Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming
This study introduces Epistemic AI Literacy (EAIL), reframing AI literacy as a process-oriented epistemic phenomenon. Using the AIR framework, it analyzes epistemic aims and processes in GenAI-supported co-programming. Analysis of a large human-AI dialogue dataset reveals prevalent lack of EAIL: 78.8% of interactions relied on non-mastery-oriented aims and less reliable strategies, while only 11.1% showed high epistemic engagement.
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[Submitted on 30 Jun 2026]
Title:Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming
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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.
Subjects:
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2607.00211 [cs.AI]
(or arXiv:2607.00211v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.00211
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mengqian Wu [view email] [v1] Tue, 30 Jun 2026 21:43:35 UTC (7,608 KB)
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