This paper presents a large-scale analysis of AI-based learning assistant (Syntea) usage in higher education using log data from 77,543 students.

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 operationalizes knowledge-based design requirements in a generative social robot tutoring system called Teachy Mini, and evaluates its effectiveness in higher education.

This paper proposes a modular, agentic AI chatbot architecture to assist students with exercise solving, aiming to ensure responsible and pedagogically sound AI use in education.

This paper analyzes the use of generative AI for feedback in higher education based on 2988 instances from Estonian bachelor students, finding that students generally find it helpful but not a self-co…

The paper proposes an interpretable, AI-driven decision layer that ranks course topics needing attention using multiple student and teacher signals, successfully identifying learning gaps before forma…

The paper introduces AIBuildAI-2, a knowledge-enhanced agent that significantly improves the automatic building of AI models by integrating an external, evolving knowledge system, achieving state-of-t…

The paper introduces LFTutor, an intelligent tutoring system leveraging LLMs and Socratic questioning to teach laypeople about logical fallacies, demonstrating its effectiveness in fostering critical…

COLLEAGUE.SKILL introduces an automated system that distills heterogeneous traces of human expertise and role-specific knowledge into portable, inspectable, and usable AI skill packages.

This study surveyed higher education practitioners to map their beliefs and behaviors regarding AI integration, finding that while they view AI favorably, institutional barriers and gaps in design-ori…

This paper analyzes large-scale human-LLM conversations to identify learning behaviors and their associated factors.

The paper introduces AGENTCL, a rigorous evaluation framework that uses controlled task streams to accurately measure an agent's ability to accumulate and reuse knowledge across multiple tasks, thereb…

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 Plan, a structured agentic behavior that decomposes multi-hop questions into ordered sub-questions before retrieval, and proposes a self-bootstrapping paradigm to train it without…

This paper investigates the vulnerability of LLM-based automatic grading systems to prompt injection (PI) attacks, demonstrating that current systems are highly susceptible to manipulation that can le…

This paper discusses the current understanding of Large Language Models (LLMs), their capabilities, and their relationship to human cognition, with a focus on emerging capabilities and mechanistic imp…

GrepSeek introduces a novel direct corpus interaction (DCI) search agent that trains an LLM to find and compose evidence from large text corpora by issuing executable shell commands, achieving state-o…