Three Years with Classroom AI in Introductory Programming: Shifts in Student Awareness, Interaction, and Performance

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 has examined AI's immediate impacts, multi-year classroom evidence remains limited on how students’ awareness of AI, student-AI interaction patterns, and course outcomes evolve as GenAI 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 course outcomes. We find clear cohort-to-cohort differences in students’ relationships with GenAI. Across cohorts, familiarity and prior use became increasingly normalized before course entry, and student-AI interaction shifted from more implementation-oriented use toward more iterative workflows centered on verification, debugging, and refinement. At the same time, course outcomes remained broadly stable. Together, these findings suggest that, during a period in which GenAI became increasingly normalized in everyday life and classrooms, the most visible cohort-level differences appeared in how students sought help, verified their work, and coordinated problem-solving with AI, rather than in final course performance. The central instructional challenge, therefore, is not simply whether students use AI, but how courses can cultivate productive, course-aligned, and agency-preserving forms of AI use.