Constructing Epistemic AI Literacy: A Framework for Student-AI Co-Programming

Mengqian Wu has introduced a new framework for understanding Epistemic AI Literacy (EAIL) in student-AI co-programming, highlighting its importance in educational contexts. This study, presented on June 30, 2026, reveals how students interact with generative artificial intelligence (GenAI) to enhance their programming skills.

Understanding Epistemic AI Literacy

Epistemic thinking is crucial for students when utilizing GenAI, especially in programming. The framework proposed by Wu redefines AI literacy as a process-oriented phenomenon that evolves through dynamic interactions between humans and AI. This approach emphasizes the need for students to construct meaningful queries, critically evaluate AI-generated outputs, and effectively regulate their problem-solving strategies.

The study employs the AIR framework, which encompasses epistemic aims, ideals, and reliable epistemic processes, to analyze student interactions with GenAI during programming tasks. By focusing on these aspects, educators can better understand how students engage with AI technologies.

Key Findings on Student Interactions with AI

Analysis of a large dialogue dataset from student-AI co-programming activities has uncovered significant trends in epistemic aims and processes. The findings indicate that a staggering 78.8% of student interactions relied on non-mastery-oriented aims, such as outsourcing tasks and verification-seeking strategies, rather than engaging in deeper epistemic processes.

• Observable dimensions of epistemic aims: Mastery-oriented aims vs. non-mastery-oriented aims
• Common epistemic processes: Outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification

Only 11.1% of interactions showcased high levels of epistemic engagement, where mastery-oriented aims were paired with advanced epistemic strategies like justification. This highlights the need for more effective educational practices to foster EAIL among students.

Implications for Educational Practices

The results of this study have significant implications for educators and curriculum developers. By recognizing the gaps in EAIL, targeted interventions can be designed to enhance students' interactions with AI. Educators can implement strategies that encourage mastery-oriented aims and promote reliable epistemic processes in AI-supported learning environments.

In conclusion, as AI technologies continue to evolve, understanding and cultivating Epistemic AI Literacy will be essential for preparing students to navigate the complexities of modern programming and AI interaction.