AI Literacy: Evaluation of an AI Literacy Course for Engineers

In this paper, we discuss the implementation and evaluation of a project-based self-direct learning competency-based module on AI literacy. This module was developed for various Bachelor's degree programs from the school of Engineering at the University of Applied Sciences and Arts Northwestern Switzerland. The aim of this course is not only to show how AI can help with writing and programming (learning), but also to get students firstly to reflect on working with AI and introduce them to important current debates, and secondly to teach the basics of large language models and central methods of data science. Students should be made aware to academic work (including finding and evaluating sources) with and about AI. During the course, they have to combine analytical and technical skills like programming and web scraping when developing a selfchosen project. An important finding of our work is that, despite their daily engagement with computer science and digital tools, without such specific formats, students are unlikely to acquire the necessary knowledge and critical thinking to navigate the rapidly changing landscape of AI autonomously and confidently.

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Published
2025-04-22
DOI
https://doi.org/10.1109/educon62633.2025.11016470
Primary Topic
Teaching and Learning Programming
Type
article
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article

AI Literacy: Evaluation of an AI Literacy Course for Engineers

Fernando Benites, Caspar Battegay
Teaching and Learning Programming
article

AI Literacy: Evaluation of an AI Literacy Course for Engineers

Fernando Benites, Caspar Battegay
article en

Abstract

In this paper, we discuss the implementation and evaluation of a project-based self-direct learning competency-based module on AI literacy. This module was developed for various Bachelor's degree programs from the school of Engineering at the University of Applied Sciences and Arts Northwestern Switzerland. The aim of this course is not only to show how AI can help with writing and programming (learning), but also to get students firstly to reflect on working with AI and introduce them to important current debates, and secondly to teach the basics of large language models and central methods of data science. Students should be made aware to academic work (including finding and evaluating sources) with and about AI. During the course, they have to combine analytical and technical skills like programming and web scraping when developing a selfchosen project. An important finding of our work is that, despite their daily engagement with computer science and digital tools, without such specific formats, students are unlikely to acquire the necessary knowledge and critical thinking to navigate the rapidly changing landscape of AI autonomously and confidently.

FHNW University of Applied Sciences and Arts Northwestern Switzerland (CH), OST - Ostschweizer Fachhochschule (CH)
Quality Education
Openalex Percentile: Top 88%
Teaching and Learning Programming
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AI Literacy: Evaluation of an AI Literacy Course for Engineers — Fernando Benites, Caspar Battegay · (2025) | TGRS Research Map | TGRS