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

2026-03-23

AI Summary

This study addresses the current lack of longitudinal evidence on the long-term impact of generative AI in programming education. Over a three-year period, we systematically analyzed the evolving patterns of AI usage, cognitive shifts, and academic performance among students in an introductory Python course, integrating survey data, coded student–AI interaction logs, and course assessment records. Our work presents the first three-year longitudinal dataset on generative AI integration in programming instruction, revealing a progressive transition from instrumental tool use toward a deeper transformation of learning paradigms: AI engagement became normalized, and help-seeking behaviors evolved alongside increasing AI literacy. The findings underscore the need for intentional course design that reconfigures effective learning practices to preserve student agency within human–AI collaborative learning environments.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Life-Long and Continual Learning

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

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.

Problem

• Generative AI
• longitudinal study
• student-AI interaction
• computing education
• AI awareness

Innovation

• generative AI
• longitudinal study
• student-AI interaction
• computing education
• AI literacy