Learning theories broadly characterised as constructivist, agree on the importance to learning of the environment, but differ on what exactly it is that constitutes this importance. Accordingly, they also differ on the educational consequences to be drawn from the theoretical perspective. Cognitive constructivism focuses on the active role of the learner, and on real-life learning. Social-learning theories, comprising the socio-historical, socio-cultural theories as well as the situated-learning and community-of-practice approaches, emphasise learning as being a process within and a product of the social context. Critical-learning theory stresses that this social context is a man-made construction, which should be approached critically and transformed in order to create a better world. We propose to view these different approaches as contributions to our understanding of the learning-environment relationship, and their educational impact as questions to be addressed to educational contexts.
This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
Physical rehabilitation programs revolve around the repetitive execution of exercises since it has been proven to lead to better rehabilitation results. Although beginning the motor (re)learning process early is paramount to obtain good recovery outcomes, patients do not normally see/experience any short-term improvement, which has a toll on their motivation. Therefore, patients find it difficult to stay engaged in seemingly mundane exercises, not only in terms of adhering to the rehabilitation program, but also in terms of proper execution of the movements. One way in which this motivation problem has been tackled is to employ games in the rehabilitation process. These games are designed to reward patients for performing the exercises correctly or regularly. The rewards can take many forms, for instance providing an experience that is engaging (fun), one that is aesthetically pleasing (appealing visual and aural feedback), or one that employs gamification elements such as points, badges, or achievements. However, even though some of these serious game systems are designed together with physiotherapists and with the patients’ needs in mind, many of them end up not being used consistently during physical rehabilitation past the first few sessions (i.e. novelty effect). Thus, in this project, we aim to 1) Identify, by means of literature reviews, focus groups, and interviews with the involved stakeholders, why this is happening, 2) Develop a set of guidelines for the successful deployment of serious games for rehabilitation, and 3) Develop an initial implementation process and ideas for potential serious games. In a follow-up application, we intend to build on this knowledge and apply it in the design of a (set of) serious game for rehabilitation to be deployed at one of the partners centers and conduct a longitudinal evaluation to measure the success of the application of the deployment guidelines.
The Hereon team has expressed interest in the use of the PO platform for the virtualization of the (hydro)dynamic behavior of offshore wind farms, in particular regarding turbidity around wind turbines. BUas has developed the Procedural Ocean (PO) platform. The platform uses procedural content generation (AI) for data-driven 3D virtualization of complex marine and maritime environments, with elements such as geo-environment (bathymery, etc.), geo-physics (weather conditions, waves), wind farms, aquaculture, shipping, ecology, and more. The virtual and immersive environment in the game engine Unreal supports advanced (game-like) user interaction for policy-oriented learning (marine spatial planning), ocean management, and decision making. We therefore propose a joint pilot Research and Development (R&D) project to explore, demonstrate and validate how a gridded dataset provided by Hereon can show the dynmics around wind farm monopiles. Furthermore, we can explore interactivity with the engineering and design of the turbine and the multiplication of the turbine design to compose a wind farm. Client: Hereon (The Helmholtz-Zentrum Hereon is a non-profit making research institute )
