[论文解读] SocratiQ: A Generative AI-Powered Learning Companion for Personalized Education and Broader Accessibility

Jason Jabbour, Kai Kleinbard+3 more|ArXiv.org|Feb 1, 2025

Online Learning and Analytics被引用 3

一句话总结

tldr: SocratiQ introduces a generative AI learning companion that uses adaptive, Socratic-style interactions to personalize education and broaden accessibility within an online textbook. It integrates personalized explanations, adaptive assessments, bounded learning, and gamification into MLSysBook.ai.

ABSTRACT

Traditional educational approaches often struggle to provide personalized and interactive learning experiences on a 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 our integration methodology, system architecture, and evaluation framework, along with the technical and pedagogical challenges encountered during implementation and our solutions. 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.

研究动机与目标

• Address the scalability gap in personalized education by leveraging generative AI to tailor learning experiences.
• Integrate a Socratic, Generative Learning framework into an online textbook to enhance engagement and accessibility.
• Demonstrate practical integration methods, architecture, and evaluation insights in a machine learning systems course context.

提出的方法

• Introduce four key features: personalized explanations, adaptive assessments, bounded learning, and gamification.
• Use system prompts to set difficulty levels aligned with Bloom’s taxonomy.
• Employ adaptive, AI-generated quizzes linked to textbook sections via an automated prompt pipeline.
• Bind LLM outputs to curated textbook content using in-context prompts and a fuzzy paragraph matching algorithm.
• Construct a knowledge graph to track reading progress and quiz performance for targeted study guidance.
• Implement privacy-conscious, local-first data architecture to balance curated content with model knowledge.

Figure 1. Overview of SocratiQ’s integration into the online machine learning textbook, showcasing how students can generate quiz questions and engage in natural language conversations for further explanations. You can try it at https://mlsysbook.ai .

实验结果

研究问题

• RQ1 How can AI-driven learning companions be integrated into online textbooks to enhance student engagement and personalization?
• RQ2 What architectural and pedagogical design choices enable effective Socratic, generative interactions for multidisciplinary topics like ML systems?
• RQ3 How do adaptive assessments and bounded learning affect understanding, retention, and completion in large courses?

主要发现

• SocratiQ enables interactive quizzes, real-time feedback, and tailored explanations within an online ML textbook environment.
• Four difficulty levels (Beginner to Expert) aligned with Bloom’s taxonomy guide the AI’s explanations and questions.
• An in-context prompt and text-bound approach helps the LLM stay tied to curated textbook content while leveraging broader pre-trained knowledge.
• A knowledge graph tracks reading progress and quiz performance to help learners focus on weaker areas and plan study steps.
• Adaptive assessments are generated on-demand from chapter sections to provide diverse, targeted practice.

Figure 2. Students can dynamically adjust the academic level to match their learning preferences.