Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming

Mengqian Wu (2026)

Epistemic thinking — understanding how knowledge is constructed and justified — plays a central role in AI Literacy, particularly when students co-program with generative AI. This paper introduces a framework for detecting epistemic aims and processes in Student Experience during programming activities. The analysis reveals that students engage in question construction, AI output evaluation, and solution integration as distinct epistemic processes. These findings inform Scaffolding design for programming education and connect to broader discussions of Agentic Education Coding where students maintain agency while leveraging AI assistance.

Key Findings

The paper 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, drawing on the AIR framework of epistemic aims, ideals, and reliable epistemic processes.

Using a large dialogue dataset of human-AI co-programming, the study identifies observable dimensions of epistemic aims (mastery-oriented aims) and epistemic processes (outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification).

A subset of interactions was manually annotated to ground the constructs, which then informed scalable automatic labeling using complementary approaches — few-shot prompting and regex-based scripts — applied interactively.

Results reveal a prevalent lack of EAIL: 78.8% of student-GenAI interactions relied on non-mastery-oriented aims and less reliable epistemic strategies such as outsourcing and verification-seeking.

Only 11.1% of interactions showed high epistemic engagement, where mastery-oriented aims were coupled with advanced strategies like epistemic justification in a more reliable epistemic process.

While GenAI facilitates task success, robust epistemic performance and genuine learning rarely emerge without deliberate instructional and design support.

Study Design & Method

The study operationalizes epistemic constructs that are normally hard to observe. Epistemic aims and processes were detected in student-AI co-programming interaction data, with manual annotation of a subset grounding the constructs. Complementary automated approaches — few-shot prompting with large language models and regex-based scripts — were then used interactively to label the full dataset at scale, providing a path from small-scale qualitative insight to large-scale measurement. The design responds to a limitation identified in a 2022 UNESCO report: AI education has typically taken a technology-oriented approach, ignoring the human and in-depth ethical questions of how AI is actually used in practice.

Implications for AI in Education

The finding that most student-GenAI interactions exhibit weak epistemic engagement — outsourcing and verification-seeking rather than mastery-oriented aims with justification — suggests that mere access to AI tools does not produce learning-oriented use. For Scaffolding design in programming education, the work points to interventions that prompt students to construct questions, evaluate AI outputs, and justify their integration decisions, supporting the development of Metacognition and Self Regulated Learning alongside technical skill. The EAIL framework also connects AI literacy to epistemic practice in CS Education: curricula should cultivate the processes by which learners decide what to trust and why, rather than only measuring whether tasks are completed.

Connected Concepts

AI Literacy

Student Experience

Scaffolding

Connected Articles

Agentic Education Coding

Citation

Mengqian Wu (2026). Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming. arXiv:2607.00211.