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

TL;DR

The didactic planning script is based on the AI application pipeline and links AI concepts with study-relevant topics and opens up a new solution space and promotes students' interest in and understanding of the potentials and risks of AI.

Abstract

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.

Figures (6)

• Figure 1: The AI application pipeline comprises six steps, with the steps in which programming is usually carried out outlined in green.
• Figure 2: Generalized didactic planning script for applied AI --- together with the discipline-specific content of the lecture or course, AI can be seamlessly integrated into various curricula.
• Figure 3: Example of the teaching unit --- simplified representation of an AI model for image classification.
• Figure 4: Example from teaching unit 4 --- result of the AI for time series forecasting.
• Figure 5: Checklist for whether practical AI can be used in the corresponding discipline-specific lecture or course.