With the surge in data-centric AI and its increasing capabilities, AI
applications have become a part of our everyday lives. However,
misunderstandings regarding their capabilities, limitations, and associated
advantages and disadvantages are widespread. Consequently, in the university
setting, there is a crucial need to educate not only computer science majors
but also students from various disciplines about AI. In this experience report,
we present an overview of an introductory course that we offered to students
coming from different majors. Moreover, we discuss the assignments and quizzes
of the course, which provided students with a firsthand experience of AI
processes and insights into their learning patterns. Additionally, we provide a
summary of the course evaluation, as well as students' performance. Finally, we
present insights gained from teaching this course and elaborate on our future
plans.

在大数据人工智能与其不断增长的能力引领下，人工智能应用已成为我们日常生活的一部分。然而，对于其能力、限制以及相关优缺点的误解广泛存在。因此，在大学环境中，有必要向计算机科学专业学生和其他学科的学生普及人工智能知识。在这篇经验报告中，我们介绍了我们为来自不同专业的学生开设的一门入门课程的概述。此外，我们讨论了课程的作业和测验，让学生第一手了解了人工智能的过程，并洞察到了他们的学习模式。另外，我们还提供了课程评价和学生表现的总结。最后，我们介绍了从教授这门课程中获得的见解，并阐述了我们的未来计划。

人工智能在日常生活中的应用 2.0：跨专业教育大学学生

Artificial Intelligence in Everyday Life 2.0: Educating University  Students from Different Majors

As natural language processing (NLP) has recently seen an unprecedented level
of excitement, and more people are eager to enter the field, it is unclear
whether current research reproducibility efforts are sufficient for this group
of beginners to apply the latest developments. To understand their needs, we
conducted a study with 93 students in an introductory NLP course, where
students reproduced the results of recent NLP papers. Surprisingly, we find
that their programming skill and comprehension of research papers have a
limited impact on their effort spent completing the exercise. Instead, we find
accessibility efforts by research authors to be the key to success, including
complete documentation, better coding practice, and easier access to data
files. Going forward, we recommend that NLP researchers pay close attention to
these simple aspects of open-sourcing their work, and use insights from
beginners' feedback to provide actionable ideas on how to better support them.

本文为了解当下自然语言处理领域新手对当前研究重复性工作的需求，对 93 名 introductory NLP 课程学生开展了研究。结果表明，编程技能和对研究论文的理解与其完成任务的时间无明显相关性，而研究作者开源工作的可访问性、文档完整性、良好编程规范和易于获取数据文件等均对成功至关重要。此外，作者建议自然语言处理研究者关注这些简单但关键的方面并充分利用初学者的反馈来提供更好的支持和改进。