This paper proposes PTFEA, a curriculum-learning-inspired framework that translates fine-tuning strategies into interpretable context engineering for Multimodal Entity Alignment (MMEA), demonstrating…

The paper introduces an LLM-based pipeline that tags learning resources with structured competencies, achieving strong performance while providing traceable evidence and leveraging graph constraints.

SkillC introduces a Contrastive Skill Credit Assignment (CSCA) framework to enable LLM agents to autonomously internalize skills during training, significantly outperforming existing methods without r…

This paper reports the first classroom deployment of LEA, an adaptive AI tutoring agent, with real students across three courses and evaluates its cross-course scalability.

This paper explores using Knowledge Components (KCs) as interpretable signals to understand assignment difficulty and student struggle in intro programming courses.

This paper reports experiences and takeaways from using puzzles to teach critical testing literacy (CTL) through workshops, introducing the pedagogical framework P4TEST.

The paper introduces a robust four-stage NLP framework that uses schema-constrained LLMs and ESCO vocabulary to accurately extract and align educational competencies with labor market demands, quantif…

This paper provides a mathematical framework for studying different policy learning problems and shows reductions between them.

This paper introduces ASE-26, a comprehensive undergraduate curriculum designed to formalize and teach agentic software engineering as a distinct academic discipline.

This paper develops a policy-learning framework to optimally assign prediction tasks to multiple agents, considering individual agent expertise and capacity constraints, achieving systematic performan…

The paper introduces BusinessCaseBench, a benchmark for measuring AI performance on analytical knowledge work using business cases and grading rubrics.

This study compares different levels of LLM access in a statistics course, finding that structured, guided use significantly improves students' reasoning skills and independent learning compared to un…

The paper introduces TeachObs, a comprehensive, human-validated benchmark for multimodal teaching observation, and evaluates frontier LLMs, finding that no single model consistently outperforms others…

CRITIC-R1 introduces a structured critic framework that treats RAG critique as an explicit error diagnosis problem using reinforcement learning, significantly improving answer quality over strong RAG…

MEMENTO proposes a novel framework that treats the open web as a continuous learning signal, enabling agents to acquire task-specific expertise and reusable research strategies in low-data domains wit…

The paper introduces AgentSchool, an advanced LLM-powered multi-agent simulator that models learning as state transitions to provide a robust, ethically viable testbed for educational research and ped…

The paper introduces TRACE, a novel metric that evaluates the logical structure of LLM reasoning (CoT) by integrating Toulmin's argumentation theory, demonstrating that sound reasoning structure corre…