The aim of this study is to provide a discussion on the definitions and conceptual dimensions of Responsible Research and Innovation based on findings from the literature. In the study, the outcomes of a literature review of 235 RRI-related articles were presented. The articles were selected from the EBSCO and Google Scholar databases regarding the definitions and dimensions of RRI. The results of the study indicated that while administrative definitions were widely quoted in the reviewed literature, they were not substantially further elaborated. Academic definitions were mostly derived from the institutional definitions; however, more empirical studies should be conducted in order to give a broader empirical basis to the development of the concept. In the current study, four distinct conceptual dimensions of RRI that appeared in the reviewed literature were brought out: inclusion, anticipation, responsiveness and reflexivity. Two emerging conceptual dimensions were also added: sustainability and care.

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In this article, we present a study on teachers' perceptions about Artificial Intelligence (AI) as a tool to support teaching in Estonian K-12 education. Estonia is promoting technological innovation in education. According to the Index of Readiness for Digital Lifelong Learning (IRDLL), Estonia was ranked first among 27 European countries. In this context, our goal was to explore teachers' perceptions about cutting-edge technologies (in this case, AI) and to contextualize our results in the scope of Fairness, Accountability, Transparency and Ethics (FATE). We carried out a survey with 140 Estonian K-12 teachers and we asked them about their understanding and concerns regarding the use of AI in education and the challenges they face. The analysis of the survey responses suggests that teachers have limited knowledge about AI and how it could support them in practice. Nonetheless, they perceive it as an opportunity for education. The results indicate that teachers need support in order to be efficient and effective in their work practice; we envision that AI can be used to provide this support. Furthermore, we identified challenges that relate to the socio-cultural context of the study: for example, teachers perceived AI as a tool to support them in accessing, adapting and using multilingual content. To conclude, we discuss the findings of this work in relation to ethical AI, and elaborate on the implications and future aspects of this work in the context of FATE and participatory design of learning environments.

The term Responsible Research and Innovation has recently gained currency, as it has been designated to be a key-term in the European research framework Horizon 2020. At the level of European research policy, Responsible Research and Innovation can be viewed as an attempt to reach a broader vision of research and innovation as a public good. The current academic debate may be fairly enriched by considering the role that phronesis may have for RRI. Specifically, in this paper we argue that the current debate might be fruitfully enriched by making a categorial shift. Such a categorial shift involves moving away from the temptation to interpret responsible research and innovation in a technocratic way towards a more pluralistic vision that is rooted in the idea of phronesis. In the present context phronesis points the attention to the cultivation and nurturement of the researcher’s formation as a type of engagement with the actual practice of researching, a practice in which researchers (and other parties concerned) are called to apply judgment and exercise discretion in specific and often unique situations without the re-assuring viewpoint of the technician.

Herbert Simon introduced the term “docility” to define the tendency of human beings to get information from social channels. In this paper, we enrich this first definition with distributed cognition based arguments, and suggest that docile individuals modify the information they get, before passing it on to others. We present a simulation model of docile and non-docile individuals in organizations, where different docility attitudes (behaviors) are considered. In standard conditions, findings suggest that the above-average docile individuals remain below 20% of the number of workers in a given organization. This way, we outline potentials and limits of this intriguing concept.

The main aim of the present study is to assess whether the open learner model (OLM) is capable of promoting students' active thinking by enhancing their self‐regulation in online higher education learning environments. To this aim, we systematically reviewed the literature of the last three decades and found 67 articles, of which only a sample of 15 were considered. Based on the findings, we performed a narrative analysis of the studies concerning technological features of OLMs that cater to the three main aspects concerning self‐regulated learning, namely, cognition, metacognition and motivation. Our analysis of the literature confirmed that these three aspects are all subject to some measure of influence. In mutually interacting, these three components support learners to reach a better understanding of their learning process. Specifically, it seems that mostly all three type of OLMs, inspectable, negotiable, and co‐operative, with simple and complex graphical presentation of their learner models and capacity to colour‐code and compare—alike appear optimal for augmenting cognition, metacognition, and motivation. They seemingly do so through offering a wealth of techniques pertaining to knowledge, difficulties, and misconception visualization. The results presented suggest that OLMs have a positive impact on learners' active thinking regarding their learning process. Practitioner NotesWhat is already known about this topic Several educational environments have been enhanced with open learner models (OLMs) with the aim of supporting learners. OLM seems to be a potential avenue for improving higher education by supporting learners' active thinking about their learning process by enhancing self‐regulated learning. Despite growing interest in OLMs, there has yet to be any effort to systematically review current research studies investigating their potential to promote self‐regulated learning. What this paper adds All type of OLMs, with their varied visualization features, to some extent are useful for promoting cognitive, metacognitive, and motivational components of self‐regulated learning. In mutually interacting, the three components of self‐regulated learning support learners to reach a better understanding of their learning process, and thereby promoting active thinking. Implications for practice and/or policy We suggest that practitioners must first grasp the possibilities for the cultivation of self‐regulated learning contained in the particular features of each OLM, and then add this insight to their own expertise in order to best integrate them for supporting students' active thinking. While a number of specific features of OLMs seem pertinent to cultivating one particular element of self‐regulated learning, there remains the chance that, through the interaction of cognitive, metacognitive, and motivational tactics, these features could in fact make a more holistic and general contribution. This supports learners to move from active‐in‐behaviour to active‐in‐thinking activities in digital learning environments.

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