Recent developments in Generative Artificial Intelligence (GenAI) have
created a paradigm shift in multiple areas of society, and the use of these
technologies is likely to become a defining feature of education in coming
decades. GenAI offers transformative pedagogical opportunities, while
simultaneously posing ethical and academic challenges. Against this backdrop,
we outline a practical, simple, and sufficiently comprehensive tool to allow
for the integration of GenAI tools into educational assessment: the AI
Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate
level of GenAI usage in assessments based on the learning outcomes they seek to
address. The AIAS offers greater clarity and transparency for students and
educators, provides a fair and equitable policy tool for institutions to work
with, and offers a nuanced approach which embraces the opportunities of GenAI
while recognising that there are instances where such tools may not be
pedagogically appropriate or necessary. By adopting a practical, flexible
approach that can be implemented quickly, the AIAS can form a much-needed
starting point to address the current uncertainty and anxiety regarding GenAI
in education. As a secondary objective, we engage with the current literature
and advocate for a refocused discourse on GenAI tools in education, one which
foregrounds how technologies can help support and enhance teaching and
learning, which contrasts with the current focus on GenAI as a facilitator of
academic misconduct.

近年来，生成人工智能（GenAI）的发展在社会多个领域引起了一次范式转变，而这些技术的使用可能成为未来几十年教育的一个定义性特征。GenAI 提供了变革性的教育机会，同时也带来了伦理和学术方面的挑战。在这个背景下，我们提出了一个实用、简单且足够全面的工具来将 GenAI 工具融入教育评估中：AI 评估量表（AIAS）。AIAS 使教育工作者能够根据他们希望解决的学习成果选择适当的 GenAI 使用水平进行评估。AIAS 为学生和教育工作者提供了更清晰、更透明的方式，为机构提供了公平、公正的政策工具，并提供了一种细致入微的方法，既拥抱了 GenAI 的机会，又意识到在某些情况下这样的工具可能在教学上不合适或不必要。通过采用一种实用、灵活、可快速实施的方法，AIAS 可以成为应对当前关于教育中的 GenAI 存在的不确定性和焦虑的迫切起点。作为次要目标，我们参与当前的文献研究，倡导关注 GenAI 教育工具的重新聚焦的讨论，突出技术如何帮助支持和增强教学和学习，与当前关注 GenAI 作为学术不端行为便利工具的焦点形成对比。

引领生成人工智能时代：介绍道德生成人工智能评估的 AI 评估尺度

Navigating the generative AI era: Introducing the AI assessment scale  for ethical GenAI assessment

Academic challenges comprise effective means for (i) advancing the state of
the art, (ii) putting in the spotlight of a scientific community specific
topics and problems, as well as (iii) closing the gap for under represented
communities in terms of accessing and participating in the shaping of research
fields. Competitions can be traced back for centuries and their achievements
have had great influence in our modern world. Recently, they (re)gained
popularity, with the overwhelming amounts of data that is being generated in
different domains, as well as the need of pushing the barriers of existing
methods, and available tools to handle such data. This chapter provides a
survey of academic challenges in the context of machine learning and related
fields. We review the most influential competitions in the last few years and
analyze challenges per area of knowledge. The aims of scientific challenges,
their goals, major achievements and expectations for the next few years are
reviewed.

学术挑战是推动现有技术发展、将特定主题和问题置于科学界关注的有效手段，同时也是缩小受限社群在访问和参与塑造研究领域方面的差距的重要方式。本文回顾了过去几年里在机器学习及相关领域内最具影响力的竞赛，并分析了各个学科领域的挑战。对科学挑战的目标、主要成就和未来几年的期望进行了审视。