In authors or contributors

9 resources

  • EdArXiv
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    Jun 2nd, 2023
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    report
    EdArXiv
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • EdArXiv
    |
    Jun 2nd, 2023
    |
    report
    EdArXiv
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • EdArXiv
    |
    Jun 2nd, 2023
    |
    report
    EdArXiv
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • Rose Wang, Dorottya Demszky
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    Jun 2nd, 2023
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    preprint
    Rose Wang, Dorottya Demszky
    Jun 2nd, 2023

    Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript...

  • Rose E. Wang, Qingyang Zhang, Carly Robi...
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    Oct 29th, 2024
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    preprint
    Rose E. Wang, Qingyang Zhang, Carly Robi...
    Oct 29th, 2024

    Scaling high-quality tutoring remains a major challenge in education. Due to growing demand, many platforms employ novice tutors who, unlike experienced educators, struggle to address student mistakes and thus fail to seize prime learning opportunities. Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes. We contribute Bridge, a method that uses cognitive task analysis to translate an expert's latent thought...

  • Rose E Wang, Ana T Ribeiro, Carly D Robi...
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    Nov 25th, 2024
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    journalArticle
    Rose E Wang, Ana T Ribeiro, Carly D Robi...
    Nov 25th, 2024
  • Rose E. Wang, Ana T. Ribeiro, Carly D. R...
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    Oct 3rd, 2024
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    preprint
    Rose E. Wang, Ana T. Ribeiro, Carly D. R...
    Oct 3rd, 2024

    Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately harms students from under-served communities, who stand to gain the most from high-quality...

  • Rishi Bommasani, Drew A. Hudson, Ehsan A...
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    Oct 29th, 2021
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    journalArticle
    Rishi Bommasani, Drew A. Hudson, Ehsan A...
    Oct 29th, 2021

    AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical...

  • Rishi Bommasani, Drew A. Hudson, Ehsan A...
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    Jul 12th, 2022
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    preprint
    Rishi Bommasani, Drew A. Hudson, Ehsan A...
    Jul 12th, 2022

    AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical...

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