This work investigates the commonalities and the relationships between analogical and logical importance of analogy, not being treated as a secondary or auxiliary process to logic, but as a generative foundation of cognition, communication, and knowledge creation. By integrating insights from philosophy, cognitive science, logic, science, systems theory, and interdisciplinary practice, we highlight how analogical thinking provides the creative leaps, cross-domain mappings, and heuristic inputs that seed conjectures and heuristics, while logical thinking contributes validation, rigor, and epistemological knowledge. Their recursive interaction produces hybrid modes of reasoning, integral, integrative, and integrated, that are necessary for addressing complex, real-world problems, which level of complexity requires inter-disciplinary fields, multi-disciplinary teams, and transdisciplinary communication, understood as cross-disciplinary and beyond disciplines communication. The latter is especially important in the kind of research that requires the participation of stockholders who may include lay persons.

Based on literature research, notional analysis and diagrams, and systemic reasoning, we emphasize the cybernetic loops that emerge when analogy and logic interact. These loops operate through feedback and feedforward relations, showing how analogy inspires hypotheses that logic tests, while logical structures, once established, generate new analogies, and so on. It further demonstrates that analogical reasoning, expressed through natural language, provides an indispensable medium for transdisciplinary communication, making knowledge transfer across and beyond disciplines possible.

Ultimately, we suggest that analogical and logical thinking, understood as complementary poles of cognition, form the basis of hybrid reasoning capable of tackling complex societal challenges. By reconceiving their relationship in cybernetic and systemic terms, it becomes possible to develop new methodologies that integrate creativity with rigor, emergence with structure, and novelty with validation, oriented for inter- and transdisciplinary communication, research, and practice.

This short experience-based, oriented by literature research, reflection, and reflexivity (essential in Second Order Cybernetics), we try to highlight the importance of analogical thinking, its relationship to logical thinking, and the cybernetic loops that emerge when both are connected through systemic and holistic perspectives. This whole, as it is known, is more than its parts because of its potential emergent properties and synergies. The latter is very probable because all of it is being managed by the respective neural nets, whose high level of complexity increases the potentiality and the probability of cognitive emergent properties that are essential for creativity and for continuously transforming knowledge into understanding, which is itself an emergent property.

Together, these form the basis for an integral, integrative, and integrated Hybrid Thinking. Such an approach may foster systemic-cybernetic and synergistic relationships that are essential for effective inter- and transdisciplinary communication. This, in turn, is required not only for addressing real-world problems, such as those encountered in case studies, consulting, and information systems analysis, synthesis, and deployment, but also for processes that involve: (1) integrating core academic activities, namely research, education, and their respective methodologies; and (2) generating communication across and beyond disciplinary boundaries.

In a few words, our objective is to provide an initial work in progress to explain the real and the potential cybernetical relationships schematized in figure 5.

Drone swarms are groups of unmanned aerial vehicles (UAVs) that operate in a coordinated manner to complete their assigned tasks. The coordination logistics are inspired by biological collectives such as flocks of birds or swarms of insects. Unlike individual drones, swarms leverage numbers and coordination to accomplish complex tasks more efficiently, robustly, and with greater adaptability. Artificial intelligence (AI) plays a pivotal role in elevating drone swarms from mere programmed machines to intelligent agents capable of self-organization, decision-making, and rapid response to dynamic environments. AI enables swarms to operate with minimal human intervention and adapt to changing scenarios in real-time. This paper focuses on the applications of artificial intelligence in making the operations of drone swarms as efficient and as smooth as possible.

Nowadays scientific challenges require us to work together not only across disciplines, but also in large teams often scattered around the globe. To understand the intrusive inefficiencies present when we deal scientifically with the dazzling natural and social challenges facing us, I have previously proposed to consider the scientific milieu itself as a primary subject of study. Since any collective action related to knowledge involves communication, brains, and minds, a central point in this plan is to understand the role our brains play in this scenario.

This paper describes the beginnings of a many-step modelling process that aims to build a brain-aware scientific culture, starting from a model of the scientific milieu itself displaying associations of the brain-mind complex with its many components. The description of the scientific milieu embraces all its aggregation and individuation levels, while highlighting channels, procedures, conditionings, and phenomenological characteristics through which our brains may interfere in the process of knowledge production, preservation, and transmission.

It extends previous efforts by sketching models that separate anatomical from physiological changes in brains; by highlighting the interplay of both these facets; by discussing next steps necessary to advance towards a more encompassing model; and by clarifying how agreements about elementary cross-disciplinary communication tokens seems to be initially achieved.

Artificial intelligence (AI) creators and developers attempt to simulate human thinking and our environment (e.g., popular web-based Second Life), but, controversially, claim to seek replicating the human brain (e.g., Human Brain Project) and what it does. However, some central questions are, "Who are we? Are we truly who we believe we are? What is thinking/mentation/ideas? Why is our world rife with contradictions and conflict? What is reality, itself?" Experts, like David Chalmers, refer to "consciousness" as the "hard problem". If we don’t fully understand these concepts in humans, how can we possibly recreate them in AI? It’s a puzzle, much like the challenge of defining and creating life itself. Answers are to be founded on what exists, not merely our desires. For replicating humans, AI developers must confront the difference between belief and authentic self. Beliefs can mask the true self. A major flaw of the well-known Turing Test—which assesses whether a machine can imitate human intelligence—is that it cannot verify whether someone’s beliefs are reflected in their actions. AI developers must be competent technicians but integrate philosophy, thus addressing overlapping questions of meaning, ethics, purpose, and ethos. Even AI creators acknowledge AI could threaten humanity. If future technology integrates self-awareness or subjective experience into advanced computing systems, we will need to revisit some ancient wisdom, "Know thyself." Any viable human identity probe (such as Authentic Systems) must be underpinned by philosophy, thus revealing the extent to which one has internalized belief by action. One the material and psychological aspects of authentic identity are know, we apply the unity of opposites law to establish authentic human identity.

Many researchers often have difficulties finding enough data to test their hypotheses [1][2]. This study explores three different ways to create “synthetic”2 (i.e., artificial) data that mimics real-world data in statistical traits like correlations (i.e., relationships between the variables). To see how well these methods perform, the study compares the patterns of synthetic data to their real-world counterparts and sees how closely the data maintain the correlations. Additionally, the study uses seven machine learning3 prediction methods to see how these synthetic data perform. The findings indicate that two methods more effectively preserve the original correlation structure, while the third method yields better predictive performance.

In an era of rapid technological advancement, precipitating significant global changes and novel socio-economic patterns, a re-evaluation of contemporary leadership paradigms is necessitated to guide societies successfully through these socio-economic transformations. Given that leadership is an inherently general and abstract phenomenon, this paper proposes a practical examination through the lens of the transformational maturity of individual leaders, alongside the digital and transformational maturity of organizations aspiring to spearhead market changes. In the latter context, both digital and transformational maturity are subsumed under the broader concept of organizational maturity, which serves as a tangible metric of leadership at the organizational level. Accordingly, this paper will present and analyze selected contemporary approaches to leadership, the global context of change relative to pervasive digital transformation, and empirical research findings pertaining to European leaders and companies. The overarching framework for this analysis is Industry 4.0, characterized by process automation and integration within enterprises, and the emerging Industry 5.0, which incorporates a human-centric dimension into this concept [1].

Does AI represent authentic intelligence, or an artificial identity? What if intelligence were replaced with identity—how would the implications change? At stake is the meaning of artificial itself, but most importantly, the focus here is the question: What constitutes authentic identity? If the objective of AI is, at least in part, to replicate human beings, then we must first ask: Who are we? Must this not be established before replication is possible? Even if we believe we know, how do we verify such knowledge? The Authentic Systems identity probe answers this question and further exemplifies a method that might challenge AI–s ability to replicate us. Our Universe, our context, may not be as it seems. It may range from cold and indifferent to nurturing, from infernal to heavenly—or it may not be material at all. The universe could be fundamentally abstract, perhaps even mathematical in nature. This realization destabilizes many assumptions about reality itself. Such questions return us to philosophy, recalling those “naïve” yet enduring inquiries posed by beginning students: Who are we? Why are we here? These questions, once smiled at, now bear urgent weight. Geoffrey Hinton, often described as the “godfather of AI,” has warned that the very systems he helped pioneer may become instruments of humanity–s self-extinction. What was once a classroom provocation has become a pressing global concern. This presentation will explore the notion of authentic identity in this context, framed also by recent discourse such as the viral video “Michio Kaku”s Terrifying Warning: Quantum AI Just Made a Godlike Discovery.” While the video falsely attributes such a warning to Kaku, it nevertheless articulates disturbing possibilities that resonate with ongoing research into consciousness, identity, and the future of humanity. We may ultimately be very different from who we think we are.

Methodological note – My role here is as a philosopher and working in its subset, logic. Having a full technical knowledge would be ideal, but I do not claim to have such. However, what a logician can do is arrange various peer-reviewed assertions within a philosophical framework (here,authentic human identity) and draw the appropriate conclusion as to what I think our destiny very well may be. My 2023 book, Managing Complexity Through Social Intelligence provides the larger context for what follows.

In the study we explore the evolving landscape of digital multimodality and its implications for transdisciplinary communication in education. It is examined how digital literacy integrates multidisciplinary and multimodal approaches, fostering embodied transdisciplinarity that transcends traditional boundaries in the age of AI.

As a product of modern civilization, the digital reality has become an independent format of being. Accordingly, electronic media act not only as a means of transmitting information, but also reveal their own world-creating, meaning-making and, as a consequence, communicative potential. The global digital realm and AI models stand as an integral environment, demanding new cognition and perception ways via complex philosophic, cultural, social, linguistic approaches, providing unlimited opportunities for human intellect, communicative development and research.

Transdisciplinary communication in digital education represents a transformative trend for humanity, reshaping the way disciplines interact and collaborate. The core concept of transdisciplinarity hinges on dialogue—bridging disciplinary divides to create new frameworks for knowledge transfer. This evolution moves beyond digital humanism and digital humanities, progressing toward post-humanity and postdisciplinarity, where rigid disciplinary boundaries dissolve in favor of interconnected knowledge systems.

The context of the erupted military intervention in Ukraine and the ensuing information warfare in various digital ambients (social media, news coverage, digital communications), the specific value is allocated to the enhanced role of digital humanism as a tool of the internationally broadcast strife for freedom and sovereignty.

This paper proposes Engaged Immersive Learning (EIL), a new framework designed to address the passivity, lack of context, and one-way communication that often characterize lecture centered higher education. The argument begins by examining the contributions and limitations of Problem/Project Based Learning (PBL) and STEM/STEAM education. While these approaches have enhanced self directed learning and creativity through problem based inquiry and interdisciplinary collaboration, they retain structural challenges: tasks are frequently designed within the classroom, activities tend to remain short lived, assessment relies on faculty defined institutional standards, and multi stakeholder collaboration often remains superficial. Drawing on Kolb’s experiential learning, Mezirow’s transformative learning, Akpan’s social constructivism, and Lave and Wenger’s concepts of Legitimate Peripheral Participation (LPP) and Communities of Practice (CoP), the study proposes design principles for EIL. It further introduces Generative AI (GenAI) as a catalyst that supports, rather than replaces, human collaboration. EIL consists of four elements: (1) an environment driven learning space in which students are immersed for extended periods in real world contexts where multiple stakeholders (e.g., corporations, government, citizens) interact; (2) a participation trajectory in which students’ roles and responsibilities expand gradually in line with LPP; (3) a dialogue design that positions GenAI as a “buddy” that sometimes errs but offers heterogeneous perspectives; and (4) practice based assessment structured around external outcomes, stakeholder perspectives, and transformative change. Case studies from a single university illustrate that EIL can generate outcomes beyond the classroom—such as international conference presentations and forms of societal implementation—enabling employers and public officials to evaluate students in terms of “would I want to work with this person?”, and treat identity shifts as explicit learning outcomes. The paper also identifies remaining challenges concerning sample size and duration, the reliability and validity of assessment rubrics, equity of access, ethics and governance in GenAI integration, and the tension between scalability and faculty workload. EIL is therefore positioned not as a finished model but as a set of design principles to be adapted to the specific contexts of different universities and regions.

Over the past two decades, global educational systems have increasingly prioritized Science, Technology, Engineering, and Mathematics (STEM) disciplines, driven by economic competitiveness, labor market demands, and technological innovation. Governments have implemented targeted policies and funding strategies to boost STEM education, resulting in significant shifts in curricula, institutional focus, and student enrollment. While this pivot has yielded measurable economic benefits, it has concurrently marginalized the humanities—disciplines essential for fostering critical thinking, ethical reasoning, cultural awareness, and civic engagement. This paper argues that the current imbalance between STEM and the humanities represents a fundamental misalignment in educational priorities, with profound consequences for democratic governance, social cohesion, and human-centered innovation. Drawing on comparative data from the United States, United Kingdom, Germany, China, India, and Australia, the paper highlights the funding disparities, enrollment trends, and policy frameworks that contribute to this trend. It further explores the intrinsic and instrumental value of the humanities, particularly in addressing the ethical challenges posed by artificial intelligence, climate change, and social inequality. Through interdisciplinary analysis and policy review, this study proposes integrative solutions to bridge the STEM-humanities divide, emphasizing the need for balanced, inclusive, and ethically informed education systems that are better equipped to confront the complexities of the 21st century.

This article extends a keynote from the 16th IMCIC 2025, offering a framework for academic institutions navigating the epistemic shifts prompted by artificial general intelligence (AGI). It argues that traditional disciplinary (Mode 1), interdisciplinary (Mode 2), and transdisciplinary (Mode 3) approaches are increasingly insufficient to address the complexity and plurality of AGI-era knowledge production.

Drawing on critical communication theories—such as the Kuhn–MacIntyre thesis on incommensurability, the Bataille–Lyotard notion of invention, Nicolescu–Ostrom’s governance frameworks, and Haraway–Scholz’s situated knowledge—the article introduces Mode 4: cyber-transdisciplinarity.1 This mode conceptualizes AGI as an epistemic co-agent, enabling real-time mediation, synthesis, and redistribution of knowledge between human and machinic actors.

Methodologically, it proposes a plural foundation that includes constructivist grounded theory, antenarrative inquiry, and intercultural competence. A case study featuring a cybernetic dashboard illustrates how institutions can foster reflexive governance through adaptive infrastructure.

The conclusion anticipates a speculative Mode 5: pan-disciplinary intelligence, characterized by AGI-facilitated planetary-scale knowledge integration. The article contends that ethical stewardship and cyber-transdisciplinary competence are essential for institutions to adapt meaningfully to AGI’s transformative potential in knowledge governance and transdisciplinary communication.

Generative AI can generate new contents in many types of outputs: images, videos, text or music. ChatGPT is web application that uses Generative AI. Many industries such as financial, health care, manufacturing, and marketing and sales use Generative AI.

This article discusses:

(1.) Literature review on the development for Large Language Models (LLM), Deep Learning, and Generative AI and recent research in these areas with a focus on Artificial Intelligence.

(2.) What are multidisciplinary, interdisciplinary and transdisciplinary applications of Artificial Intelligence and other applications for Generative AI such as those for vision, speech, language, decision, Open AI Service.

(3.) The concept of Trans-AI/DS (Data Science) as the transformative, transdisciplinary and translational artificial intelligence and data science.

A bibliography of current research in Generative AI is also presented and its relationship to Artificial Intelligence (AI), Machine Learning, and Deep Learning. Illustrative figures are also provided to visually enhance the differences and applications of each of these.

Traditional models of interdisciplinary collaboration in higher education often struggled to transcend disciplinary silos, resulting in fragmented Communication and superficial integration. The Components of Role-playing in Experiential Learning (CREL) Framework offered a transformative alternative by embedding role-playing mechanics into structured learning environments, enabling participants to embody perspectives beyond their disciplinary boundaries.

First, this paper introduced the five core components of the CREL Framework—Scenario, Social Contract, Collaboration, Choice, and Experience—and explained how they function as an integrated system for designing and assessing collaborative learning environments. Then, it argued that the framework catalyzed effective interdisciplinary collaboration and authentic experiential learning by simulating complex, real-world scenarios that demanded mutual understanding, adaptive thinking, and co-constructed solutions.

Next, drawing from theoretical foundations in ludology, experiential education, and transdisciplinary Communication, the authors demonstrated how CREL facilitated role adoption, dialogic engagement, and reflective inquiry. Finally, by positioning play as a rigorous epistemic practice rather than a pedagogical supplement, the article advanced a more inclusive, participatory, and dynamic vision of interdisciplinary education. It concluded by outlining the theoretical foundations of the framework, addressing key limitations, anticipating common critiques, and calling for curricular, institutional, and policy reforms to legitimize role-play as a mode of scholarly engagement and professional development.

This essay investigates the Messiah Savior Complex in Big Tech, where artificial intelligence is presented as a redemptive force capable of solving humanity’s most urgent challenges. Using the historical analogy of the narwhal tusk trade, in which tusks were sold as unicorn horns to European elites, the analysis illustrates how myth-based narratives continue to influence technological realities. In contemporary discourse, these narratives take the form of hyperstitions, which are beliefs that become real through repetition, institutional reinforcement, and collective investment. Such dynamics obscure empirical scrutiny and displace critical engagement with the socio-technical realities of AI development. The essay argues that magical thinking and industry promotion often sustain these belief structures to deflect regulatory oversight and maintain public enthusiasm. Rather than rejecting technological progress, the paper calls for a transdisciplinary framework that treats AI as embedded in systems requiring accountability, transparency, and contextual awareness.

This paper explores how emotion, gameplay context, and social norms shape role choice in both digital and institutional systems. Using Overwatch as a case study, it examines gendered experiences of physiological excitement and decision-making during play. Women often describe heightened emotional engagement where victory represents both personal achievement and resistance to cultural expectations. Such emotions influence situational choices, including whether to play as a Tank, DPS, or Support, and which hero to select based on perceived risk or visibility.

Drawing on feminist game studies and transdisciplinary communication, the authors argue that role preference is determined not only by game mechanics but also by cultural scripts. Comparisons with Apex Legends show how design encodes gender differently across franchises. In Overwatch, the historical coding of Tanks as masculine and Healers as feminine reinforces stereotypes even as players subvert them.

Building on Donna Haraway’s concept of the cyborg, the woman-Tank represents a hybrid identity that is both empowered and constrained by visibility and performance. Poor performance invites criticism, while success often goes unacknowledged. By situating Overwatch within broader systems of inequity, this study shows how emotion, design, and norms interact to reproduce or challenge bias, positioning games as laboratories for systemic equity and cultural transformation.

Artificial Intelligence (AI) is being used increasingly worldwide to provide better legal education to the public by providing them with easily accessible and automated legal information. However, its integration into justice systems raises significant legal, ethical, and policy concerns. In Albania, where legal literacy remains low despite ongoing judicial reforms, AI-driven tools present both an opportunity and a challenge. This study explores the legal and regulatory implications of incorporating AI into public legal education, analyzing potential risks related to misinformation, algorithmic bias, data privacy, and human rights compliance.

Using a doctrinal legal research approach, the study examines Albania’s existing legal framework, including constitutional provisions on access to justice, data protection laws, and the justice reform strategy and subsequent legislation. This is followed by a comparative legal analysis of AI-driven legal education initiatives in Estonia, the United Kingdom, Canada, and Singapore. This analysis provides insights into regulatory best practices. Furthermore, the study evaluates AI’s alignment with international human rights norms, particularly the right to legal information under UN Sustainable Development Goal 16.

The findings of this study reveal the existing gaps in Albania’s legal system regarding AI-driven legal education and emphasize the need for including strong legal protections. The study proposes policy recommendations aiming at the usage of AI tools to enhance public legal literacy while maintaining legal accuracy, transparency, and accountability. These recommendations include AI oversight mechanisms, legal accuracy standards, and ethical AI guidelines that are tailored to Albania’s socio-political context. The responsible integration of AI capabilities into legal education can help Albania improve public trust in its justice system and strengthen democratic participation.

The civic mission of universities has gained renewed prominence as higher education institutions confront the imperative of contributing to society. Beyond their traditional functions of teaching and research, universities are increasingly recognized as anchor institutions that generate public value by fostering engaged citizenship and addressing complex societal challenges. This article examines the civic mission of universities through two interrelated pathways: service-learning and transdisciplinary communication. Service-learning, defined as the integration of community engagement with academic instruction and critical reflection, strengthens students’ disciplinary learning while cultivating civicmindedness. In parallel, transdisciplinary communication provides the epistemic and practical framework to address wicked problems in society by integrating diverse academic perspectives with community knowledge in the co-production of solutions. Taken together, these approaches underscore how universities can fulfill their civic mission in ways that are educationally transformative and socially responsive.

Artificial Intelligence (AI) presents higher education with both transformative potential and profound risk. While AI-tools can personalize learning and enhance collaboration, they also threaten to erode critical thinking, intellectual independence, and the reflective depth of human inquiry. This conceptual analysis applies transdisciplinary communication (TDC) and educational psychology frameworks to examine how AI integration influences learning, teaching, and institutional practices. Findings highlight that AI-tools can strengthen collaboration, automate administrative tasks, and promote inclusivity across languages and disciplines. However, overreliance on automation risks reducing empathy, ethical reasoning, and creativity. The study identifies interpretation, judgment, and contextual understanding as human dimensions that remain non-delegable in AI-supported education. Higher education must move from viewing AI as a replacement for cognition to treating it as a catalyst for transformative learning. Embedding human-in-the-loop practices and metacognitive reflection ensures that AI amplifies inquiry rather than undermines it. The enduring task is to balance innovation with discernment, preserving education's human purpose while guiding AI's integration ethically and intelligently.

The paper explores after-course preparation for certification exams. The question addressed is, “What are the broad themes for seeking tutorial help after course completion? What types of problems are typical during tutorial help? What are their characteristics?” The paper is based on the author’s experience in tutorial assistance for Society of Actuary certification exams at Towson University and additionally to help-desk experiences with ACTEX an online educational company that assists in exam preparation. This is part I of what hopefully will become a series of such explorations. In this part I, we explore the Probability course. Four broad areas of seeking help emerge including (i) problems integrating multiple course modules, (ii) many-parameter problems, (iii) modules with multiple vs. single formulas, and (iv) modules where solution is accomplished by an algorithm vs. a formula. The study, besides being of use to those conducting tutorials, is useful to instructors teaching the course since it emphasizes potentially weak spots in the curriculum needing strengthening. This study is consistent with and reflects several important constructs from the psychology of pedagogy.

This study will turn around the difficulties non-English speaking researchers encounter when they try to communicate their local research in the globalized context of international conferences (such as IIIS), where English is used as lingua franca. The presenter will embroider her presentation around a technical term in English - such as grid - and in Modern Greek - such as θέμα (: topic of an essay; theme, as in 'thematic units'; and issue, as in "there is an issue here"), compare them with their equivalent terms in Spanish, French and German and try to systematize the research steps (or advanced translation steps and tools) required for a non-English speaking scientist to find the right term in order to make himself/herself understood by a wider international public who communicates in English. During the discussion, the author of this study will try to show how far the AI can help HI (Human Intelligences or nous) in this kind of research, depending on whether researchers are: (1) aware of the polysemy of scientific discourse (i.e. the non-existence of 1:1 equivalence of terms between at least two different linguistic systems), and (2) trained in using comparative and associative thinking. As the writer of this study will claim, it is finally HI who decides which term(s) s/he is going to use, exercising his/her own judgement after s/he has taken into consideration the scientific and social context of the term.

In the study we explore the evolving landscape of digital multimodality and its implications for transdisciplinary communication in education. It is examined how digital literacy integrates multidisciplinary and multimodal approaches, fostering embodied transdisciplinarity that transcends traditional boundaries in the age of AI.

As a product of modern civilization, the digital reality has become an independent format of being. Accordingly, electronic media act not only as a means of transmitting information, but also reveal their own world-creating, meaning-making and, as a consequence, communicative potential. The global digital realm and AI models stand as an integral environment, demanding new cognition and perception ways via complex philosophic, cultural, social, linguistic approaches, providing unlimited opportunities for human intellect, communicative development and research.

Transdisciplinary communication in digital education represents a transformative trend for humanity, reshaping the way disciplines interact and collaborate. The core concept of transdisciplinarity hinges on dialogue—bridging disciplinary divides to create new frameworks for knowledge transfer. This evolution moves beyond digital humanism and digital humanities, progressing toward post-humanity and post-disciplinarity, where rigid disciplinary boundaries dissolve in favor of interconnected knowledge systems.

The context of the erupted military intervention in Ukraine and the ensuing information warfare in various digital ambients (social media, news coverage, digital communications), the specific value is allocated to the enhanced role of digital humanism as a tool of the internationally broadcast strife for freedom and sovereignty.

If trans-disciplinarity goes beyond the boundaries of various scientific fields (i.e. of interdisciplinarity) by various stakeholders, such as practitioners, citizens, policymakers, and businesses, in the knowledge production process, why does it seem so difficult in practice?

Why do very good theoretical models of trans-disciplinarity fail both in creating integrative solutions that are sound and relevant to real-world problems and in fostering societal innovation and learning?

This presentation will try to answer this sequence of questions by examining which inherent problems in academia and various societies operate as stumbling blocks:

On the one hand, in academia, there is the bias that expressing difficult issues in an understandable language is something that is neither scientific nor profitable; it is rather time-consuming and very insecure in social practice. There is also the academic attitude that “what is said is the ‘absolute’ truth of the matter” [a snobbish attitude/hubris]; a variety of examples will be provided.

On the other hand, the wider society is very hesitant when encountering a “pure” academic discourse (which is not understandable) and sense that they are looked down upon by the academics.

Finally, the presenter will discuss the intangible aspects of trans-disciplinarity, and how these aspects, when practiced, can remove stumbling blocks of miscommunication, a fact that takes time and effort from both sides.