Project-Based Learning (PBL) is an important learning method that promotes understanding and acquiring practical skills through training learners through a project. However, effective PBL often requires sustained orchestration and collaboration, but existing LLM-based learning tools provide partial assistance without explicitly modeling these roles, and overly comprehensive help provided by LLM can reduce learner autonomy. We propose SimPBL, a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. We conduct comprehensive evaluation to study the effectiveness of SimPBL, where we observe a 14% improvement in learner examination score. Results from extensive studies further highlights the ability of SimPBL to manage learning behavior and improve learning experience. Code and materials are available at https://anonymous.4open.science/r/SimPBL-D5B8.

Accurate assessment of critical thinking is historically limited by the Intention Behavior Gap in psychology: the disconnect between what individuals self-reported disposition and their actual practical behaviors. We try to bridge this gap with MASA (Multi-Agent Scenario-based Assessment), a framework that operationalizes cognitive assessment into an interpretable and interactive multi-agent workflow with Assessment Chain-of-Thought (AsCoT). Validating on both large-scale simulations (N=1,161) and human participants (N=70), we find that MASA aligns better with human expert ratings (r=0.882) than traditional gold-standard inventories (r=0.720), with an average cost of only 0.41 per participant. These results suggest that by shifting from self-report inventory to behavior-grounded dialogue, MASA offers a more accurate, cost-effective, and transparent solution for real-world cognitive evaluation.

Knowing and teaching differ fundamentally: effective instruction requires transforming knowledge into forms learners can grasp. Large language models, when asked to generate lessons (a concrete form of teaching), produce content lacking pedagogical depth. We trace this failure to three decisions that expert teachers make: selecting content by recognizing each source’s instructional role, sequencing topics so foundations precede applications, and synthesizing components into a unified whole. To scaffold these decisions, we introduce TeachCraft, a framework with three agents: Explorer classifies sources by pedagogical intent to guide selection; Planner orders objectives from foundational to advanced; Generator produces lesson materials through a schema that ensures consistency across components. To evaluate this approach, we construct LessonBench, 40 expert-designed lessons paired with two to five heterogeneous source documents, on which TeachCraft achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines, with ablations confirming that each decision contributes independently to overall lesson quality.[Source code is available at <https://anonymous.4open.science/r/TeachCraft-1672>]

Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching. While preliminary studies have focused on task-specific, independent LLM-empowered agents, the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. In this work, we propose SimClass, a multi-agent classroom simulation teaching framework. We recognize representative class roles and introduce a novel class control mechanism for automatic classroom teaching, and conduct user experiments in two real-world courses. Using the Flanders Interactive Analysis System and Community of Inquiry theoretical frameworks from educational analysis, we demonstrate that LLMs can simulate a dynamic learning environment for users with active teacher-student and student-student interactions. We also observe group behaviors among agents in SimClass, where agents collaborate to create enlivening interactions in classrooms to improve user learning process. We hope this work pioneers the application of LLM-empowered multi-agent systems in virtual classroom teaching. Our implementation and service can be found at https://github.com/THU-MAIC/SimClass.

We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension strategy, aiding in training LLMs to understand reasoning patterns more effectively as well as a cross-way validation strategy, ensuring the quality of the automatically generated data. To evaluate the performance of TableLLM, we have crafted benchmarks tailored to address both document and spreadsheet formats as well as constructed a well-organized evaluation pipeline capable of handling both scenarios. Thorough evaluations underscore the advantages of TableLLM when compared to various existing general-purpose and tabular data-focused LLMs. We have publicly released the model checkpoint, source code, benchmarks, and a web application for user interaction on this anonymized repository.

We investigate the usage of entity linking (EL)in downstream tasks and present the first modularized EL toolkit for easy task adaptation. Different from the existing EL methods that dealwith all the features simultaneously, we modularize the whole model into separate parts witheach feature. This decoupled design enablesflexibly adding new features without retraining the whole model as well as flow visualization with better interpretability of the ELresult. We release the corresponding toolkit,HOSMEL, for Chinese, with three flexible usage modes, a live demo, and a demonstrationvideo. Experiments on two benchmarks forthe question answering task demonstrate thatHOSMEL achieves much less time and spaceconsumption as well as significantly better accuracy performance compared with existingSOTA EL methods. We hope the release ofHOSMEL will call for more attention to studyEL for downstream tasks in non-English languages.