Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22751
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid
Authors: Stadelmann, Thilo
Keuzenkamp, Julian
Grabner, Helmut
Würsch, Christoph
et. al: No
DOI: 10.3390/educsci11070318
10.21256/zhaw-22751
Published in: Education Sciences
Volume(Issue): 11
Issue: 7
Page(s): 318
Issue Date: 25-Jun-2021
Publisher / Ed. Institution: MDPI
ISSN: 2227-7102
Language: English
Subjects: Flexible educational design; E-learning; Constructivism; Design-based research; COVID-19; Post-pandemic tertiary engineering education; Artificial intelligence
Subject (DDC): 006: Special computer methods
378: Higher education
Abstract: We present the “AI-Atlas” didactic concept as a coherent set of best practices for teaching Artificial Intelligence (AI) and Machine Learning (ML) to a technical audience in tertiary education, and report on its implementation and evaluation within a design-based research framework and two actual courses: an introduction to AI within the final year of an undergraduate computer science program, as well as an introduction to ML within an interdisciplinary graduate program in engineering. The concept was developed in reaction to the recent AI surge and corresponding demand for foundational teaching on the subject to a broad and diverse audience, with on-site teaching of small classes in mind and designed to build on the specific strengths in motivational public speaking of the lecturers. The research question and focus of our evaluation is to what extent the concept serves this purpose, specifically taking into account the necessary but unforeseen transfer to ongoing hybrid and fully online teaching since March 2020 due to the COVID-19 pandemic. Our contribution is two-fold: besides (i) presenting a general didactic concept for tertiary engineering education in AI and ML, ready for adoption, we (ii) draw conclusions from the comparison of qualitative student evaluations (n = 24-30) and quantitative exam results (n = 62-113) of two full semesters under pandemic conditions with the result of previous years (participants from Zurich, Switzerland). This yields specific recommendations for the adoption of any technical curriculum under flexible teaching conditions—be it on-site, hybrid, or online.
URI: https://digitalcollection.zhaw.ch/handle/11475/22751
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Centre for Artificial Intelligence (CAI)
Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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