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Productive Struggle: How Artificial Intelligence is Changing Learning, Effort, and Youth Development in Education.

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Title: Productive Struggle: How Artificial Intelligence is Changing Learning, Effort, and Youth Development in Education.
Authors: Philip, Thomas M.1
Source: Education Review (10945296). 2025, Vol. 32, p1-11. 11p.
Subject Terms: *Educational technology, *Motivation (Psychology), *Metacognition, *Intelligent tutoring systems, *Academic achievement, Youth development
Abstract: Productive Struggle: How Artificial Intelligence is Changing Learning, Effort, and Youth Development in Education, recently released by Bellwether, considers the role of GenAI in education. It proposes a criterion for evaluating these new technologies' impact on student learning: When does ease afforded by GenAI enable greater learning, and when is ease a shortcut with a hidden cost? Toward this end, it examines how AI-powered tools may interact with a student's memory and information processing, attention and engagement, motivation and mindset, and metacognition and self-regulation. Unfortunately, the report conflates the distinct and often implicit meanings of learning, which leads to unjustified conclusions about the potential impact of AI in education. Taking a highly individualistic lens to learning, and thus often overlooking complex classroom environments that impact learning, the report also makes unfounded inferences about the use of AI-powered tools in classrooms. Further, it does not offer proactive recommendations to mitigate the known harm associated with the use of GenAI, particularly when used with children and youth. In sum, while the report restates and reminds us of important questions that must be addressed, its usefulness for policymakers is quite limited. [ABSTRACT FROM AUTHOR]
Copyright of Education Review (10945296) is the property of Educational Policy Analysis Archives & Education Review and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Productive Struggle: How Artificial Intelligence is Changing Learning, Effort, and Youth Development in Education, recently released by Bellwether, considers the role of GenAI in education. It proposes a criterion for evaluating these new technologies' impact on student learning: When does ease afforded by GenAI enable greater learning, and when is ease a shortcut with a hidden cost? Toward this end, it examines how AI-powered tools may interact with a student's memory and information processing, attention and engagement, motivation and mindset, and metacognition and self-regulation. Unfortunately, the report conflates the distinct and often implicit meanings of learning, which leads to unjustified conclusions about the potential impact of AI in education. Taking a highly individualistic lens to learning, and thus often overlooking complex classroom environments that impact learning, the report also makes unfounded inferences about the use of AI-powered tools in classrooms. Further, it does not offer proactive recommendations to mitigate the known harm associated with the use of GenAI, particularly when used with children and youth. In sum, while the report restates and reminds us of important questions that must be addressed, its usefulness for policymakers is quite limited. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Education Review (10945296) is the property of Educational Policy Analysis Archives & Education Review and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 2025
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