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Guiding Research questions: What new images of reason are produced with machine learning? To what extent are new epistemic paradigms influencing new ways of theorizing decision, judgment, understanding and human learning? What images and theories of learning inform current AI efforts? How do these approaches relate to other theories of learning? How do present concepts of algorithm differ from past concepts? Is there a general ecological paradigm that captures the current technicity of the digital planet? Can art offer ways to hack ‘smart’ environments and promote critical participation in computational publics? Do particular software applications have the potential to transform pedagogy in substantial ways? To what extent do new digital technologies simply fold into existing institutional structures and accelerate or amplify established pedagogies? How will automated production affect education and employment? How is social policy and practice changing in response to predictions about futures shaped by AI and digital labor? How do algorithmic logics shape governance practices (e.g. prediction, anticipation?) How are governments and private entities using machine learning and data science for education policy? How does the use of machine learning and data science affect public scrutiny and debate about decision making in education? Do current ethical and legal guidelines for AI intersect with the principles that underpin public education? Can machine learners ‘compensate for society’ by correcting biases in training data? How can ‘black boxed’ machine learning be adopted in education contexts where situated and culturally responsive practices are necessary?