Learning Under Algorithmic Conditions: Critical Perspectives on AI and Curriculum

Session Type: Symposium

Abstract

Concepts related to curriculum studies (learning, training, reason, intelligence, memory) are all key notions in machine learning and AI, where theories of learning are often borrowed directly from cognitive psychology, and inherit assumptions from a long-standing colonial legacy of learning research. This symposium offers a critical discussion of how machine learning and human learning are converging under current algorithmic conditions. Speakers share research on a series of case studies (robots, deep fakes, soft thought, participatory AI, computational thinking, ChatGPT) exploring key questions: How might current AI models be reshaping the way we understand curriculum studies? In what ways is curriculum newly linked to machine learning algorithms? How are knowledge and reason recast under these new algorithmic conditions?

Sub Unit

• Division B - Curriculum Studies / Division B - Section 4: Policies and the Politics of Curriculum

Chair

• Craig A. Carson, Adelphi University

Papers

• The limits of learning with participatory AI - Michael Madaio, Google Research

• Learning as Computational Thinking - Matthew X. Curinga, Adelphi University

• The role of deception and fabulation in generative algorithms - Elizabeth De Freitas, Adelphi University

• Instruction vs. Construction: How Might Generative Artificial Intelligence Best Support Learning? - Shayan Doroudi, University of California - Irvine; Sina Rismanchian, University of California - Irvine

• Surrogate robot pedagogy and the racialized inner child - Ezekiel J. Dixon-Roman, Teachers College, Columbia University

• Learning with Incomputable Algorithms and Soft Thought - P. Taylor Webb, University of British Columbia

Discussant

• Craig A. Carson, Adelphi University