InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation
arXiv:2508. 03174v4 Announce Type: replace Abstract: Collaborative partnerships play a crucial role in inquiry-oriented education.
arXiv:2606. 05222v1 Announce Type: cross Abstract: Artificial intelligence (AI) has been applied across educational contexts to support learning.
arXiv:2508. 03174v4 Announce Type: replace Abstract: Collaborative partnerships play a crucial role in inquiry-oriented education.
arXiv:2606. 09041v1 Announce Type: cross Abstract: Research on artificial intelligence in education (AIED) is rapidly expanding, yet technical progress often lacks human-centered grounding and adequate attention to cultural context.
arXiv:2608. 11245v1 Announce Type: new Abstract: Online education offers unprecedented scalability and accessibility to global learners from diverse backgrounds, but it often suffers from low engagement and poor long term learning effectiveness.
arXiv:2606. 12441v1 Announce Type: cross Abstract: The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative artificial intelligence (AI) proliferates in educational settings.
arXiv:2607. 12180v1 Announce Type: cross Abstract: An AI teammate's design properties (personality, communication style, when it speaks) can shape a team's trust, coordination, and decisions.
arXiv:2608. 05171v1 Announce Type: cross Abstract: Generative AI (GAI) creates new opportunities for collaborative problem-solving (CPS), yet its role in shaping student interaction remains unclear.
arXiv:2607. 08748v1 Announce Type: new Abstract: In this study, we present a large-scale descriptive analysis of the use of an AI-based learning assistant (Syntea) in higher education.
arXiv:2607. 13370v1 Announce Type: cross Abstract: This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes.
This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes. That prior work validated LEA on a single STEM course (CMP511) exclusively through simulation, using synthetic learner agents.
arXiv:2401. 07386v5 Announce Type: cross Abstract: This study expands on previous work that introduced the AIcon2abs method (AI from Concrete to Abstract: Demystifying Artificial Intelligence to the general public), an innovative approach designed to increase public understanding of machine learning (ML) across diverse age groups, including K-12 students, and aims to evaluate its effectiveness.
arXiv:2606. 27233v1 Announce Type: cross Abstract: We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaboration.
arXiv:2607. 02198v1 Announce Type: cross Abstract: Human-AI teaming has received increasing attention in the literature.