Mapping Student-GenAI Interactions onto Experiential Learning: The GENIAL Framework

Mapping Student-GenAI Interactions onto Experiential Learning: The GENIAL Framework | 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 Mapping Student-GenAI Interactions onto Experiential Learning: The GENIAL Framework Jonathan Cardoso-Silva, Dorottya Sallai, Casey Kearney, Francesca Panero, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8631326/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 This study proposes a conceptual framework for understanding how generative artificial intelligence (GenAI) affects university students’ learning journey. The use of GenAI tools in academic settings left scholars at a lack of theoretical tools to capture the impact of how chatbots impact learning. Current studies rely heavily on self-reports and controlled experiments, missing how students engage with chatbots while learning. Building on literature on experiential learning and styles of student engagement with GenAI as theoretical underpinnings, the GENIAL framework sheds light on the dynamics of AI-human interaction in all stages of the experiential learning cycle, allowing us to identify how the cycle of learning is affected by the use of GenAI tools. Our findings, based on the analysis of 200 course chatlogs at a London-based research-intensive university and two case studies in which we illustrate the use of the GENIAL framework, reveal that GenAI can catalyse or disrupt learning depending on student agency and contextual factors. Our framework reveals how students learn with GenAI and identifies if critical steps are missed. By introducing a diagnostic notation that maps GenAI interactions onto Kolb's learning stages, the framework enables educators to mark whether each stage is catalysed (+), disrupted (-), or skipped entirely. We contribute to the literature by turning vague concerns about ‘AI cheating’ into precise diagnostic evaluation that enables educators and policymakers to address the negative impacts of GAI usage on learning as well as the curriculum. Generative artificial intelligence Higher education Learning processes Educational technology Learning analytics 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8631326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635542628,"identity":"314ccbad-5e15-484e-8f85-f441fd941ef2","order_by":0,"name":"Jonathan Cardoso-Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBAC9gbmBwwMbGDmgwNAphwfTEoChxbGBjYDqBYegwMJDAzGbIS18AjAtDAwALUkthGhhfFzRZmNPT8DD+OBxDa79Db+wwcYftQwJM5swKmFWfLMuTSgAqBfEtuSc9sk0hIYe44xJM7GbQuDZGPb4QSD+w+AfjnDDNTCY8DA28CQOA+3FuafQC32BgcYQFrq09n4z39g/ItHi2ADjxjIFsYNYC0VhxPYGHIYmEG24HKYNDObmWUD2C+gQK44bgj0i8FhmWMSxri8z8fe/PhmAzjE2B9/+GBQLc/Pf/jhwzc1NrIzDuCwhhmb4AHcsTIKRsEoGAWjgBgAAM9vVX+WFf/xAAAAAElFTkSuQmCC","orcid":"","institution":"London School of Economics and Political Science","correspondingAuthor":true,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Cardoso-Silva","suffix":""},{"id":635542629,"identity":"2377b5ee-e7cd-40b1-91df-2874e4edbf30","order_by":1,"name":"Dorottya Sallai","email":"","orcid":"","institution":"London School of Economics and Political Science","correspondingAuthor":false,"prefix":"","firstName":"Dorottya","middleName":"","lastName":"Sallai","suffix":""},{"id":635542630,"identity":"4158c376-34c6-422a-866f-f71847406ec0","order_by":2,"name":"Casey Kearney","email":"","orcid":"","institution":"London School of Economics and Political Science","correspondingAuthor":false,"prefix":"","firstName":"Casey","middleName":"","lastName":"Kearney","suffix":""},{"id":635542632,"identity":"e4850abd-2618-4c71-a573-442b1ef15bee","order_by":3,"name":"Francesca Panero","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Panero","suffix":""},{"id":635542639,"identity":"d3a46758-83d5-430d-938c-94b8776eba5f","order_by":4,"name":"Marcos E. 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The use of GenAI tools in academic settings left scholars at a lack of theoretical tools to capture the impact of how chatbots impact learning. Current studies rely heavily on self-reports and controlled experiments, missing how students engage with chatbots while learning. Building on literature on experiential learning and styles of student engagement with GenAI as theoretical underpinnings, the GENIAL framework sheds light on the dynamics of AI-human interaction in all stages of the experiential learning cycle, allowing us to identify how the cycle of learning is affected by the use of GenAI tools. Our findings, based on the analysis of 200 course chatlogs at a London-based research-intensive university and two case studies in which we illustrate the use of the GENIAL framework, reveal that GenAI can catalyse or disrupt learning depending on student agency and contextual factors. Our framework reveals how students learn with GenAI and identifies if critical steps are missed. 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