student-learning
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- PaperarXiv — AI in Education (cs.CY)6 Jul 2026
When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code
Victor-Alexandru Pădurean, Kaitlin Riegel, Alkis Gotovos, Jyotika Mahapatra et al.
Researchers analyzed 2,636 sessions from 917 CS1 students using GenAI to write code, comparing responses to naturally occurring prompt failures and deliberately injected bugs. Injected bugs prompted direct code edits and higher success rates, while prompt failures led students to refine their prompts. The study suggests combining both bug types fosters code review, debugging, and specification refinement skills.
Original abstract
As Generative AI (GenAI) becomes increasingly central to software development, CS education is integrating prompt-centered workflows where students describe intended program behavior in natural language to elicit code. However, professional practice requires careful review and verification of GenAI-generated code that may appear correct while containing subtle faults. This creates a challenge for CS1-level activities, where current models often solve tasks correctly and reduce students' incentive to closely inspect generated outputs. We investigate how prompt-centered programming activities can be adapted to better foster these practices. Specifically, we explore an approach where realistic, runnable bugs are injected into otherwise correct solutions, thus requiring students to read and repair generated outputs. We analyzed 2,636 sessions from 917 students, and examined behavior across instances of naturally occurring prompt-related failures and deliberately injected bugs within each session. Our findings show that students responded differently across bug sources. Deliberately injected bugs more often led to direct code edits and higher next-attempt success, suggesting localized repair of near-miss solutions. Prompt-related failures instead more often led students to refine prompts by clarifying constraints, updating function signatures, adding edge cases, or reframing the task. Student reflections reinforce the emphasis on review and repair, describing useful practice in code understanding, code review, and debugging, as well as a more careful verification mindset and greater awareness of GenAI limitations. Ultimately, prompt-related failures and injected bugs together support a pedagogically useful GenAI workflow, where students practice both specification refinement through prompts and debugging through code editing.
- PaperJournal of Learning Analytics18 Mar 2026
Beyond Time on Task
Paul V. Sargent, Isabel Hilliger, Jorge A. Baier
This study analyzed student workload dynamics beyond average time-on-task across 14 engineering courses, finding that workload peaks correlate with perceived learning and difficulty. The authors propose metrics for practical use in course design and suggests that considering workload dynamics provides better predictions of student learning than average measures alone.
Original abstract
Student workload analysis has the potential to play a crucial role in providing both actionable insights to inform course design and curricular adjustments that promote student learning and well-being. While numerous studies have emphasized the need for analyzing workload beyond single-value metrics, such as credit hours, the interpretation and practical application of these metrics for educational interventions remains unclear. In this study, we explore the interplay between time-on-task measurements with student-perceived learning and difficulty.We move beyond average indicators of time-on-task by proposing and examining various metrics related to the dynamics of workload over time. Across 14 engineering courses taught at Pontificia Universidad Católica de Chile, we analyze three different sources of data: (1) self-reported time-on-task and perceived difficulty obtained through a weekly timesheetsurvey, (2) interactions with the learning management system (LMS), and (3) perceived learning attainment obtained from the course evaluation survey. Our results show that LMS-based and self-reported time-on-task were highly correlated. Also, workload dynamics metrics, such as the presence of workload peaks, were highly correlated with perceived learning and perceived difficulty. As such, this study provides evidence in support of considering workload dynamics, rather than average measures of time-on-task, to predict variables related to student learning. The metrics proposed by this framework could be used to implement practical tools for educators and administrators willing to optimize course design and improve learning attainment.