The digitalization of science education: Déjà vu all over again? 1 | INTRODUCTION This special issue set out to provide a platform for reporting on empirical research that examines the use and impact of 21st century cutting-edge digital technologies and ecologies on science teaching, learning, and assessment. For decades technologies, and more recently digital technologies, have been said to revolutionize education, STEM education and, more specifically, science education. This movement started in the 1980s, when personal computers began to become available in classrooms. Personal computers, together with the respective software opened up a wealth of new possibilities in teaching and learning. Simulations provided dynamic visualizations of complex content in order to better support students in mastering understanding of these contents (Marks, 1982), and interactive learning programs based on videos allowed students to work through the curriculum at their own pace enabling a more individualized learning experience (Leonard, 1985). The former quickly developed into a vast amount of software tools designed to support students' science learning-from tools designed to model authentic phenomena (Doerr, 1996) to simulations of laboratory environments to engage students in authentic inquiry (Niesink et al., 1997). The latter, partially fueled by the advent of the internet and related technologies such as the hypertext protocol, developed into a variety of computer-based learning environments-from online (e)learning environments provided for remote, self-determined learning (Ajadi, Salawu, & Adeoye, 2007) to intelligent tutoring systems designed to automatically monitor and support students' learning (Graesser, Conley & Olney, 2012). More recent developments are driven by substantial increases in computing power on the one hand and research findings suggesting that (digital) technologies, such as simulations or modeling tools, alone are not necessarily helping students' learning, but instead need to be embedded in meaningful curriculum (e.g., Zhang, 2012; for an overview see Krajcik & Mun, 2014) on the other. Many new developments in the field engage students in authentic learning experiences through simulations of the real world (Barab et al., 2009), and integrate multiple individual technologies, such as simulations, modeling, or data analysis tools into a carefully sequenced curriculum activities (Gerard, Spitulnik & Linn, 2010). Most recent developments even integrate an automated tracking of students' learning and respective supports either through the learning environment itself or through the teacher (Gobert et al., 2013; Gobert & Sao Pedro, 2017). And the future is envisioned even brighter: Augmented Reality devices are expected to create authentic learning experiences, and Artificial Intelligence to allow for more open, exploratory (e)learning environments that automatically guide students in their learning based on their specific needs. All these technologies are said to be soon delivered through low cost person...