Date
Publisher
arXiv
Generative AI (GenAI) tools such as ChatGPT now provide novice programmers with instant, personalized support and are reshaping computing education. While a growing body of work examines AI's immediate impacts, longitudinal evidence remains limited on how students' awareness, student-AI interaction patterns, and course outcomes evolve as AI becomes routine in classrooms. To address this gap, we investigate an introductory Python course across three successive AI-supported cohorts (2023-2025). Using questionnaires, coded student-AI dialogue logs, and course assessment records, we examine cohort-to-cohort shifts in students' AI awareness, interaction practices, and learning outcomes. We find that students' relationships with GenAI change systematically over time: familiarity and uptake become increasingly normative, and help-seeking practices evolve alongside growing AI literacy and shifting expectations of what the assistant should provide. These changes suggest that, in the AI era, the central instructional challenge is less about whether students use AI and more about how courses redefine productive learning practices while maintaining student agency. Our study offers longitudinal evidence and practical implications for designing and integrating AI programming support in course settings.
Study at a glance
- Question: How do students' AI awareness, interaction workflows, and course performance shift across successive cohorts?
- Setting and data: Undergraduate introductory Python course at a Japanese national university, 2023–2025, 248 students plus a 39-student pre-AI baseline cohort.
- Method: Mixed methods combining surveys, thematic coding and transition network analysis of dialogue logs, and cohort comparisons.
Why it matters
Most research examines the immediate impacts of generative AI, leaving limited longitudinal evidence on how student awareness, interaction patterns, and learning outcomes evolve as AI becomes a routine classroom tool.
Key findings
- Student AI usage became nearly universal, rising from 36.8% active use in 2023 to 91.7% in 2025, alongside increases in self-reported familiarity.
- Student prompting shifted from code implementation requests in 2023 to verification-centered, multi-step debugging workflows in 2024 and 2025.
- Cohort assignment scores and final grades showed no statistically significant differences across years or relative to the pre-AI baseline offering.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
