REVIEW 6 cited by
EduAgent: Generative Student Agents in Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 6 Pith papers
-
CoderAgent: Simulating Student Behavior for Personalized Programming Learning with Large Language Models
A new LLM-based agent, CoderAgent, simulates students' iterative programming process (why, how, where, what to modify) and outperforms baselines on predicting next code edits, though gains are modest.
-
From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization
EduVisAgent, a five-agent framework, outperforms all baseline AI models at generating pedagogically effective interactive visualizations for STEM problems, according to the new EduVisBench benchmark and its GPT-4o-bas...
-
Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation
LASEV, a multi-agent LLM system that compiles structured 'executable video scripts' into educational videos, reports 92-96% expert-rated publishable quality and more than one million videos per day at 95% lower cost.
-
Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment
WikiHowAgent generates 114,296 simulated teacher-learner conversations from 14,287 WikiHow tutorials and evaluates their pedagogic quality with LLM and human judges.
-
InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation
InqEduAgent fits a Gaussian process to simulated collaboration gains and then uses a Pareto front to pick learning partners, reporting small average gains over random pairing on six CMMLU domains.
-
AgentSME for Simulating Diverse Communication Modes in Smart Education
A one-round peer-exchange protocol improves accuracy of six LLMs on a Chinese sociology multiple-choice benchmark, and DeepSeek shows the largest lexical diversity.
Discussion (0). Continue with ORCID to comment.