[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81754-en":3,"doc-seo-81754-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81754,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Constructing Epistemic AI Literacy Detecting Epistemic Aims and Processes in Student-AI Co-Programming","Epistemic thinking is central to how students learn when using generative AI, especially in programming where learners must form queries, evaluate and validate model outputs, and regulate problem-solving strategies. The study proposes Epistemic AI Literacy (EAIL) as a process-oriented epistemic phenomenon arising from dynamic human–AI interaction. Using a large co-programming dialogue dataset, it identifies observable epistemic aims and processes, supported by manual annotation and scalable automatic labeling. Results show 78.8% of interactions lack mastery-oriented aims and reliable strategies, while only 11.1% show high epistemic engagement with justification.","# Constructing Epistemic AI Literacy:Detecting EpistemicAims and Processes in Student-AI Co-Programming*\n\nMengqian Wu¹  \n¹McGill University,Quebec,Canada  \n## Abstract\n\nEpistemic thinking plays a central role in students'learning processes when applying generative artificialintelligence(GenAI),particularly in programming contexts where learners must construct queries,evaluate andvalidate AI-generated outputs,and regulate problem-solving strategies.This study introduces observable theconceptual framework of Epistemic AI Literacy(EAIL),reframing AI literacy as a process-oriented epistemicphenomenon that emerges through dynamic human-AI interactions across different domains.Drawing onAIR(epistemic aims,ideals and reliable epistemic processes)framework,this study examines how epistemicaims and epistemic processes are enacted in GenAI-supported co-programming activities and explores scalableapproaches for operationalizing these constructs in interaction data.Using a large dialogue dataset of human-AIco-programming,this study identifies observable dimensions of epistemic aims (i.e.,mastery-oriented aims)andepistemic processes (i.e.,outsourcing,explanation seeking,verification seeking,prompt monitoring,and epistemicjustification).A subset of interactions is manually annotated to ground these constructs,which then informscalable automatic labeling using complementary approaches interactively including few-shot prompting andregex-based scripts.The results reveal a prevalent lack of EAIL,with 78.8%of student-GenAI interactions relyingon non-mastery-oriented aims and less reliable epistemic strategies like outsourcing and verification-seeking.Conversely,only 11.1%of interactions showed high epistemic engagement,where mastery-oriented aims werecoupled with advanced epistemic strategies like epistemic justification in a more reliable epistemic process.Thesefindings suggest that while GenAI facilitates task success,robust epistemic performance and genuine learningrarely emerges without deliberate instructional and design support.  \n## Keywords\n\nEpistemic AI Literacy,Epistemic Thinking,Large Language Model,Computer Science Education,Programming  \n## 1.Introduction\n\nAccording to a 2022 UNESCO report [1],‘a technology-oriented approach has been typically takentowards AI skills training,and AI is usually only taught as part of the computing curriculum.Human andin-depth ethical questions are too often ignored'(p.14).It highlights a central limitation of prevailingAI education approaches.When framing skills in AI literacy as technical competencies,it is common tooverlook the human,epistemic,and ethical dimensions of how AI is actually used in practice.Wheninstruction focuses mainly on how AI systems function,rather than how people interpret,trust,evaluate,and regulate AI-generated knowledge,learners are left unprepared to engage critically with AI in realdecision-making contexts.  \nFostering students'AI literacy is incrementally essential,specifically cultivating a specific set ofskills and knowledge related to understanding and engaging with AI technologies [2].AI literacy isbroadly defined as learners'ability to understand,use,evaluate,and critically engage with AI systemsacross technical,ethical,and societal dimensions [3][4][5].Research on AI literacy has expandedsignificantly in recent years,but ongoing debate continues concerning whether literacy should be treatedas general awareness or as a measurable competency,with many frameworks operationalizing literacyin competency-like ways to make it actionable [3][6][7].Accordingly,existing AI literacy researchhas developed a range of frameworks and assessment instruments,most commonly operationalized  \nthrough self-report questionnaires and multiple-choice knowledge tests [8][9].For example,the ABCEframework[4]serves as a prominent model for AI literacy,with its cognitive dimensions closely alignedwith the hierarchical structure of Bloom's taxonomy [10].Within this framework,cognitive learning is","cbCaigFIBBugtqHb","https://ap.wps.com/l/cbCaigFIBBugtqHb","pdf",7790321,4,1,12,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What does Epistemic AI Literacy (EAIL) mean in this study?\",\"answer\":\"EAIL is defined as the ability to understand, regulate, and critically engage with AI systems as cognitive and decision-making agents by pursuing, evaluating, and regulating knowledge during interaction.\"},{\"question\":\"Which observable epistemic aims and processes were identified?\",\"answer\":\"The study identifies epistemic aims such as mastery-oriented goals, and epistemic processes including outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification.\"},{\"question\":\"What were the main findings about students’ use of GenAI in co-programming?\",\"answer\":\"Most student–GenAI interactions (78.8%) relied on non-mastery-oriented aims and less reliable epistemic strategies like outsourcing and verification-seeking, while only 11.1% showed high epistemic engagement tied to more reliable processes such as epistemic 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does Epistemic AI Literacy (EAIL) mean in this study?","Question",{"text":75,"@type":76},"EAIL is defined as the ability to understand, regulate, and critically engage with AI systems as cognitive and decision-making agents by pursuing, evaluating, and regulating knowledge during interaction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which observable epistemic aims and processes were identified?",{"text":80,"@type":76},"The study identifies epistemic aims such as mastery-oriented goals, and epistemic processes including outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings about students’ use of GenAI in co-programming?",{"text":84,"@type":76},"Most student–GenAI interactions (78.8%) relied on non-mastery-oriented aims and less reliable epistemic strategies like outsourcing and verification-seeking, while only 11.1% showed high epistemic engagement tied 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