We are planning to introduce this literacy course to senior secondary school students. In the future, this AI literacy course could be extended to include AI application projects and discussions of related ethical issues regarding the wider use of AI in society. Prior knowledge of programming was not necessary for AI concepts development, and the flipped classroom learning approach enabled more flexible learning by the participants. These findings indicated that the participants of diverse study backgrounds, and of both genders, could understand the concepts of machine learning, supervised learning, regression, classification, unsupervised learning, and clustering. The results of our pre-course and post-course surveys indicated that the participants made significant progress in understanding AI concepts, felt empowered to work with AI.

We promoted this course to 4000 students and recruited 120 volunteer participants to attend and complete the 7-h course. One of the study's research questions was whether university students from a variety of disciplines could develop a conceptual understanding of AI through a literacy course. The objective of this study was to design, implement and evaluate an AI literacy course for university students. This study can be used to guide future research on fostering AI literacy among educated citizens from diverse study backgrounds.Ī few limited efforts have been made to promote artificial intelligence (AI) literacy for citizens. The courses will be expanded to incorporate the development of AI applications together with discussion of ethical issues regarding the wide use of AI in society. The AI literacy courses successfully lower the barrier to entry for AI literacy and address a public need. The participants felt empowered by the significant gains in their literacy and conceptual understanding of AI.

The results of their pre- and post-course conceptual tests, surveys and self-reflective writing tasks indicated that the courses successfully equipped the participants with a conceptual understanding of AI. Eighty-two volunteers completed two AI literacy courses comprising 7 h of machine learning and 9 h of deep learning. This study fills the knowledge gap by evaluating AI literacy courses that aim to build conceptual understanding among university students from diverse study backgrounds. There is a research gap in refocussing AI literacy courses from combining conceptual learning with mathematical formulae and programming codes to emphasising conceptual building from the beginning. The innovative teaching experience carried out during two years meant a great motivation, an improvement of the learning capacity and a continuous recycling of knowledge to which Computer Engineers are faced to.Īrtificial intelligence (AI) applications have become prevalent in all walks of life, but efforts to promote AI literacy to educated citizens from diverse study backgrounds are limited. The main objective of this work is to gamify the classroom with the idea of providing students with valuable hands-on experience by means of addressing a real problem, as well as the possibility to cooperate and compete simultaneously to acquire ML competences. In this context, communities offering platforms for open competitions such as Kaggle can be used as a motivating element. Developing the necessary competences in this field is not a trivial task, and applying innovative methodologies such as gamification can smooth the initial learning curve.

It falls under the umbrella of Data Science and is usually developed by Computer Engineers becoming what is known as Data Scientists. Machine learning (ML) is the field of science that combines knowledge from artificial intelligence, statistics and mathematics intending to give computers the ability to learn from data without being explicitly programmed to do so.

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