Epistemic Fluency in Interdisciplinary Learning Environments; Interdisciplinary education and the complications of integration

Renate G. Klaassen ^1* Miles MacLeod ^2,3 Kostas Nizamis ⁴ Siara R. Isaac ⁵

• ¹ EEMCS, Delft Institute of Mathematics, Prime- Research, Delft University of Technology - 4TU Centre for Engineering Education, Delft, Netherlands
• ² University of Twente, Enschede, Netherlands
• ³ Department of Philosophy, Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, Netherlands
• ⁴ Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
• ⁵ Center for Learning Sciences, Ecole Polytechnique Federale de Lausanne, Lausanne, Geneva, Switzerland

Nowadays, learning occurs increasingly in collaboration with the broader world, leading to emerging complex and dynamic situations (Herrington et al., 2000;Ankrah et al., 2015). It poses a challenge to problem-solving, as moving problems, changing contexts, and the need to rely on a plethora of methods from multiple disciplines are the new normal (Lury, 2021;Dorst, (2015); Dooremalen (2021, p. 559). Interdisciplinary education is seen by many as 'the solution' for dealing with emerging complexity (Markauskaite, et. al, 2024). It is therefore important to clarify what interdisciplinarity is? Interdisciplinarity is characterised by many as a process of integrating theory, methods, approaches, and knowledge of two or more different disciplines to create innovative solutions (Lattuca et al., 2012;MacLeod, 2018). However, one type of integration can be unlike another. For instance, integrating art and history is quite different from an approach combining natural coastal defense systems involving biology, engineering, and environmental science (Boix Mansilla, 2016). In transdisciplinary education, integration of disciplines may also involve bridging of different knowledge systems (Kilic-Bebek et al., 2023). Typical (conceptual) problem definitions taught in disciplinary courses may no longer hold in complex situations where human control is shared, for example, on networked platforms. During the problem definition process, AI or other data can be involved in creating new methods that do not build upon the senses or classical empirical data-gathering methods (Lury 2021;Dooremalen et al. 2021). Consequently, the cognitive and conceptual structure of scientific problem-solving, as we currently know it, may limit our ability to move towards the different modes of learning required for interdisciplinary work (MacLeod, 2018). Accordingly, developing students' capacity for "integration" requires educators to reconsider the nature of the considered knowledge and which knowledge systems are addressed in different pedagogical situations (modes of learning) within and extending beyond the university walls (Barnett & Jackson, 2020).What we propose to do in this introductory paper is to provide theoretical motivation for the concept of epistemological fluency, developed by Markauskaite (2018), which we take as a primary skill or ability requirement students should obtain for handling interdisciplinary environments. Epistemic fluency is the capacity to understand, switch between and combine different kinds of knowledge and participate in different ways of knowing about the world, (Markauskaite & Goodyear,2018). The concept of epistemological fluency derives from a theoretical understanding of the kinds of knowledge abilities relevant to different problemsolving contexts, and the abilities of students to identify the contexts, adapt and flexibly move between them. These categorisations will be used to identify the level at which the proposed I n r e v i e w competencies and activities discussed in the different articles in this special issue might be situated. Our goal is to help educators both understand the motivations underlying epistemic fluency, but also identify papers in this special issue that are relevant to particular challenges they encounter in their teaching, such as uncertainty or complex situations.We propose using Savin-Baden's concept of level of knowing to identify the conditions which will support students to develop epistemic fluency, and particularly those levels which involve uncertainty (see also section 1.2.). Savin Baden (2014Baden ( , 2020) ) identified five levels of knowing, which she defines as "modes of learning", that address different levels of knowledge systems/creation. We might assume these modes are representative of levels of interdisciplinary complexity in learning contexts:• Mode 1 is traditional disciplinary knowledge creation within academia;• Mode 2 is the knowledge that transcends different disciplines and is validated by the world of work; • Mode 3 is knowing and acting in situations characterised by, acting in and with uncertainty, identifying epistemological gaps; in real life ambiguous and unknown situations in real life; • Mode 4 is focused on unknown knowledge, including uncertainty, gaps between different pieces of knowledge (hierarchies) and finally; • Mode 5 addresses various knowledge systems in complex, dynamic and uncertain contexts.It is suggested that with the emerging and increasing complexity of different learning modes (Savin-Baden, 2014, 2020), traditional ways of creating epistemic knowledge within disciplinary subfields are under pressure and are no longer optimally efficient or effective. Further, these different knowledge modes affect the pedagogical situation, problem-solving strategies and the notion of interdisciplinary knowledge integration. In mode two, learning environments involving the interdisciplinary integration of two or more disciplines pose challenging problems, according to MacLeod, such as disparate values and incompatible methods of epistemic knowing across disciplinary divides (2018). As Boon (2020) describes going from an epistemological perspective linking and integrating (in interdisciplinary contexts) is perhaps one of the severest challenges of utilising highly-fragmented scientific disciplines in solving highly-complex "real world" problems. In interdisciplinary education or research, "expertise" then means students, teachers and researchers can use, link and integrate "knowledge (i.e. epistemic resources including data, concepts, models, and theories) and methods from different disciplines to generate knowledge for solving real-world problems.To varying degrees, problem-solving practices within domains are functionally dependent on, and distributed amongst, epistemic principles, conceptual tools, material, social, and technological environments, and practices, as well as tacit and intuitive knowledge, form a "system of practices" (Chang, 2014;MacLeod, 2018). These practices are adapted to specific phenomena and contexts, allowing researchers to validate, act, and think of their results for the I n r e v i e w domains addressed. However, uncertainty about knowing what to know, the discovery of epistemological gaps in one's knowledge and the different purposes and values of knowledge systems all impact learning and possibly slow down the integrative activities in inter and emerging transdisciplinary contexts. These challenges should be dealt with constructively. The result is that even in basic cases, interdisciplinary learning requires active reflection on how to overcome knowledge gaps, uncertainty, complexities, etc., and students might need to be trained to overcome such challenges. Whereas teachers are concerned with the kinds of skills or attitudes students should employ to respond to the context effectively. These challenges are compounded when external-academic knowledge systems become involved, and uncertainty increases as per higher modes of learning. In Figure 1, we summarise the variables that cause learning in complex situations to be challenging and how these variables reciprocally influence one another.This diagramme illustrates why traditional disciplinary skills are inadequate and why students require a multiplicity of different skills which adapt them to these different contexts or enable them to move flexibly between them. One cannot be flexible and adaptive towards different context and ways of knowing if one cannot discern the underlying variables that interact and shape it.Paradigmatic beliefs are the belief systems, with the fundamental assumptions, theories, etc, through which we view and interpret the world. In higher education contexts, it helps to establish the legitimacy of a research question, the use of methodologies and ways of interpreting data. The belief in positivism, for example, is that verifying predetermined hypotheses through experimentation provides objective and quantitative scientific proof or predictions (Park et al., 2019).These beliefs are profoundly influenced by how knowledge in knowledge systems, e.g. quadruple or quintuple helix systems such as government, academia, industry and society emerge (Caryannis et al., 2018).; shaping epistemological communities in which knowledge is organised, validated, and shared. Social actors acquire, exchange and evaluate knowledge in a certain way, while they are influenced by historical events, social dynamics, cultures, and situations in which their knowledge emerged (Wang et al., 2022). In education, these interaction effects should trigger critical reflection on the paradigmatic beliefs and knowledge systems and instigate questioning behaviour to understand the system of practices in context better (Chang, 2014;MacLeod, 2018).Stakeholder knowledge is the accumulation of expertise and information of individuals or groups engaged in the inter/transdisciplinary learning environment or beyond in the professional practice (Gerlak et al.,2023). Stakeholder knowledge may thus moderate and be moderated by paradigmatic beliefs and, therefore, the organisation of the knowledge system.These peripheral variables in Figure 1, paradigmatic beliefs, knowledge systems and stakeholders interactively shape a situated environment and engagement on online platforms. The intended input is moulded and restricted by contextual or platform constraints, where interaction loops with the (online) situation, will give rise to dynamic changes.One more pervasive variable is the uncertainty arising from the interactions in the complex environment. We will therefore briefly explain the uncertainty variable, also identified by Savin-Baden (2014, 2020), which determines the complexity of the modes of learning.Uncertainty is expressed within different fields as probability or likelihood of something that will or will not occur (philosophy-mathematics) (Colombo et al., 2021). It is also called epistemic uncertainty, where factual knowledge is quantifiable and can be calculated with an error margin (expected uncertainty or known knowledge gaps). The apparent certainty that is created through calculation is, however, unreliable. An increase in quantifiable uncertainty makes people, for example, more conservative in their decision-making strategies, depending on the background information available (Preuschoff, et. al, 2013). This shows that decision making can be rather circumstantial with numerical dependencies based on the choice of quantification methods, but also influence risk averse behaviour. However, in, sustainability, economics, policy, social and urban sciences, uncertainties are approached as "the identification of the level of unknowns', where uncertainty takes on a more ontological way of knowing, called non-quantifiable or even non-identifiable knowledge. This type of unquantifiable knowledge dramatically decreases the capacity of humans to deal with uncertainty. Some posit that human decision-making is at the heart of dealing with these uncertain situations (van Bueren and ten Heuvelhof, 2005;Dooremalen et al., 2021).(Un)fortunately, decision-making processes are also based on paradigmatic beliefs emerging from prior knowledge, personality, and social and cultural background parameters. Therefore, these beliefs also pre-determine how we (adaptively) handle uncertainty (Figure 1). The unquantifiable uncertainty, such as a lack of knowledge about the stakeholder's perspectives or environmental inaccuracies, can equally trigger a host of inappropriate decision-making patterns. Unexpected and volatile uncertainties involve therefore multiple dimensions such as expectations, continuous evaluations, risk tolerance and reward (Bland & Schaefer, 2012). Unravelling uncertainty does not necessarily increase epistemic fluency, rather the awareness of uncertainty and navigating the (emergent) uncertainties via strategies that help create "new" emergent knowledge conceivable both guide and necessitate the acquisition of epistemic fluency in context.As such the emergence of epistemic fluency depends on our understanding of how knowledge emerges (2.1), the nature of conceptual knowledge (2.2) and how the environment supports knowing (2.3). How knowledge emerges is a precursor of understanding knowledge via identification of conceptual constructs, processes and methods. The expanding nature of conceptual knowledge explores how knowledges is built and fits in the system of knowledges. How the environment supports knowledge is by positioning in epistemic environments and the adaptation of knowing in these environments. These three topics are explored in the following three subsections, particularly how theories of each can help inform both what epistemic fluency entails in depth, but also the conditions under which it can be developed. In the final section the contributions of the articles included in this special issue will be positioned within the theoretical framework.To develop instructional approaches, we need to begin with an understanding of how knowledge typically emerges in complex situations, in which multiple knowledge systems and uncertainty are involved. According to Markauskaite (2018) conceptual knowledge is the basis of cognition that can be situated in the world, yielding embedded actions and providing resources from which humans organise their conceptual system. This requires first and foremost awareness and identification of conceptual constructs, processes and methods.Becoming aware is however a meta-activity guided by reflections, dia or-trialogue learning, the use of artefacts, embodied cognition. It allows for continual interpretation, investigation and reflective conversation with oneself and others about a topic/problem, using information from theory, experience and interaction to guide and inform new actions. Schon (1991), Ramsden (2003) further unravel "awareness" and have pointed out that knowing requires the elements of reflection in action and reflection on action to make sense of what happens in the world. Reflection in action is based on tacit, practical knowledge and our habitus or even embodied cognition that guides our activities using our senses. When giving actions meaning at a higher cognitive level, reflection on action is required that can take place in different situational contexts. Reflection on action helps to frame the intellectual (internal) dialogue through emerging experience, awareness and the creation of cognition. This reflection on action is supported by the following activities or working methods (informed by Bauters, 2017); -The use of physical artefacts or affordances that help us create intelligent activity, also called boundary objects, that, ideally, bring about shared meaning. -The social encounter with others in collaboration or (social) practices is an engagement in perspective-taking and creating a space in which the known is the known we commonly believe, accept as true and feel is justified. -The temporal use of representations and means to frame our common understanding.-Embodied cognition in which the implicit meaning and information from the senses guide our knowing.Typically, these activities support the types of knowing, that are related to paradigms of representational or propositional knowing. Representational and propositional knowing and approaches are, for example, studied and discussed as representative schemata (Bereiter, 1992) and mental resources (di Sessa, 2008), social and cultural discourses of shared meaning-making (Engstrom, 2008), the brain and creative intelligence (Damassio, 2012). Lury (2021) points out, however, that the new emerging learning environments are dominated not by representation or proposition, but by participation. Participation is distributed at various levels of engagement with multiple disciplines, stakeholders and (knowledge)platforms, who shape a problem space instead of a problem definition. The problem space requires using compositional methodologies that help deal with new emerging epistemic knowledge.Compositional methodologies can be defined as processes and practices by which events or occasions come into being (Lury, (2021), Markauskaite's, (2018)) . New epistemic knowledge is not merely a mutual understanding, a common notion of how to work, but a creation of new knowns and unknowns, of operational values of decision-making, use-ability or I n r e v i e w actionability. Incorporating the new knowns requires the participant to go beyond the knowable. Indeed, it requires the participants to reciprocally align and exchange changing information (Galotti, 2017) and to be autonomous, responsible and accountable for the answers that are provided within a particular context (Bauters, 2017).Due to the diversity in types of knowledge building situations, Markauskaite provides a systematic categorization of skills relevant to handling different aspects of knowledge building processes in complex contexts. Ultimately students should acquire the ability to engage in different circumstances and know what is appropriate for a given set of circumstances. This ability is captured by the notion of epistemological fluency which is defined by Markauskaite and Goodyear (2018) as the skill of being flexible and adaptable concerning different kinds of (inter and trans)disciplinary and context-specific knowledge and different ways of knowing about the world. Markauskaite (2018) states that how knowledge emerges is recognising the constituent elements within the dynamic knowledge systems/ problem spaces and identifying the interaction between these elements within the system while addressing a specific task in a particular environment. She identifies five particular skills requirements: conceptual resourcefulness, epistemic resourcefulness, dynamic conceptual resourcefulness, dynamic epistemic resourcefulness and dynamic resourcefulness. The conceptual knowledge and resourcefulness are closest to what we know, while the others increasingly move away from knowledge gathering as we know it in traditional academia (see Table 1, column 3 "focus of learning").Conceptual knowledge/ epistemic resourcefulness (see Table 1, modes 1+2) can be defined as situated and grounded in specific situations, experiences, and actions. It is not a single abstracted representation of a category but rather a skill for constructing idiosyncratic representations tailored to the needs of situated action. Conceptual categories are remembered through situated information (Markauskaite, p.601, 2018); these are:• selected properties of the conceptual category relevant to the situation,• information about the background setting,• possible actions that could be taken, and • perceptions of internal states that one might have experienced during previous encounters with the conceptual phenomena (such as affects, motivations, cognitive states operations).These four steps help prepare humans for situated action and goal-directed guiding activities while bridging the gap between theory and practice.In epistemic resourcefulness, the learner learns about "knowing about knowing" by activating disciplinary reference frameworks and schemas, allowing for pattern recognitionand uncovering methods for justification and explanations. This results in a specific type of epistemic understanding, questioning and information-gathering strategies, and a profound sense of self-identification with a group. These outcomes foster authentic enquiry, a process scaffolded by the educator, and promote a sense of belonging and community in the learning journey.An expansion on this notion is the dynamic conceptual, epistemic, and grounded resourcefulness. The dynamic conceptual level of knowledge (see Table 1, mode 3) particularly addresses the interaction of artefacts, humans, and systems in the situated space.The interaction creates rich relationships and emerging conceptual knowledge not yet discovered, spanning mind, body, and world as a source of input (Markauskaite, 2015). Selfengineering of the mind and the environment is a precondition to dealing with this emerging linguistic materiality. Linguistic materiality is giving meaning through the exchange of perception and acting upon artefacts, platforms, or doing things in interaction with stakeholders, grounding knowledge and knowing in meaning-making in rich semiotic contexts. Similarly, to "reflection on action", one needs all the senses to reflect and act upon constructs (constituent concepts), processes of inquiry, and environment, while coordinating the emerging and grounded knowledge formation.Dynamic epistemic resourcefulness (Table 1, modes 4) occurs at the level of shaping or designing the environment towards an eco-system in which touch-points trigger conscious self-engineering of the learning and knowing that help students' address emergent issues in the problem space. Co-creation and participation become essential to jointly orchestrate spaces of open learning, such that all the participants become able to create coherence from the incoherent and value from value options. Uncertainty is the driver that allows for curiosity and agency towards becoming responsible and accountable. Thus, ultimately, education is a form of leadership that enables collective discernment of values, aspirations and constituent patterns the systems might be based on and which support the creation of emergent knowing (Ioannou, 2023).Grounded epistemic resourcefulness (table 1, mode 5) then seeks to consolidate agency in professional contexts, where the learner is sensitised and operates in harmony with the affordances, constraints and other relevant dimensions of the encountered situations. Furthermore, It requires the learner to reconfigure or reshape "the ground" to accommodate the requirements of an encountered situation by using the senses, cognition and acting upon the world (Markauskaite, 2018).Both the dynamic conceptual level ( As mentioned, the support of knowing, or emergent knowledge construction on these views comes about through a shift in focus towards constructs, processes and environments that allow for coordinating mind-body-world experiences. The constructs for knowing are needed to perceive conceptually and sense intelligently. Processes are needed to determine the optimal methodology for addressing a problem space and the transcendence beyond disciplines or isolated knowledge systems. Furthermore, the environment provides the opportunity to experience responsibility for learning, critical reflection, collaboration, and co-construction of knowledge in dialogic, trialogic and professionally situated eco-systems or environments.Learning, then, takes place by creating and positioning oneself in an epistemic environment where different activities and participation are offered to stimulate the learning of epistemic fluency, allowing for new ways of combining descriptions and causalities of emerging epistemic knowledges. The emergence of epistemic fluency depends on our understanding of (1) how knowledge emerges, (2) the nature of conceptual knowledge, and (3) how the I n r e v i e w environment supports knowing (through artefacts/objects, stakeholders, embodied sensing), each soliciting a grounding in experience, environment and temporal, embodied action (Markauskaite, 2018). These five learning approaches listed in Table 1 and described in the previous section on expanding the nature of conceptual knowledge, match to a certain extent, Savin-Baden's modes of learning (& section 1.1), with increasing levels of uncertainty expressed in epistemological gaps or progressively complex and different knowledge systems. Knowledge systems range from different scientific domains to industry, governance, environmental or societal knowledge systems. In addition to identifying the mode of learning, the focus of learning, the approach to learning and the aim of learning are included to understand the shift in focus that occurs when the mode of learning changes. We propose an integrated arrangement of epistemic fluency levels, activities and aims of learning, informed by Markauskaite and Savin Baden, in Table 1. The integration allows for the identification of learning contexts and the epistemic levels of fluency needed in "a mode" context. In Table 2 in the conclusions, the modes are linked to the 6 papers that comprise this special issue and to the type of epistemic fluency addressed. First, we will, however, briefly discuss the core of the six papers, their environments and approaches in supporting the development of epistemic fluency, such that the positioning of the papers in table 2 will logically follow.In this editorial, the suggested categorisation based on Savin-Baden's work is used to identify the level at which the proposed competencies and activities discussed in the different articles might be situated. In particular, the papers in this special issue explore what integration may entail at the higher modes of learning (Table 1, rows 3,4,5) and what the required epistemic fluency entails. We laud the authors who experimented with embedding epistemic fluency within their educational environment, providing students with the opportunity to experience the relevance of their conceptual knowledge in a situated context (expanded educational environments) where practical knowledge and a range of (theoretical and) conceptual knowledge interact to create emerging knowledge. These articles address competencies and skills that help the development of epistemic fluency and that will guide student decisionmaking and coordination of embodied knowledge through complex and uncharted territories.The contributions to the special issue cover the following issues relevant to training epistemic fluency as we have explored it above: I n r e v i e wBeckerle et al. ( 2022), argue that interdisciplinarity is traditionally ingrained in biomedical engineering, where engineers, designers, doctors, movement scientists, roboticists, software engineers, and others must work together (ref, this issue). This interdisciplinarity creates a need for integrated education and new didactics methods to engage with the plethora of disciplines. Beckerle et al. (2022) introduces the importance of "four design considerations" established at six German universities. The German universities have years of experience in dealing with the collaboration and problem-solving processes in interdisciplinary contexts. These four instructional design considerations are Communication, Integration, Blending, and Orientation. Communication is about establishing a precise terminology that draws inspiration from the disciplines involved and is understandable as a reference language linked to the disciplines involved. Integration refers not only to in-class interaction but also to the codevelopment of didactics, learning objectives, and co-analysing of learning processes. The interaction between the different disciplines, flipping the classroom methods and pushing students to assume different roles and viewpoints help students to develop a joint understanding of the problem they are trying to solve. Blended learning and didactics beyond the traditional (seminars, small-scale projects, application of theory in a realistic context) can help create richer educational experiences for all students. Lastly, it is vital in an interdisciplinary context to offer proper structure and directions to students (orientation), especially those who need to become more familiar with interdisciplinary types of learning (Uthrapathi-Shakila et al, 2021) These four elements of interdisciplinary education are interconnected and can remarkably reinforce each other. This article's learning activities and knowledge creation are performed at a mode 2 level, "inquiry and problem-solving processes," with occasional mode 3, "creating new knowledge" levels. Participating in established inquiry and problem-solving practices allows the teacher to scaffold the complexity and uncertainty of knowledge acquisition and allow for guidance in uncertain territory. The curriculum is chosen to give a firm basis to explore and create new knowledge on the different knowledge scales that might be encountered.Van Goch (2023) has shared with us the design inquiry process of interdisciplinary research at a liberal arts and science department of a general university and particularly how students connect different knowledge(s) and different ways of knowing to conduct interdisciplinary research. Van Goch (2023) identified practical constraints, the need for collaboration and the impossibility of knowing all the relevant sources. For the success of an interdisciplinary research course on design inquiry in mode four, new inquiry processes should be developed, and emergent constructs based on existing concepts explored. This challenge may not be feasible yet at the undergraduate level, where the learning is still very much and forcibly linked to the disciplinary background of the students and does not I n r e v i e w necessarily involve external stakeholders and environments. Nevertheless, it is an excellent start into exploring the potential cognitive development of students via reflection on action, into students who are facing the encounter with wider academic contexts and starting to create new knowledge. It also shows how ambitions for higher modes of learning should fit the wider eco system of curricular knowledge development. We see that a mode 4 design can result in a mode 2 activity if there is a misalignment between the target group and the context.Like van Goch, Lambalgen & Vos (2023) unpack how knowledge emerges in an interdisciplinary research course of a liberal science arts bachelor programme. They ask "how do students making use of the platform Miro, solve interdisciplinary problems in teams?" They particularly looked at the level of construction, conflict, co-construction and integration as a mental process of students who engage with interdisciplinary research. Importantly, they point out that epistemic fluency is not a competence with a beginning or an end. Epistemic fluency is in itself a regenerative process emerging with different starting points and iterating to different or more in-depth types of epistemological knowing and facilitated through different means of reflection in and on action. Another observation concerns the importance of differentiating between each individuals' epistemic fluency and that of a group. They demonstrate the necessity of participation in groups to raise the average level of interdisciplinary integration and the growth capacity for epistemic fluency. Students initially tend to be focused on their peers to cover the knowledge gaps. Successively students' consult frequently with the commissioners in this environment to get to grips with their unknowns. Finally, dealing with uncertainty appears not only to be a cognitive endeavour solved by analytic reasoning alone, but shows it is first and foremost, an attitudinal and affective issue that is a part of the learning process in increasingly complex environments.This study provides first insights into metacognitive uncertainty strategies and suggests those strategies should become a more prominent topic in coaching students. When uncertainty becomes an explicit part of inter-transdisciplinary education, students learn to deal with both the known and unknowns in the transition towards a sustainable society.In this special issue, Norris, Grohs, and Knight (2022) focus on a fluid thermodynamics course in which learning takes place and system thinking is assessed through scenario-based I n r e v i e w evaluation. They are posing a systems approach as the most appropriate method to address diverse contexts and obtain a systems view of complicated/complex problems. They show that students are initially clueless, with no structured approach towards a complex problem. Norris et al. (2022) finds students reduce complexity while creating problem schemata based on reductionism, prior experience, and assessment prompt-driven approaches. Eventually, students learn how to deal with the difficulties of different emerging epistemologies. They learn to determine through systems engineering practices and the acquisition of competencies which of the systems modelling approaches contribute to operating in increasingly complex systems and which approaches do not. These include the identification of knowledge gaps, the identification of relevant constructs, and the recommendation for analogical reasoning to abstract the underlying reasoning skills across domains. How knowledge emerges in the system emphasises the relations and processes needed in iterative cross-sector and collaborative endeavours. Furthermore, different environments in cases or real-life authentic settings offer knowledge development linked to a particular knowledge system. The space created allows for participatory problem-solving in online and offline contexts, centring communication, integration, and orientation as critical decision-making devices on how to proceed.Evolving interdisciplinary engineering education aims to equip engineers with the ability to tackle complex problems beyond traditional disciplinary boundaries. This competency demands not only disciplinary knowledge and skills but also higher-order thinking skills (HOTS) necessary to navigate interdisciplinary paradigms. The last paper in this issue from Sivakumar and Boon (2024) focuses on one such HOTS; encompasses complex cognitive processes that go beyond the basic process of recalling. In this paper focused on Intellectual Humility. Sivakumar and Boon (2024) I n r e v i e wHow does one acquire epistemological fluency for inter and transdisciplinary practices and how do we shape the educational environment to accommodate this type of learning. We discussed these are bounded to (1) how knowledge emerges, (2) the nature of knowledge and(3) the situatedness of learning. Based on the notion of pedagogical modes we found epistemological fluency takes on a different colour in terms of conceptual, dynamic and epistemic resourcefulness for understanding that is being offered in a particular context. Meaning each context needs to be unravelled along these three dimensions to be able to offer the appropriate methods and scaffolds for learning, such as reflection in action and on action with the support of peers, artefacts, temporal use of representations, embodied cognition, besides participation as key drivers. Here we find suggestions of methods for modes 2, 3 and 4 for learning of epistemic fluency, ranging from pedagogical designs, facilitating skills building to challenging student towards systemic action, and reflection on and in action. The contributions from the 6 articles comprising this special issue are summarised in Table 2, a more applied interpretation of Table 1 modes of learning that focuses on learning approaches in practice. Note, however, none of the proposed methods have ventured in the 5 th mode of learning where participation in the creation of problem spaces and possible solutions are addressed to the extent that participants are autonomous, responsible and accountable for the answers that are provided within a particular context. Possibly this fifth mode of learning is appropriate for a PhD, Post-master level of learning and requires a level of maturity that cannot be expected at an earlier stage in the learning cycle (Barnet & Jackson, 2020).As Lury (2021) puts it, selecting an environment is an opportunity to experience and demarcate a situation in which problems might be transformed by both acting upon them and being acted upon by the environment A situation one might call integrative pluralism (Dooremalen et al., 2021), which is a process in which co-evolution of theoretical and empirical progress requires mutual reinforcement of, on the one hand, coherent and consistent theories and, on the other empirically solidified facts, which are "true". Klopp et al. describe integrative pluralism in 6 steps that help reinforce explanation of a phenomenon and creates grounded actionable knowledge; the theories must be logically correct, be free of circular reasoning, based on empirically based theories that are explicitly mentioned and have explanatory power of the observed practical situation or the phenomenon and should exclude alternatives that have less explanatory power (2023). The papers in this special issue show that inter and transdisciplinarity goes beyond the classical theory formations and involves a focus on processes of identification of knowledge, reflection in and on action and attitudinal, affective and emotional aspects in dealing with the complex environments for learning. Systemic approaches, touch points, modelling and artefacts as well as extensive participatory I n r e v i e w activities with peers and externals help to shape the learning of emergent knowledge (compositional methods, processes and constructs).In this special issue we found new insights on different levels of conceptual and epistemic resourcefulness, the development of theory on handling uncertainty, practicing intellectual humility and applying tools (artefacts) for understanding how knowledge emerges, and using system thinking and reflection to discover the boundedness of knowledge. The different nature of the knowledges as well as the different approached to the situated context help to a greater or lesser extent the development of epistemic fluency in increasingly complex learning environments. To cross the divide from theory development in context to actionable knowledge in context, requires more dedicated studies in underlying learning mechanism/ways of thinking or reflections that occur in learners in interdisciplinary learning contexts.Miles MacLeod is an associate professor for philosophy of science at the University of Twente. He specializes in the study of interdisciplinary research, with a particular focus over the past 10 years on the cognitive challenges of interdisciplinary work resulting from variations in standards and practices between different disciplines. He applies insights from his work particularly to the development and improvement of interdisciplinary educational programmes; as well as the improvement of research practices within interdisciplinary projects I n r e v i e w