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Field
Abstract
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Before
This study explores how artificial intelligence (AI) can help address the impact of teacher shortages on students' financial literacy. We will conduct a study with about 4,000 students in Flemish secondary schools to see if AI can improve their financial literacy. Students will be randomly assigned to one of three groups: one will learn in a traditional setting and course material, another will learn with the help of a reduced traditional setting and an available AI tool, and a third will learn using a specially designed AI chatbot. We expect that students who learn with AI will do better than those in traditional classes, and that the tailored AI chatbot will be more effective than a general AI model. Additionally, we will investigate whether AI has a stronger impact on students that have less access to resources. We'll be comparing their financial literacy using tests before and after the learning period. To ensure learning retention, students will be tested two months after completing the post-test to assess the long-term learning benefits. We also will analyze how students interact with AI tools. Our goal is to provide insights for educators and policymakers about how AI can be used effectively to address teacher shortages and to enhance educational efficiency and resource allocation in response to the ongoing teacher shortage crisis.
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After
We provide the first causal estimate of the ``tailoring premium'' for educational AI. In a randomized trial with 2,440 secondary students, we demonstrate that offering a curriculum-tailored chatbot increases immediate learning by 0.126 standard deviations, while a generic chatbot produces learning gains that are statistically indistinguishable from traditional instruction. The learning gain is driven by a 13.2 percentage point increase in module completion, demonstrating that the tool's value comes from solving student engagement. For compliers—students induced to complete the module by the tailored design—the effect is larger and more durable, increasing long-term knowledge retention by a significant 0.309 standard deviations. The returns to educational AI depend critically on its design, not just its availability.
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