@article{Zhao2026, 
author = {H. Vicky Zhao and Yanpin Ren and Chao Shang and Haonan Yin and Zhanwei Xu and Rui Jiang and Benben Jiang and Qing Zhuo and Hangjing Zhang and Xin Pei and Changshui Zhang and Hua Geng},
title = {AI Tutors and the Transformation of Education: Opportunities, Challenges, and Future Directions},
year = {2026},
journal = {Cybernetics and Intelligence},
keywords = {AI for Education, Intelligent Tutoring Systems, Large Language Models, Higher Education},
url = {https://www.sciopen.com/article/10.26599/CAI.2026.9390014},
doi = {10.26599/CAI.2026.9390014},
abstract = {Artificial Intelligence (AI) tutoring systems have transformative potential for higher education, yet their integration faces critical challenges including but not limited to curriculum misalignment, epistemic unreliability, and fragmented learning experiences. This paper introduces the AI Learning Companion, a platform developed by the Department of Automation at Tsinghua University. The platform enforces course-specific knowledge isolation while explicitly modeling conceptual overlaps across courses via a curriculum knowledge graph. It integrates three key functional modules–multimodal resource ingestion, retrieval-augmented intelligent Q&A with citation tracking, and automated quiz generation. It is powered by a layered architecture that leverages large language models through disciplined prompt engineering, context management, and evidence-centered generation. Deployed across 36 courses and serving over 1,300 students, our findings demonstrate that the platform enhances learning efficiency, fosters traceable and in-depth understanding, and can be integrated into the teaching-management loop for data-driven intervention.}
}