LEARNING MACHINES

is a project that explores the interconnected relationships between today’s emerging technologies (AI, algorithms, machine learning, computation) and the machinic formations of subjectivity through which pedagogy becomes perceptible, legible, productive, and thus valuable within contemporary educational milieus.

Through a series of research-creation processes and productions, we experiment with crafting pedagogical fabulations that engage with questions of intelligence, artificiality, embodied cognition, techno-human relations, and the complex dynamics that condition teaching and learning. By foregrounding the circuits between theory and practice, Learning Machines aims to reorient and reshape pedagogical approaches to accessibility for today’s computational turn, offering speculative and practical frameworks for reimagining art education in the age of algorithmic assimilation.

What, OR who, are Learning Machines?

Learning machines, as we understand them, encompass not only today’s emerging technologies–AI, algorithms, machine learning protocols, computational tools–but also the machinic formations of subjectivity (Guattari, 1995) through which pedagogical configurations are rendered perceptible, legible, productive, and thus valuable within educational milieus. In this sense, learning machines name more than one thing.

The concept refers to artificial intelligences, sure: software trained on datasets, algorithms that learn to recognize patterns, complete sentences and simulate conversation. But learning machines also describe the very systems of education—schools, curricula, institutions—that are programmed to produce particular, often predictable and measurable, outputs. And, the concept also gestures to “us”: the machinic bodies that are co-constituted in recursive pedagogical circuits and “machinic subjectivities,” one of Guattari’s most radical propositions.

  • jessie is a teacher, artist, researcher, philosopher and conjurer of weird pedagogies for unthought futures. As an Assistant Professor in Art Education at Concordia, beier works with folks to develop research-creation experiments that craft ecological speculations and heretical forms of pedagogy aimed at collective practices of negation, refusal, and fabulation.

  • Sarah is a PhD student in Art Education at Concordia University, where she thinks with/through pedagogies of sharing to explore collective art encounters and questions of accessibility. She is the co-founder and co-director of Musée Ambulant, a nomad museum in Canada that brings art to its audiences, where they are.

  • Azza is a visual artist and art teacher now on hiatus. She has worked across traditional and digital media and is currently cautiously exploring the spaces AI both opens and forecloses. Her fatalistic acceptance of this technology drives her to examine its role in mediating the interplay of art, philosophy, and capital, probing questions of agency, value, and collaboration in a world in flux.

  • Jihane is a visual artist, elementary school art teacher, and PhD student in Art Education at Concordia University, where she explores the integration of AI in studio-based art education contexts. She is a member of the Montreal core team of Women in AI and Robotics, director of Provincial Affiliate Liaison for the Canadian Society of Education through Art (CSEA), and as an artist, she is represented by Sivarulrasa Gallery in Almonte, Ontario.

  • Natalie is an artist, philosopher, and PhD student at Concordia University. Her research in Art Education examines the spectrum of imagination and the role of artificial intelligence in making creative expression more accessible. She is interested in how AI can bridge gaps in understanding and foster connections between diverse ways of thinking.