Ecological systems are the quintessential complex systems, involving numerous high-order interactions and non-linear relationships. The most used statistical modeling techniques can hardly accommodate the complexity of ecological patterns and processes. Finding hidden relationships in complex data is now possible using massive computational power, particularly by means of artificial intelligence and machine learning methods. Here we explored the potential of symbolic regression (SR), commonly used in other areas, in the field of ecology. Symbolic regression searches for both the formal structure of equations and the fitting parameters simultaneously, hence providing the required flexibility to characterize complex ecological systems. Although the method here presented is automated, it is part of a collaborative human–machine effort and we demonstrate ways to do it. First, we test the robustness of SR to extreme levels of noise when searching for the species-area relationship. Second, we demonstrate how SR can model species richness and spatial distributions. Third, we illustrate how SR can be used to find general models in ecology, namely new formulas for species richness estimators and the general dynamic model of oceanic island biogeography. We propose that evolving free-form equations purely from data, often without prior human inference or hypotheses, may represent a very powerful tool for ecologists and biogeographers to become aware of hidden relationships and suggest general theoretical models and principles.

Recently, efforts have been made to add programming activities to the curriculum that promote computational thinking and foster 21st-century digital skills. One of the programming modalities is the use of Tangible Programming Languages (TPL), used in activities with 4+ year old children. In this review, we analyze solutions proposed for TPL in different contexts crossing them with non-TPL solutions, like Graphical Programming Languages (GPL). We start to characterize features of language interaction, their use, and what learning activities are associated with them. Then, in a diagram, we show a relation between the complexity of the languages with factors such as target age and output device types. We provide an analysis considering the type of input (e.g., TPL versus GPL) and output devices (e.g., physical robot versus graphical simulation) and evaluate their contribution to further insights about the general trends with respect to educational robotic systems. Finally, we discuss the opportunities to extend and improve TPLs based on the different solutions identified.

The Vulcano challenge is a new and innovative robotic challenge for legged robots in a physical and simulated scenario of a volcanic eruption. In this scenario, robots must climb a volcano’s escarpment and collect data from areas with high temperatures and toxic gases. This paper presents the main idea behind this challenge, with a detailed description of the simulated and physical scenario of the volcano ramp, the rules proposed for the competition, and the conception of a robot prototype, Vulcano, used in the competition. Finally, it discusses the performance of teams invited to participate in the challenge in the context of Azorean Robotics Open, the Azoresbot 2022. This first test for this challenge provided insights into what the participants found exciting and positive and what they found less positive.

This paper described the functionalities of a simple robot adopted in the classroom to promote computational thinking (CT) in the context of the project PeCOT—computational thinking with tangible objects. This robot, similar to some commercial educational robots, was developed to be used in primary education classroom contexts (second, third, and fourth grades) by children between 8 and 10 years old. PeCOT is a pedagogical intervention project and research project that will run over the next 2 years. The aim of this paper is to present the robot, how it works, and some activities that can be implemented in the educational context to promote CT skills, including learning in different subject areas, such as mathematics or natural science. Thus, we begin by presenting a general description of the robot. Next, we identify activities and formalize the programming steps for each of the activities. Finally, we discuss the potential that the robot and the proposed activities may have to promote the development of CT skills.