The proposed paper delves into the realm of uncertainty in two ways. First, uncertainty, by the means of education (Biesta 2014) and human action (Arendt 1958), refers to the lack of complete knowledge or predictability when making decisions. It encompasses various forms of ambiguity, doubt, and unpredictability that can significantly impact human thinking and action (Mazzone & Elgammal 2019). Second, by focusing on complex machine interactions, our research seeks to illuminate the ways in which humans engage with AI systems in order to approach the complexity of the underlying digital infrastructure (Williamson et al. 2023) and its implications for images and imaginaries of complex machine systems.

In our research, we are exploring contexts and practices that inhabit moments of uncertainty when humans interact with complex machine systems in various playful and creative ways. We do not want to completely eliminate the potential of human uncertainty by focussing on machine systems, we rather try to acknowledge the notion of everlasting indeterminacy in human machine interaction. Therefore, we take uncertainty as starting point to reconstruct images of AI on the one hand and ultimately shape the conditions and constraints of educational processes on the other hand.

Therefore, we want to outline the extent to which people need to be prepared for explanations and know about interaction, in order to benefit from them in a mode of explanation. By addressing the triangle of uncertainty, creativity and exploration, we also hope to get insights to which extent creative methods and visual cues can be leveraged to teach AI concepts and foster digital literacy among learners. The research can be divided into three parts (exploration, creativity and uncertainty) that equally contribute to the overall question: how to address uncertainty in order to enhance human modes of understanding, images and imaginaries of complex machine systems?

Our qualitative research, based on 16 interviews and ethnographic observations over the past two years, examines how people engage with machines for development, artistic expression, exploration, and educational purposes (Ahlborn et al. 2022). The data was collected through an individual research project on art and AI (Ahlborn 2023) as well as part of research within the Transregional Collaborative Research Center TRR 318 "Constructing Explainability" on robotic interaction. We use ethnographic research and narrative interviews following (Christin 2020) to explore and reconstruct images, imaginaries of AI and moments of uncertainty in dynamic interactions with complex machine systems. Our goal is to enhance the understanding of the subtleties and complexities of this dynamic space in-between human machine interactions and modes of uncertainty. We recognize the persistent nature of uncertainty in these interactions, viewing it as a basis to explore AI-related images and imaginaries.

The findings will not only inform teaching materials, such as data stories and metaphors for civic data infrastructures and higher education, aiming to reconstruct AI images as well as imaginaries and foster diverse understandings in educational settings, the key results are also part of further basic research on uncertainty in educational settings challenged by machine systems. Our reconstructive approach offers a unique perspective, laying the foundation for future interdisciplinary research on explainability of AI and the complexity of educational processes.

Ahlborn, J. (2023). „Damn Data! On the (Explorative) Role of AI Art“. Long paper presentation as part of the symposium „Normalizing the Body. Addressing the Lack of Diversity in Digital Technologies and What it Means for Educational Science“. #ECER 2023, Glasgow, Scotland. Ahlborn, J., Verständig, D., & Stricker, J. (2022). Decoding Datafication: Media educational approaches in communicating the complexity of digital data and data infrastructures. #ECREA 2022, Aarhus, Denmark https://pub.uni-bielefeld.de/record/2967844 Arendt, H. (1958). The Human Condition. The University of Chicago Press. Biesta, G. J. J. (2015). Beautiful Risk of Education. Routledge. https://doi.org/10.4324/9781315635866 Christin, A. (2020). The ethnographer and the algorithm: Beyond the black box. Theory and Society, 49(5–6), 897–918. https://doi.org/10.1007/s11186-020-09411-3 Mazzone, M., & Elgammal, A. (2019). Art, Creativity, and the Potential of Artificial Intelligence. Arts, 8(1), 26. https://doi.org/10.3390/arts8010026 Verständig, D. (2020). Nothing to see? – How to address algorithms and their impact on the perception of the world. In D. Kergel, B. Heidkamp, R. C. Arnett, & S. Mancino (Eds.), Communication and Learning in an Age of Digital Transformation (pp. 220–237). Routledge. Williamson, B., Macgilchrist, F., & Potter, J. (2023). Re-examining AI, automation and datafication in education. Learning, Media and Technology, 48(1), 1–5. https://doi.org/10.1080/17439884.2023.2167830