SocratiQ: A Generative AI-Powered Learning Companion for Personalized Education and Broader Accessibility

Traditional educational approaches often struggle to provide personalized and interactive learning experiences at scale. In this paper, we present SocratiQ, an AI-powered educational assistant that addresses this challenge by implementing the Socratic method through adaptive learning technologies. The system employs a novel Generative AI-based learning framework that dynamically creates personalized learning pathways based on student responses and comprehension patterns. We provide an account of the core design elements of SocratiQ, practical considerations for its deployment, and user feedback on its effectiveness. Although our implementation focuses on machine learning systems education, the integration approaches we present can inform similar efforts across STEM fields. Through this work, our goal is to advance the understanding of how generative AI technologies can be designed and systematically incorporated into educational resources.

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Publication Details

Published
2025-06-21
DOI
https://doi.org/10.1145/3743646.3750010
Citations
4
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
6.77
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SocratiQ: A Generative AI-Powered Learning Companion for Personalized Education and Broader Accessibility

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SocratiQ: A Generative AI-Powered Learning Companion for Personalized Education and Broader Accessibility

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Abstract

Traditional educational approaches often struggle to provide personalized and interactive learning experiences at scale. In this paper, we present SocratiQ, an AI-powered educational assistant that addresses this challenge by implementing the Socratic method through adaptive learning technologies. The system employs a novel Generative AI-based learning framework that dynamically creates personalized learning pathways based on student responses and comprehension patterns. We provide an account of the core design elements of SocratiQ, practical considerations for its deployment, and user feedback on its effectiveness. Although our implementation focuses on machine learning systems education, the integration approaches we present can inform similar efforts across STEM fields. Through this work, our goal is to advance the understanding of how generative AI technologies can be designed and systematically incorporated into educational resources.

Harvard University Press (US)
Openalex Percentile: Top 3%
Online Learning and Analytics
6.77
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