This study describes one local upper secondary school in Finland during the COVID-19 pandemic. All teaching was changed to distant for around two months. The study describes students’ and teachers’ perceptions during that time. Participants responded to the survey four times and freely described their experiences five times. The number of participants varied from 56 to 72 students and 9 to 15 teachers at different times. The data were analyzed by descriptive statistics, cluster analysis, and qualitative content analysis. The main findings indicate that distance teaching was implemented very successfully. However, open comments and cluster analysis revealed many challenges. Students complained of heavy workloads and fatigue. Some students lost motivation. These difficulties did not disappear over time. The main challenges for teachers included non-authentic interaction and a lack of the spontaneity that in-person teaching provides. Teachers quickly learned to use technological platforms, but interaction through it was not of as high quality. Teachers were also worried about students’ progress. Teachers did not recognize students’ heavy workload and motivation problems in the way that students described them. The study provides several recommendations for future remote teaching.

This article presents results on how students became engaged and motivated when using digital storytelling in knowledge creation in Finland, Greece and California. The theoretical framework is based on sociocultural theories. Learning is seen as a result of dialogical interactions between people, substances and artefacts. This approach has been used in the creation of the Global Sharing Pedagogy (GSP) model for the empirical study of student levels of engagement in learning twenty-first century skills. This model presents a set of conceptual mediators for student-driven knowledge creation, collaboration, networking and digital literacy. Data from 319 students were collected using follow-up questionnaires after the digital storytelling project. Descriptive statistical methods, correlations, analysis of variance and regression analysis were used. The mediators of the GSP model strongly predicted student motivation and enthusiasm as well as their learning outcomes. The digital storytelling project, using the technological platform Mobile Video Experience (MoViE), was very successful in teaching twenty-first century skills.

The article reflects on teacher professional development as a continuum that starts during pre-service time, continues into the first years of newly qualified teachers’ induction phase, and spans a career-long development throughout their teaching careers. Finnish teachers work in contexts that provide high professional autonomy and agency in their work. Pre-service teacher education prepares them for this responsible role. In earlier years, in-service training occurred on training days and through short courses. The new trend sees teachers as developers in the whole school community. Teachers have researchbased orientation in pre-service teacher education, which makes them capable to design school-based projects and their own development as it relates to school development. The article introduces four cases in which new trends have already been implemented. These best practices are examples how to (1) support the school community to cross boundaries towards multi-professional cooperation, (2) design an innovative school community using a design-based approach together with many partners, (3) connect pre-service and in-service research-based teacher education in science, technology and math (STEM) teaching, and (4) promote induction for new teachers.

This special issue raises two thematic questions: (1) How will AI change learning in the future and what role will human beings play in the interaction with machine learning, and (2), What can we learn from the articles in this special issue for future research? These questions are reflected in the frame of the recent discussion of human and machine learning. AI for learning provides many applications and multimodal channels for supporting people in cognitive and non-cognitive task domains. The articles in this special issue evidence that agency, engagement, self-efficacy, and collaboration are needed in learning and working with intelligent tools and environments. The importance of social elements is also clear in the articles. The articles also point out that the teacher’s role in digital pedagogy primarily involves facilitating and coaching. AI in learning has a high potential, but it also has many limitations. Many worries are linked with ethical issues, such as biases in algorithms, privacy, transparency, and data ownership. This special issue also highlights the concepts of explainability and explicability in the context of human learning. We need much more research and research-based discussion for making AI more trustworthy for users in learning environments and to prevent misconceptions.