How First-Year Students Actually Use ChatGPT in Permitted Assessments: Empirical Typologies, Verification Gaps, and the Policy-Practice Divide

How First-Year Students Actually Use ChatGPT in Permitted Assessments: Empirical Typologies, Verification Gaps, and the Policy-Practice Divide | 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 How First-Year Students Actually Use ChatGPT in Permitted Assessments: Empirical Typologies, Verification Gaps, and the Policy-Practice Divide Mahmoud Elkhodr, Ahmedi Azra, Ergun Gide This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8628653/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 Building upon the Structured AI Guided Education (SAGE) framework, this mixed-methods study examines how first-year ICT students navigate generative AI tools during a supervised in-class assessment under institutional AI Collaborate permissions. Analysing behavioural data (n=167) and reflective responses (n=163) collected through an embedded 12-item reflection instrument, a competency-confidence inversion is identified wherein students demonstrate sophisticated AI interaction strategies whilst experiencing regulatory anxiety. Crucially, the data reveals a ``Goldilocks Zone'' of interaction (4--8 prompts) where engagement is optimised, distinguishing effective use from passive consumption. Four distinct student typologies emerged: Strategic Optimisers (32\%), Dialogic Learners (28\%), Cautious Adopters (23\%), and Experimental Users (17\%). Students predominantly seek partnership in developing AI literacy frameworks rather than prescriptive policies, with 77.8\% struggling with verification competencies despite 73\% demonstrating systematic verification behaviours. The findings reveal AI functions as a linguistic equaliser for international students (46.7\% citing English confidence) and transforms rather than eliminates intellectual labour through time reallocation. These empirical patterns validate embedded SAGE verification protocols in cultivating systematic cross-referencing behaviours whilst revealing that verification competency, confidence and ethical awareness require explicit pedagogical intervention beyond assessment-embedded scaffolding alone, positioning students as co-creators rather than compliance subjects in defining legitimate AI-enhanced academic practice. Artificial Intelligence and Machine Learning ChatGPT Generative AI Higher Education Assessment Student AI Engagement Verification Competencies Policy-Practice Gap SAGE Framework First-Year Students Full Text Additional Declarations The authors declare no competing interests. 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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Analysing behavioural data (n=167) and reflective responses (n=163) collected through an embedded 12-item reflection instrument, a competency-confidence inversion is identified wherein students demonstrate sophisticated AI interaction strategies whilst experiencing regulatory anxiety. Crucially, the data reveals a ``Goldilocks Zone'' of interaction (4--8 prompts) where engagement is optimised, distinguishing effective use from passive consumption. Four distinct student typologies emerged: Strategic Optimisers (32\\%), Dialogic Learners (28\\%), Cautious Adopters (23\\%), and Experimental Users (17\\%). Students predominantly seek partnership in developing AI literacy frameworks rather than prescriptive policies, with 77.8\\% struggling with verification competencies despite 73\\% demonstrating systematic verification behaviours. 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