This article describes musicians' lifelong and lifewide learning as it was investigated through biographical research. Key developments in the professional lives of 32 musicians were examined, focusing on critical incidents and educational interventions in their life, educational and career span. The main thread was the question of how these musicians learn. After analysis, three conceptual entities were established in the biographies, the first being musicians' artistic, generic and educational leadership; second, the interconnection between their varied learning styles; and third, their need for an adaptive and responsive learning environment within a reflexive and reflective institutional culture. Two biographical examples of musicians suffering from performance anxiety are described, focusing on their leadership, learning styles and subsequent transformative and transitional learning when developing coping strategies. The article concludes with directions for teaching and learning that can be extrapolated from the findings of biographical research.
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?
