The advent of generative artificial intelligence (GenAI) and large language
models (LLMs) has opened new avenues for enhancing human learning. This study
introduces Professor Leodar, a custom-built, Singlish-speaking Retrieval
Augmented Generation (RAG) chatbot designed to enhance educational support for
undergraduate engineering students. Deployed at Nanyang Technological
University, Singapore, Professor Leodar offers a glimpse into the future of
AI-assisted learning, offering personalized guidance, 24/7 availability, and
contextually relevant information. Through a mixed-methods approach, we uncover
the impact of Professor Leodar on student learning, engagement, and exam
preparedness, with 97.1% of participants reporting positive experiences. These
findings help define possible roles of AI in education and highlight the
potential of custom GenAI chatbots. Our combination of chatbot development,
in-class deployment and study of learning outcomes offers a benchmark for GenAI
educational tools and serves as stepping stone for redefining the interplay
between AI and human learning.

该研究介绍了 Leodar 教授，一个定制的、使用新加坡英语（Singlish）的 Retrieval Augmented Generation（RAG）聊天机器人，旨在增强本科工程学生的教育支持。通过混合方法，揭示出 Professor Leodar 对学生学习、参与和考试准备的影响，其中 97.1% 的参与者报告了积极体验。这些发现帮助定义了 AI 在教育中的可能角色，突显了定制 GenAI 聊天机器人的潜力。我们的聊天机器人开发、课堂部署和学习成果研究的组合为 GenAI 教育工具提供了一个基准，并为重新定义人工智能和人类学习的相互作用奠定了基础。

运用 GenAI 于高等教育：检索增强的生成对话机器人对人类学习的影响研究

Harnessing GenAI for Higher Education: A Study of a Retrieval Augmented  Generation Chatbot's Impact on Human Learning

This paper examines the transformative role of Large Language Models (LLMs)
in education and their potential as learning tools, despite their inherent
risks and limitations. The authors propose seven approaches for utilizing AI in
classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator,
and AI-student, each with distinct pedagogical benefits and risks. The aim is
to help students learn with and about AI, with practical strategies designed to
mitigate risks such as complacency about the AI's output, errors, and biases.
These strategies promote active oversight, critical assessment of AI outputs,
and complementarity of AI's capabilities with the students' unique insights. By
challenging students to remain the "human in the loop," the authors aim to
enhance learning outcomes while ensuring that AI serves as a supportive tool
rather than a replacement. The proposed framework offers a guide for educators
navigating the integration of AI-assisted learning in classrooms

本文探讨了大型语言模型在教育中的转型作用和潜力，提出了利用人工智能进行辅助教学的七种方法，并介绍了实践策略来减少风险，并保证人工智能作为支持性工具而非替代品。