Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course

Examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming courses. Identifies distinct patterns of GenAI use among students and correlates them with academic performance, finding that certain interaction patterns (seeking explanation rather than code generation) are associated with stronger learning outcomes.

Key Findings

Survey data were collected from 210 undergraduate students in a first-year object-oriented programming (OOP) course where GenAI use was permitted for coursework but prohibited in assessments.

Students used GenAI significantly more often for explanation seeking and debugging than for code generation.

Cluster analysis identified five distinct learner-AI interaction profiles, including a "smart" high-usage pattern characterized by low reliance on code generation and high use for conceptual support and debugging.

Usage patterns were associated with differences in perceived assignment difficulty, self-assessed understanding, trust in AI-generated code, and norm-related attitudes.

Critically, no significant differences in assessment performance were found across clusters — self-directed GenAI use alone did not lead to measurable learning gains.

Study Design & Method

This full research paper investigates how students integrate GenAI tools when learning OOP and how different patterns of use relate to learning experiences and outcomes. The study surveyed 210 first-year undergraduates about their self-directed GenAI use, academic performance, perceived difficulty, understanding, and trust. Cluster analysis was used to derive learner-AI interaction profiles, which were then compared on the self-report and performance measures.

Implications for AI in Education

The absence of performance differences across usage clusters underscores the need for pedagogically guided and process-aware AI support in programming education: letting students self-direct their GenAI use, even in sophisticated patterns, does not by itself produce learning gains. The "smart" profile — high conceptual support and debugging with low code generation — offers a concrete target for course design, suggesting educators should steer students toward explanation seeking and debugging rather than answer generation in CS Education settings.

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Citation

Marina Lepp (2026). Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course. arXiv:2607.24755. cs.HC, cs.AI, cs.CY.