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Keynote: Chris Dede ELAI2022 Conference
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Title: Intelligence Augmentation via Artificial Intelligence: IA rather than AI
Description: Science fiction often portrays “intelligence” as involving complementary roles of reckoning and judgment. For example, In the Star Trek series the judgment and decision making of ‘Captain Picard’ is enhanced by the reckoning skills (such as calculations, administration, and computation) of the android ‘Data’, a machine without human capacities like emotions. This partnership is Intelligent Augmentation (IA); the human and machine work synergistically together to be greater than their individual abilities.
While many forecasts chart an evolution of artificial intelligence (AI) in taking human jobs, more likely is a future where AI changes the division of labor in most work-roles, driving a need for workforce development to shift towards uniquely human skills. This forecast implies that learning knowledge, skills, and dispositions for work should increasingly prioritize capability building of human judgment, applied wisdom, and decision making—at the expense of developing some reckoning skills that AI will assume. Moreover, machine learning (ML) could be used to help “engineer” learning, by applying evidence-based strategies to the continual re-design of performance-based simulation experiences to optimize their effectiveness and efficiency. This will enable developing diagnostic/formative longitudinal assessments of judgement that complement our current high-stakes tests centered on reckoning.
Description: Science fiction often portrays “intelligence” as involving complementary roles of reckoning and judgment. For example, In the Star Trek series the judgment and decision making of ‘Captain Picard’ is enhanced by the reckoning skills (such as calculations, administration, and computation) of the android ‘Data’, a machine without human capacities like emotions. This partnership is Intelligent Augmentation (IA); the human and machine work synergistically together to be greater than their individual abilities.
While many forecasts chart an evolution of artificial intelligence (AI) in taking human jobs, more likely is a future where AI changes the division of labor in most work-roles, driving a need for workforce development to shift towards uniquely human skills. This forecast implies that learning knowledge, skills, and dispositions for work should increasingly prioritize capability building of human judgment, applied wisdom, and decision making—at the expense of developing some reckoning skills that AI will assume. Moreover, machine learning (ML) could be used to help “engineer” learning, by applying evidence-based strategies to the continual re-design of performance-based simulation experiences to optimize their effectiveness and efficiency. This will enable developing diagnostic/formative longitudinal assessments of judgement that complement our current high-stakes tests centered on reckoning.
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