Artificial Intelligence (AI) literacy is a rapidly evolving research field. Due to the broad scope of AI literacy-related publications, a comprehensive analysis of the field is needed in order to examine the main characteristics of the current scientific output. Based on it, we conducted a bibliometric analysis of the field where we investigated the publications' evolution over time, research constituents (authors, countries, institutions, publication venues), collaboration patterns, and emerging trends. The findings point out that the United States of America (USA), China, Spain, and Germany are the most contributing countries in the AI literacy field. Moreover, the organizations that most contribute to the AI literacy field are the Massachusetts Institute of Technology, the University of Eastern Finland, and the Georgia Institute of Technology. Furthermore, KI - Künstliche Intelligenz, ACM Transactions on Computing Education, and IEEE Access are the most disseminating journals, and FIE, AAAI, SIGCSE, and CHI are the most disseminating conferences of AI literacy research. According to keywords co-occurrence analysis, machine learning, data, big data, deep learning, and ethics are the most addressed AI topics. Finally, based on the achieved results, this bibliometric analysis draws some conclusions regarding the AI literacy field and points out potential directions for future works.

Inspired by explanations of machine learning concepts in children’s books, we developed an approach to introduce supervised, unsupervised, and reinforcement learning using a block-based programming language in combination with the benefits of educational robotics. Instead of using blocks as high-end APIs to access AI cloud services or to reproduce the machine learning algorithms, we use them as a means to put the student “in the algorithm’s shoes.” We adapt the training of neural networks, Q-learning, and k-means algorithms to a design and format suitable for children and equip the students with hands-on tools for playful experimentation. The children learn about direct supervision by modifying the weights in the neural networks and immediately observing the effects on the simulated robot. Following the ideas of constructionism, they experience how the algorithms and underlying machine learning concepts work in practice. We conducted and evaluated this approach with students in primary, middle, and high school. All the age groups perceived the topics to be very easy to moderately hard to grasp. Younger students experienced direct supervision as challenging, whereas they found Q-learning and k-means algorithms much more accessible. Most high-school students could cope with all the topics without particular difficulties.