{"ID":5552845,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T21:38:22.376728174Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00211","arxiv_id":"2607.00211","title":"Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming","abstract":"Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies. This study introduces the conceptual framework of Epistemic AI Literacy (EAIL), reframing AI literacy as a process-oriented epistemic phenomenon that emerges through dynamic human-AI interactions across different domains. Drawing on the AIR (epistemic aims, ideals and reliable epistemic processes) framework, this study examines how epistemic aims and epistemic processes are enacted in GenAI-supported co-programming activities and explores scalable approaches for operationalizing these constructs in interaction data. Using a large dialogue dataset of human-AI co-programming, this study identifies observable dimensions of epistemic aims (i.e., mastery-oriented aims) and epistemic processes (i.e., outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification). The results reveal a prevalent lack of EAIL, with 78.8% of student-GenAI interactions relying on 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 were coupled with advanced epistemic strategies like epistemic justification in a more reliable epistemic process.","short_abstract":"Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies. This study introduces the con...","url_abs":"https://arxiv.org/abs/2607.00211","url_pdf":"https://arxiv.org/pdf/2607.00211v1","authors":"[\"Mengqian Wu\"]","published":"2026-06-30T21:43:35Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.HC\"]","methods":"[]","has_code":false}