Led by Lev Vygotsky Simulacrum
About this course
How can you see that a step is climbable before you try it, do with a teacher what you cannot yet do alone, and think with notebooks, colleagues and machines? This course answers with the research that places much of perceiving, learning and thinking outside the individual head.
Lev Vygotsky Simulacrum hosts, teaching the zone of proximal development and distributed cognition. The Gibsonian Affordances Simulacrum teaches ecological perception, John Dewey Simulacrum teaches situated learning, and the Engelbartian Augmentation Simulacrum teaches the design of human-AI partnership, with guest tutors who include Jerome Bruner, Benjamin Bloom and George Miller.
From the first module you choose one real learning or thinking situation you take part in, such as learning a skill, working in a team or studying with an AI assistant. You analyse it in every module and present a redesign of it at the end.
Led by Gibsonian Affordances Simulacrum
The question
The Gibsonian Affordances Simulacrum teaches perception as the pickup of information by an animal moving through its surroundings. You write affordances as relations between a surface and a particular body, use the stair-climbing ratio to predict what a person can climb, weigh the claim that perception needs no inner picture against David Marr's objection, and work out how an expanding image tells a diving bird or a driver when contact will come. You choose the learning situation you will study through the course and audit the affordances of its main tool.
Outcome
You can state what a surface or tool affords to a particular person, explain the case for and against direct perception, and audit the affordances of a real interface. (Ecological perception)
Sub-units
Led by Lev Vygotsky Simulacrum
The question
Lev Vygotsky Simulacrum teaches the gap between what a learner can do alone and what they can do with help, and how good instruction works inside it. You record that gap for a real task, design with Jerome Bruner Simulacrum a scaffold that withdraws as the learner succeeds, trace how talk with other people becomes thinking on your own, and test with Benjamin Bloom Simulacrum whether an AI tutor is building your ability or doing the work for you.
Outcome
You can locate a learner's zone of proximal development, design help that fades, and judge whether an AI tutor scaffolds learning or replaces it. (Zone of proximal development)
Sub-units
Led by Lev Vygotsky Simulacrum
The question
Vygotsky turns to thinking that is spread across people, tools and notations. You follow a warship's navigation team as it fixes the ship's position with instruments and charts, see how a checklist or a diagram changes the task rather than the person, analyse teams and cockpits as single thinking systems, and with the Engelbartian Augmentation Simulacrum map where the thinking in your own situation is held and which of your skills an AI tool strengthens or wears away.
Outcome
You can trace a task as information passed between people and tools, and judge where an AI partner extends thinking and where it erodes it. (Distributed cognition)
Sub-units
Led by John Dewey Simulacrum
The question
John Dewey Simulacrum teaches learning as joining a practice rather than receiving knowledge. You compare apprenticeships that worked and failed, from Liberian tailors to supermarket butchers, analyse a community of practice you belong to, find out why what is learned in one setting so often fails in another, and design an apprenticeship that makes an expert's thinking visible. The Brooksian Systems Simulacrum closes the module on why machine-learning systems also fail when moved out of the setting they learned in.
Outcome
You can analyse learning as growing participation in a practice and explain why knowledge, human or machine, so often fails to transfer between settings. (Situated learning)
Sub-units
Led by Engelbartian Augmentation Simulacrum
The question
The Engelbartian Augmentation Simulacrum asks when people and AI systems think better together than apart. You weigh the evidence on human-AI teams, analyse cognitive load with George Miller Simulacrum, decide with the Kahnemanian Cognition Simulacrum when a machine should recommend and when it should decide, and test with Vygotsky whether an AI's explanation actually helps you understand. In the final sitting you present an analysis and redesign of the learning or thinking situation you chose in the first module.
Outcome
You can design a human-AI partnership that leaves people more capable, and present an analysis and redesign of a real learning or thinking situation. (Cognitive partnership and capstone)
Sub-units