Samenvatting

This presentation explores AI literacy as a critical and situated dimension of Artistic Research (AR), drawing from recent investigation conducted with Master’s students in a discursive artistic AI course at Frank Mohr Institute. Using a structured questionnaire as both a pedagogical and research tool, the study uncovers how emerging artists conceptualise, use, and imagine artificial intelligence—revealing tensions between speculative expectations and grounded practices. The paper is expanded through a reflection in dialogue with selected students, and an artistic analysis of works they developed and the author curated for the 2024 symposium Encounters in Artistic Research. This triangulated methodology allows for a nuanced view of how literacy, practice, and critique co-evolve across time and context. The findings reveal striking conceptual ambiguities: definitions of AI among participants range from technical (algorithmic/machine learning) to metaphorical, critical, and even anthropomorphic. While AI is present in students’ daily lives, it remains marginal within their artistic practices. Nevertheless, their imagined futures for AI—emotional support, creative co-authorship, abstract thinking, political disruption—exceed their current capabilities and signal a strong desire for meaningful integration. By positioning AI literacy not merely as tool-based training but as a form of epistemological inquiry, this paper proposes a reframing of Human-Centred AI (HCAI) in artistic practice. The artist is not only a user or co-creator but an interpreter of algorithmic systems, capable of transforming technological opacity into critical reflection. This resonates with the evolving landscape of AR, where methodologies bridge creative speculation with pedagogical, social, and technological critique. The paper will conclude by proposing initial parameters for a literacy-driven, participatory methodology in artistic AI research. It will reflect on how such frameworks can contribute to institutional knowledge while resisting premature normalization of AI tools. Inviting a broader discussion on authorship, algorithmic imaginaries, and the role of artistic research in shaping inclusive and reflexive AI futures.