The integration of artificial intelligence (AI) in educational measurement
has revolutionized assessment methods, enabling automated scoring, rapid
content analysis, and personalized feedback through machine learning and
natural language processing. These advancements provide timely, consistent
feedback and valuable insights into student performance, thereby enhancing the
assessment experience. However, the deployment of AI in education also raises
significant ethical concerns regarding validity, reliability, transparency,
fairness, and equity. Issues such as algorithmic bias and the opacity of AI
decision-making processes pose risks of perpetuating inequalities and affecting
assessment outcomes. Responding to these concerns, various stakeholders,
including educators, policymakers, and organizations, have developed guidelines
to ensure ethical AI use in education. The National Council of Measurement in
Education's Special Interest Group on AI in Measurement and Education (AIME)
also focuses on establishing ethical standards and advancing research in this
area. In this paper, a diverse group of AIME members examines the ethical
implications of AI-powered tools in educational measurement, explores
significant challenges such as automation bias and environmental impact, and
proposes solutions to ensure AI's responsible and effective use in education.

人工智能在教育测量中的整合革命了评估方法，通过机器学习和自然语言处理实现了自动化评分、快速内容分析和个性化反馈，为学生提供及时、一致的反馈和宝贵的学业表现洞察，从而增强了评估体验。然而，将人工智能应用于教育也带来了诸多伦理关切，涉及到有效性、可靠性、透明度、公平性和公正性等问题。算法偏倚和人工智能决策过程的不透明性等问题可能会将不平等问题持续下去并影响评估结果。为了应对这些关切，包括教育工作者、决策者和组织在内的各方利益相关者已制定了指南，以确保人工智能在教育中的道德使用。美国教育测量学会（NCME）的 AI 与教育测量特别兴趣小组（AIME）也致力于确立道德标准并推动该领域的研究。本文中，来自 AIME 成员的多样化团队审视了人工智能驱动教育测量工具的伦理影响，探讨了自动化偏倚和环境影响等重大挑战，并提出了解决方案，以确保人工智能在教育中的负责任和有效使用。

教育测量中人工智能的崛起：机遇与伦理挑战

The Rise of Artificial Intelligence in Educational Measurement:  Opportunities and Ethical Challenges

Technical debt is a well-known challenge in software development, and its
negative impact on software quality, maintainability, and performance is widely
recognized. In recent years, artificial intelligence (AI) has proven to be a
promising approach to assist in managing technical debt. This paper presents a
comprehensive literature review of existing research on the use of AI powered
tools for technical debt avoidance in software development. In this literature
review we analyzed 15 related research papers which covers various AI-powered
techniques, such as code analysis and review, automated testing, code
refactoring, predictive maintenance, code generation, and code documentation,
and explores their effectiveness in addressing technical debt. The review also
discusses the benefits and challenges of using AI for technical debt
management, provides insights into the current state of research, and
highlights gaps and opportunities for future research. The findings of this
review suggest that AI has the potential to significantly improve technical
debt management in software development, and that existing research provides
valuable insights into how AI can be leveraged to address technical debt
effectively and efficiently. However, the review also highlights several
challenges and limitations of current approaches, such as the need for
high-quality data and ethical considerations and underscores the importance of
further research to address these issues. The paper provides a comprehensive
overview of the current state of research on AI for technical debt avoidance
and offers practical guidance for software development teams seeking to
leverage AI in their development processes to mitigate technical debt
effectively

本文综合回顾了关于使用 AI 工具避免技术债务的现有研究，其中分析了 15 篇涵盖不同 AI 技术的文章，如代码分析与审查，自动化测试，代码重构，预测性维护，代码生成和代码文档化，并探讨了它们在解决技术债务方面的效果、挑战和限制，为想要在软件开发过程中有效减轻技术债务的团队提供了实用指南。

软件开发中的技术债务管理人工智能

Artificial Intelligence for Technical Debt Management in Software  Development

Low-code programming allows citizen developers to create programs with
minimal coding effort, typically via visual (e.g. drag-and-drop) interfaces. In
parallel, recent AI-powered tools such as Copilot and ChatGPT generate programs
from natural language instructions. We argue that these modalities are
complementary: tools like ChatGPT greatly reduce the need to memorize large
APIs but still require their users to read (and modify) programs, whereas
visual tools abstract away most or all programming but struggle to provide easy
access to large APIs. At their intersection, we propose LowCoder, the first
low-code tool for developing AI pipelines that supports both a visual
programming interface (LowCoder_VP) and an AI-powered natural language
interface (LowCoder_NL). We leverage this tool to provide some of the first
insights into whether and how these two modalities help programmers by
conducting a user study. We task 20 developers with varying levels of AI
expertise with implementing four ML pipelines using LowCoder, replacing the
LowCoder_NL component with a simple keyword search in half the tasks. Overall,
we find that LowCoder is especially useful for (i) Discoverability: using
LowCoder_NL, participants discovered new operators in 75% of the tasks,
compared to just 32.5% and 27.5% using web search or scrolling through options
respectively in the keyword-search condition, and (ii) Iterative Composition:
82.5% of tasks were successfully completed and many initial pipelines were
further successfully improved. Qualitative analysis shows that AI helps users
discover how to implement constructs when they know what to do, but still fails
to support novices when they lack clarity on what they want to accomplish.
Overall, our work highlights the benefits of combining the power of AI with
low-code programming.

LowCoder 是第一个既支持视觉程序界面又支持自然语言接口的低代码工具，我们通过用户研究发现，LowCoder 特别有用于 AI 操作员的发现性和迭代组合。