Applications such as ChatGPT and WOMBO Dream make it easy to inspire students
without programming knowledge to use artificial intelligence (AI). Therefore,
given the increasing importance of AI in all disciplines, innovative strategies
are needed to educate students in AI without programming knowledge so that AI
can be integrated into their study modules as a future skill. This work
presents a didactic planning script for applied AI. The didactic planning
script is based on the AI application pipeline and links AI concepts with
study-relevant topics. These linkages open up a new solution space and promote
students' interest in and understanding of the potentials and risks of AI. An
example lecture series for master students in energy management shows how AI
can be seamlessly integrated into discipline-specific lectures. To this end,
the planning script for applied AI is adapted to fit the study programs' topic.
This specific teaching scenario enables students to solve a discipline-specific
task step by step using the AI application pipeline. Thus, the application of
the didactic planning script for applied AI shows the practical implementation
of the theoretical concepts of AI. In addition, a checklist is presented that
can be used to assess whether AI can be used in the discipline-specific
lecture. AI as a future skill must be learned by students based on use cases
that are relevant to the course of studies. For this reason, AI education
should fit seamlessly into various curricula, even if the students do not have
a programming background due to their field of study.

给没有编程知识的学生提供创新策略，将 AI 教育整合到课程中，以应用 AI 为基础的教学规划脚本和教育模型为例，展示了 AI 的潜力、风险及其在学术专业中的实际应用。

非编程人员的人工智能：应用于无编程技能学生的讲座中的人工智能

AI for non-programmers: Applied AI in the lectures for students without  programming skills

Instance-level graph neural network explainers have proven beneficial for
explaining such networks on histopathology images. However, there has been few
methods that provide model explanations, which are common patterns among
samples within the same class. We envision that graph-based histopathological
image analysis can benefit significantly from such explanations. On the other
hand, current model-level explainers are based on graph generation methods that
are not applicable in this domain because of no corresponding image for their
generated graphs in real world. Therefore, such explanations are communicable
to the experts. To follow this vision, we developed KS-GNNExplainer, the first
instance-level graph neural network explainer that leverages current
instance-level approaches in an effective manner to provide more informative
and reliable explainable outputs, which are crucial for applied AI in the
health domain. Our experiments on various datasets, and based on both
quantitative and qualitative measures, demonstrate that the proposed explainer
is capable of being a global pattern extractor, which is a fundamental
limitation of current instance-level approaches in this domain.

我们开发了 KS-GNNExplainer，这是第一个利用现有实例级方法以有效方式提供更具信息和可靠的可解释输出的实例级图神经网络解释器，从而使得基于图形的组织病理学图像分析可以显著受益。我们的实验表明，该解释器能够成为全局模式提取器，从而克服了当前实例级方法在该领域中的基本局限性。