How Undergraduate and Graduate Students Self-Regulate GenAI Use in Project-Based Learning: Choosing, Creating, Filtering, and Stopping Strategies

Abstract

Generative AI (GenAI) is now common in university project work, yet previous studies often examine students’ trust, creativity, overload, or engagement separately. This leaves a key gap: how students regulate GenAI across a full project workflow. This exploratory study addresses that gap by examining a four-process “ AI-mediated Self-Regulated Learning ” ( AI-SRL ) cycle: (1) evaluating and selecting GenAI suggestions, (2) experiencing shifts in creative agency, (3) managing overload through filtering and summarizing, and (4) monitoring time and energy to stop or continue working with GenAI. We conducted a two-course basic qualitative design study with 97 undergraduate and graduate students. Data came from an open-ended questionnaire aligned to the four processes. We used inductive content analysis with a shared codebook, reliability checks, and cross-level comparisons. Findings show that students use combinations of strategies across the AI-SRL cycle. They exercise agency through goal alignment, revision, and verification, with graduates reporting stronger cross-checking and source-based justification. Creativity was described as a conditional outcome: it increased when GenAI widened ideas but declined when it replaced personal exploration. Overload was managed through targeted prompts and structured outputs, again more common among graduates. Most students did not lose track of time; they used clear stopping cues such as fatigue, repetition, or satisfaction. Together, results reveal two distinct metacognitive regulation styles: “ Exploratory-Simplification ” and “ Systematic-Methodical ”.