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In this article, we use the family resemblance approach as a framework to contribute to the debate about the similarities and differences between the constituent disciplines of STEAM (science, technology, engineering, arts and mathematics) and to explore the implications for education. The family resemblance approach has been used in science education in various ways, for instance, in teacher education and undergraduate teaching and as an analytical tool for examining science curricula and assessments. The relevant sense of application of the family resemblance approach for our purposes in this article is that it is a framework that has the potential to differentiate the disciplines underpinning STEAM. We explore the utility of the family resemblance approach for clarifying what is meant by the nature of STEAM and, subsequently, we elaborate on some practical examples drawn from a project conducted in Hong Kong with Year 7 (12–13-year-old) students to illustrate how the use of the family resemblance approach can help articulate a contrast of nature of science and the arts in school activities.

News media plays a vital role in communicating scientific evidence to the public during the COVID-19 pandemic. Such communication is important for convincing the public to follow social distancing guidelines and to respond to health campaigns such as vaccination programmes. However, newspapers were criticised that they focus on the socio-political perspective of science, without explaining the nature of scientific works behind the government’s decisions. This paper examines the connections of the nature of science categories in the COVID-19 era by four local newspapers in the United Kingdom between November 2021 to February 2022. Nature of science refers to different aspects of how science works such as aims, values, methods and social institutions of science. Considering the news media may mediate public information and perception of scientific stories, it is relevant to ask how the various British newspapers covered aspects of science during the pandemic. In the period explored, Omicron variant was initially a variant of concern, and an increasing number of scientific evidence showed that the less severity of this variant might move the country from pandemic to endemic. We explored how news articles communicate public health information by addressing how science works during the period when Omicron variants surge. A novel discourse analysis approach, epistemic network analysis is used to characterise the frequency of connections of categories of the nature of science. The connection between political factors and the professional activities of scientists, as well as that with scientific practices are more apparent in left-populated and centralist outlets than in right-populated news outlets. Among four news outlets across the political spectrum, a left-populated newspaper, the Guardian, is not consistent in representing relations of different aspects of the nature of scientific works across different stages of the public health crisis. Inconsistency of addressing aspects of scientific works and a downplay of the cognitive-epistemic nature of scientific works likely lead to failure in trust and consumption of scientific knowledge by the public in the healthcare crisis.

Nature of science (NOS) has become one of the major emphases in curriculum and assessment. This study explores what NOS is included and how this is achieved in the Hong Kong biology curriculum and 7-year high-stakes assessments. While conceptualizations of the official definition of NOS differ, this study employs the family resemblance approach (FRA) to analyze the inclusion of NOS in the curriculum and the high-stakes assessments. In one characterisation of FRA (i.e. Erduran and Dagher 2014), NOS comprises a cognitive-epistemic system and a social-institutional system. There is a total of 11 categories in these two systems and these categories are interconnected with one another. By adopting the epistemic network analysis (ENA), the frequency of connections among these NOS categories in the curriculum and the high-stakes assessments can be visualized and compared. Two main similarities are observed when comparing the epistemic networks of the curriculum and the high-stakes assessments: (1) a greater emphasis on the cognitive-epistemic rather than the social-institutional system and (2) the rarity of connections between categories in the social-institutional system. However, the high-stakes assessments differ from the curriculum in that they have more connections between the cognitive-epistemic system and the social-institutional system. Moreover, assessment items in the Hong Kong biology examinations involve a limited range of contexts and skills. Implications for the design of curricula and high-stakes assessments will be discussed.

The paper reports about the outcome of a systematic review of research on family resemblance approach (FRA) to nature of science in (NOS) science education. FRA is a relatively recent perspective on NOS being a system of cognitive-epistemic and social-institutional aspects of science. FRA thus consists of a set of categories such as aims and values, practices, knowledge and social organizations in relation to NOS. Since the introduction of the FRA, there has been increasing interest in investigations about how FRA can be of use in science education both empirically and practically. A journal content analysis was conducted in order to investigate which FRA categories are covered in journal articles and to identify the characteristics of the studies that have used FRA. These characteristics included the target level of education and focus on pre- or in-service teachers. Furthermore, epistemic network analysis of theoretical and empirical papers was conducted to determine the extent to which the studies incorporated various key themes about FRA, such as its transferability to other domains and differentiation of the social-institutional system categories. The findings illustrate an increasing number of empirical studies using FRA in recent years and broad coverage in science education. Although the social-institutional system categories included intraconnections, these were not as strong as those intraconnections among categories within the cognitive-epistemic system. Future research directions for the use of FRA in K-12 science education are discussed.

There is a growing application of Artificial Intelligence (AI) in K-12 science classrooms. In K-12 education, students harness AI technologies to acquire scientific knowledge, ranging from automated personalized virtual scientific inquiry to generative AI tools such as ChatGPT, Sora, and Google Bard. These AI technologies inherit various strengths and limitations in facilitating students’ engagement in scientific activities. There is a lack of framework to develop K-12 students’ epistemic considerations of the interaction between the disciplines of AI and science when they engage in producing, revising, and critiquing scientific knowledge using AI technologies. To accomplish this, we conducted a systematic review for studies that implemented AI technologies in science education. Employing the family resemblance approach as our analytical framework, we examined epistemic insights into relationships between science and AI documented in the literature. Our analysis centered on five distinct categories: aims and values, methods, practices, knowledge, and social–institutional aspects. Notably, we found that only three studies mentioned epistemic insights concerning the interplay between scientific knowledge and AI knowledge. Building upon these findings, we propose a unifying framework that can guide future empirical studies, focusing on three key elements: (a) AI’s application in science and (b) the similarities and (c) differences in epistemological approaches between science and AI. We then conclude our study by proposing a development trajectory for K-12 students’ learning of AI-science epistemic insights.

As students read scientific texts created in generative artificial intelligence (GenAI) tools, they need to draw on their epistemic knowledge of GenAI as well as that of science. However, only a few research discussed multimodality as a methodological approach in characterising students’ ideas of GenAI-science epistemic reading. This study qualitatively explored 44 eighth and ninth graders’ multimodal representations of ideas about GenAI-science epistemic reading and developed an analytical framework based on Lemke’s ( 1998 ) typology of representational meaning, namely presentational, organisational, and orientational meanings. Under each representational meaning, several categories were inductively generated while students expressed preferences in using drawn, written, or both drawn and written mode to express certain categories. Findings indicate that a multimodal approach is fruitful in characterising students’ semiotic resources in meaning-making of ideas about GenAI-science epistemic reading. We suggested implications regarding future intervention studies on tracking students’ ideas about GenAI-science epistemic reading using the analytical framework developed in this study.

There is a growing application of Artificial Intelligence (AI) in K-12 science classrooms. In K-12 education, students harness AI technologies to acquire scientific knowledge, ranging from automated personalized virtual scientific inquiry to generative AI tools such as ChatGPT, Sora, and Google Bard. These AI technologies inherit various strengths and limitations in facilitating students’ engagement in scientific activities. There is a lack of framework to develop K-12 students’ epistemic considerations of the interaction between the disciplines of AI and science when they engage in producing, revising, and critiquing scientific knowledge using AI technologies. To accomplish this, we conducted a systematic review for studies that implemented AI technologies in science education. Employing the family resemblance approach as our analytical framework, we examined epistemic insights into relationships between science and AI documented in the literature. Our analysis centered on five distinct categories: aims and values, methods, practices, knowledge, and social–institutional aspects. Notably, we found that only three studies mentioned epistemic insights concerning the interplay between scientific knowledge and AI knowledge. Building upon these findings, we propose a unifying framework that can guide future empirical studies, focusing on three key elements: (a) AI’s application in science and (b) the similarities and (c) differences in epistemological approaches between science and AI. We then conclude our study by proposing a development trajectory for K-12 students’ learning of AI-science epistemic insights.

Researchers in science education lacks valid and reliable instruments to assess students’ disciplinary and epistemic reading of scientific texts. The main purpose of this study was to develop and validate a Reading in Science Holistic Assessment (RISHA) to assess students’ holistic reading of scientific texts. RISHA measures students’ content, procedural, and epistemic domains of reading two texts, one history-of-science text and another socio-scientific text. The initial 24-item RISHA was administered to 161 Grade 9 students from 3 schools. The multidimensional Rasch partial credit model was used to analyze the reliability and validity of RISHA. All items demonstrated good fit and reliability. According to logit scores generated for each domain in Rasch analysis, students in our study performed better in content domain and less well in the epistemic domain. Students also performed significantly better in the epistemic domain of the socio-scientific text than in the history-of-science text. RISHA provides accurate measures in various domains of reading scientific texts and various contexts of scientific texts. We propose that RISHA could potentially be applied to studying the effect of reading-science intervention or predictors of students’ performance in each domain of reading scientific texts.