Conversational AI agents often require extensive datasets for training that
are not publicly released, are limited to social chit-chat or handling a
specific domain, and may not be easily extended to accommodate the latest
advances in AI technologies. This paper introduces Jill Watson, a
conversational Virtual Teaching Assistant (VTA) leveraging the capabilities of
ChatGPT. Jill Watson based on ChatGPT requires no prior training and uses a
modular design to allow the integration of new APIs using a skill-based
architecture inspired by XiaoIce. Jill Watson is also well-suited for
intelligent textbooks as it can process and converse using multiple large
documents. We exclusively utilize publicly available resources for
reproducibility and extensibility. Comparative analysis shows that our system
outperforms the legacy knowledge-based Jill Watson as well as the OpenAI
Assistants service. We employ many safety measures that reduce instances of
hallucinations and toxicity. The paper also includes real-world examples from a
classroom setting that demonstrate different features of Jill Watson and its
effectiveness.

介绍了一款名为 Jill Watson 的基于 ChatGPT 的对话式虚拟教学助手，采用模块化设计，能处理多个大型文档，通过比较分析表明该系统在性能上优于传统基于知识的 Jill Watson 和 OpenAI Assistants 服务，并且引入了多项安全措施以减少幻觉和有毒内容。

Jill Watson：由 ChatGPT 提供支持的虚拟教学助理

Jill Watson: A Virtual Teaching Assistant powered by ChatGPT

The emergence of Large language models (LLMs) is expected to have a major
impact on education. This paper explores the potential of using ChatGPT, an
LLM, as a virtual Teaching Assistant (TA) in an Introductory Programming
Course. We evaluate ChatGPT's capabilities by comparing its performance with
that of human TAs in some TA functions. The TA functions which we focus on
include (1) solving programming assignments, (2) grading student code
submissions, and (3) providing feedback to undergraduate students in an
introductory programming course. Firstly, we investigate how closely ChatGPT's
solutions align with those submitted by students. This analysis goes beyond
code correctness and also considers code quality. Secondly, we assess ChatGPT's
proficiency in grading student code submissions using a given grading rubric
and compare its performance with the grades assigned by human TAs. Thirdly, we
analyze the quality and relevance of the feedback provided by ChatGPT. This
evaluation considers how well ChatGPT addresses mistakes and offers suggestions
for improvement in student solutions from both code correctness and code
quality perspectives. We conclude with a discussion on the implications of
integrating ChatGPT into computing education for automated grading,
personalized learning experiences, and instructional support.

研究论文探讨了在一个初级编程课程中使用 ChatGPT 作为虚拟助教的潜力，并通过比较其与人类助教在一些助教功能上的表现来评估 ChatGPT 的能力。论文结论中讨论了将 ChatGPT 纳入计算机教育中对自动分级、个性化学习和教学支持的影响。

ChatGPT 在初级编程课程中是否能担任助教的角色？

Can ChatGPT Play the Role of a Teaching Assistant in an Introductory  Programming Course?

Teaching assistants have played essential roles in the long history of
education. However, few MOOC platforms are providing human or virtual teaching
assistants to support learning for massive online students due to the
complexity of real-world online education scenarios and the lack of training
data. In this paper, we present a virtual MOOC teaching assistant, LittleMu
with minimum labeled training data, to provide question answering and chit-chat
services. Consisting of two interactive modules of heterogeneous retrieval and
language model prompting, LittleMu first integrates structural, semi- and
unstructured knowledge sources to support accurate answers for a wide range of
questions. Then, we design delicate demonstrations named "Chain of Teach"
prompts to exploit the large-scale pre-trained model to handle complex
uncollected questions. Except for question answering, we develop other
educational services such as knowledge-grounded chit-chat. We test the system's
performance via both offline evaluation and online deployment. Since May 2020,
our LittleMu system has served over 80,000 users with over 300,000 queries from
over 500 courses on XuetangX MOOC platform, which continuously contributes to a
more convenient and fair education. Our code, services, and dataset will be
available at this https URL

通过最小限度的标记训练数据，我们提供了一个名为 LittleMu 的虚拟 MOOC 教学助手，以提供问题解答和闲聊服务，并通过离线评估和在线部署测试了该系统的性能。