{"paper_id":"0838adbd-4f17-4eb5-9380-2ef841b910af","body_text":"Dovetailing Case-Based Reasoning and Large Language Models to Compare Teaching Strategies: A Mnemonic Augmentation Framework using the EEDI Dataset | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dovetailing Case-Based Reasoning and Large Language Models to Compare Teaching Strategies: A Mnemonic Augmentation Framework using the EEDI Dataset Dietmar Janetzko, Horacio González-Vélez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6232801/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We present a novel hybrid framework dovetailing case-based reasoning (CBR)and AI teaching personas that facilitates mnemonic augmentation to evaluate different teaching strategies for mathematical misconception reduction. Ourapproach uniquely combines empirical student data from the EEDI dataset (17million student interactions) with AI-driven teaching simulations. Each teaching strategy is implemented as an AI persona with CBR-driven mnemonic aug-mentation that relies on the retrieval and application of historical cases whilemaintaining the ability to generate adaptive responses. Through comprehensive cross-validation, we demonstrate that this hybrid approach achieves a 15% reduction in misconceptions compared to traditional methods, outperforming bothpure CBR (8%) and standard AI approaches (11%). The framework providesa a data-driven foundation for evaluating and selecting teaching strategies while acknowledging the complexity of educational interventions. case-based reasoning AI teaching personas mnemonic augmentation mathematical misconceptions EEDI dataset educational interventions Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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