Modelling collective learning in design

In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated

Scientific discovery reloaded

The way scientific discovery has been conceptualized has changed drastically in the last few decades: its relation to logic, inference, methods, and evolution has been deeply reloaded. The ‘philosophical matrix’ moulded by logical empiricism and analytical tradition has been challenged by the ‘friends of discovery’, who opened up the way to a rational investigation of discovery. This has produced not only new theories of discovery (like the deductive, cognitive, and evolutionary), but also new ways of practicing it in a rational and more systematic way. Ampliative rules, methods, heuristic procedures and even a logic of discovery have been investigated, extracted, reconstructed and refined. The outcome is a ‘scientific discovery revolution’: not only a new way of looking at discovery, but also a construction of tools that can guide us to discover something new. This is a very important contribution of philosophy of science to science, as it puts the former in a position not only to interpret what scientists do, but also to provide and improve tools that they can employ in their activity

Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

AI Plus Other Technologies? The Impact of ChatGPT and Creativity Support Systems on Individual Creativity

The emergence of generative artificial intelligence (AI) has triggered a massive technological surge. Software and systems increasingly incorporate generative AI as a fundamental component of their applications. Unfortunately, there is a lack of awareness of the interaction between generative AI and other tools and their consequences and causes. In this research, we explored the impact of the concurrent use of generative AI and creativity support systems (CSS) on users’ creativity. In addition, by categorizing the stimuli provided by the CSS into high and low relatedness, we further investigated the effects of using generative AI with various CSS. By focusing on the interaction effect between generative AI and CSS, this research not only sheds light on the broader implications of generative AI but also serves as a guiding framework for the evolution of future CSS and furthering the enhancement of individual creativity

The Oneiric Reality of Electronic Scents

This paper investigates the ‘oneiric’ dimension of scent, by suggesting a new design process that can be worn as a fashion accessory or integrated in textile technologies, to subtly alter reality and go beyond our senses. It fuses wearable ‘electronic scent’ delivery systems with pioneering biotechnologies as a ground-breaking ‘science fashion’ enabler. The purpose is to enhance wellbeing by reaching a day‐dream state of being through the sense of smell. The sense of smell (or olfaction) is a chemical sense and part of the limbic system which regulates emotion and memory within the brain. The power of scent makes content extremely compelling by offering a heightened sense of reality which is intensified by emotions such as joy, anger and fear. Scent helps us appreciate all the senses as we embark on a sensory journey unlike any other; it enhances mood, keeps us in the moment, diverts us from distractions, reduces boredom and encourages creativity. This paper highlights the importance of smell, the forgotten sense, and also identifies how we as humans have grown to underuse our senses. It endeavours to show how the reinvention of our sensory faculties is possible through advances in biotechnology. It introduces the new ‘data senses’ as a wearable sensory platform that triggers and fine tunes the senses with fragrances. It puts forward a new design process that is currently being developed in clothing elements, jewellery and textile technologies, offering a new method to deliver scent electronically and intelligently in fashion and everyday consumer products. It creates a personal ‘scent wave’, around the wearer, to allow the mind to wander, to give a deeper sense of life or ‘lived reality’ (verses fantasy), a new found satisfaction and confidence, and to reach new heights of creativity. By combining biology with wearable technologies, we propose a biotechnological solution that can be translated into sensory fashion elements. This is a new trend in 21st century ‘data sensing’, based on holographic biosensors that sense the human condition, aromachology (the science of the effect of fragrance and behaviour), colour-therapy, and smart polymer science. The use of biosensors in the world of fashion and textiles, enables us to act on visual cues or detect scent signals and rising stress levels, allowing immediate information to hand. An ‘oneiric’ mood is triggered by a spectrum of scents which is encased in a micro-computerised ‘scent‐cell’ and integrated into clothing elements or jewellery. When we inhale an unexpected scent, it takes us by surprise; the power of fragrance fills us with pleasurable ripples of multi‐sensations and dream‐like qualities. The aromas create a near trance‐like experience that induces a daydream state of (immediate) satisfaction, or a ‘revived reality’ in our personal scent bubble of reality. The products and jewellery items were copyrighted and designed by Slim Barrett and the technology input was from EG Technology and Epigem

CREATIVITY IN ENTREPRENEURSHIP EDUCATION

This paper uses social cognitive theory to investigate entrepreneurial intent among participants in graduate entrepreneurship programs. To the best of our knowledge, the paper is the first to investigate the importance of creativity in entrepreneurship education and theoretical models of entrepreneurial intentions. Specifically, we test whether students creative potential is related to their intention to engage in entrepreneurship. Theoretically derived hypotheses are tested using multiple and ordinal regression analyses. We find that high scores on a creativity test and prior entrepreneurial experiences were positively associated with entrepreneurial intentions, whereas perception of risks had a negative influence. Our theoretical predictors of entreprenurial intention received strong support, indicating that creativity should be considered in models of entrepreneurial intentions. Yet, the use of intentions as dependent variable has its know weaknesses in that we might not distinguish between 'dreamers' and 'doers'. The findings indicate that exercises in creativity can be used to raise entrepreneurial intentions of students in entrepreneurship education. Heterogeneity in creative styles among students also points to the problems of a ‘one-size-fits-all’ approach to entrepreneurship education.Entrepreneurship education; intentions; creativity

What You Know and What You Don\u27t Know: A Discussion of Knowledge Intensity and Support Architectures in Improving Crowdsourcing Creativity

Building on the componential theory of creativity, we studied how the crowdsourcing creativity support architectures and the task knowledge intensity levels affect the crowd’s creativity. Using an online experiment, we found that remixing can trigger the crowd to be more creative than external stimuli and using either architecture triggers the crowd to be more creative overall. Also, the crowd is more creative in solving low-knowledge-intensity tasks than in solving high-knowledge-intensity tasks. Interestingly, regardless of the knowledge intensity levels of tasks, crowdsourcing support architectures have a significant impact on the crowd’s creativity. Therefore, our paper contributes to the crowdsourcing literature on promoting crowd creativity and provides practical implications on solving societal challenges, especially large-scale problems