knowledge in educational contexts. Large language models (LLMs) may contain
such prior knowledge since they are pre-trained from a large corpus. However,
because student behaviors are dynamic and multifaceted with individual
differences, directly prompting LLMs is not robust nor accurate enough to
capture fine-grained interactions among diverse student personas, learning
behaviors, and learning outcomes. This work tackles this problem by presenting
a newly annotated fine-grained large-scale dataset and proposing EduAgent, a
novel generative agent framework incorporating cognitive prior knowledge (i.e.,
theoretical findings revealed in cognitive science) to guide LLMs to first
reason correlations among various behaviors and then make simulations. Our two
experiments show that EduAgent could not only mimic and predict learning
behaviors of real students but also generate realistic learning behaviors of
virtual students without real data.