Precision agriculture provides important issues toward a more sustainable agriculture. Many farmers have the necessary technology to operate site-specifically, but they do not use it in practice, and thus available information and communications technology (ICT) systems are not used to their full potential. This paper addresses how to reduce the so-called “problem of implementation”, based on the knowledge that participatory approaches during the design and development process is one of the most important factors to frame technology adoption. The development of sustainable ICT systems through theories and methodologies from the fields of human computer interaction and user-centered design (UCD) is presented and an ongoing Swedish project for development of an agricultural decision support system (AgriDSS) for nitrogen fertilization is used as an example to frame the issue. The overreaching aim is to develop AgriDSSs that are sustainable in design as well as through design by stressing the importance of participatory approaches for the successful development of AgriDSSs. The Swedish project has the intention to apply a UCD approach, and some pitfalls on starting to use this way of working is identified as well as some suggestions on how to reduce them through co-learning processes. Despite the challenges presented in this paper, ICT can contribute significantly to long-term sustainable development. Thus, several competences and scientific disciplines need to act in concert to help develop a sustainable development of agriculture via a transdisciplinary approach that can make an impact on society at many levels.

The concept of “social situatedness,” that is, the idea that the development of individual intelligence requires a social (and cultural) embedding, has recently received much attention in cognitive science and artificial intelligence research, in particular work on social or epigenetic robotics. The work of Lev Vygotsky, who put forward this view as early as the 1920s, has influenced the discussion to some degree but still remains far from well known. This article therefore is aimed at giving an overview of his cognitive development theory and a discussion of its relation to more recent work in primatology and socially situated artificial intelligence, in particular humanoid robotics.

Industry 4.0 aims to support the factory of the future, involving increased use of information systems and new ways of using automation, such as collaboration where a robot and a human share work on a single task. We propose a classification of collaboration levels for Human-Robot collaboration (HRC) in manufacturing that we call levels of collaboration (LoC), formed to provide a conceptual model conducive to the design of assembly lines incorporating HRC. This paper aims to provide a more theoretical foundation for such a tool based on relevant theories from cognitive science and other perspectives of human-technology interaction, strengthening the validity and scientific rigour of the envisioned LoC tool. The main contributions consist of a theoretical grounding to motivate the transition from automation to collaboration, which are intended to facilitate expanding the LoC classification to support HRC, as well as an initial visualization of the LoC approach. Future work includes fully defining the LoC classification as well as operationalizing functionally different cooperation types. We conclude that collaboration is a means to an end, so collaboration is not entered for its own sake, and that collaboration differs fundamentally from more commonly used views where automation is the focus.

The human–robot interaction (HRI) field goes beyond the mere technical aspects of developing robots, often investigating how humans perceive robots. Human perceptions and behavior are determined, in part, by expectations. Given the impact of expectations on behavior, it is important to understand what expectations individuals bring into HRI settings and how those expectations may affect their interactions with the robot over time. For many people, social robots are not a common part of their experiences, thus any expectations they have of social robots are likely shaped by other sources. As a result, individual expectations coming into HRI settings may be highly variable. Although there has been some recent interest in expectations within the field, there is an overall lack of empirical investigation into its impacts on HRI, especially in-person robot interactions. To this end, a within-subject in-person study ( $$N=31$$N = 31 ) was performed where participants were instructed to engage in open conversation with the social robot Pepper during two 2.5 min sessions. The robot was equipped with a custom dialogue system based on the GPT-3 large language model, allowing autonomous responses to verbal input. Participants’ affective changes towards the robot were assessed using three questionnaires, NARS, RAS, commonly used in HRI studies, and Closeness, based on the IOS scale. In addition to the three standard questionnaires, a custom question was administered to capture participants’ views on robot capabilities. All measures were collected three times, before the interaction with the robot, after the first interaction with the robot, and after the second interaction with the robot. Results revealed that participants to large degrees stayed with the expectations they had coming into the study, and in contrast to our hypothesis, none of the measured scales moved towards a common mean. Moreover, previous experience with robots was revealed to be a major factor of how participants experienced the robot in the study. These results could be interpreted as implying that expectations of robots are to large degrees decided before interactions with the robot, and that these expectations do not necessarily change as a result of the interaction. Results reveal a strong connection to how expectations are studied in social psychology and human-human interaction, underpinning its relevance for HRI research.