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

arXiv: https://arxiv.org/abs/2607.24755 · HTML: https://mlsanity.com/paper/2607.24755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 18 of 18)

- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [GPTutor: a ChatGPT-powered programming tool for code explanation](https://mlsanity.com/paper/2305.01863/) (2023)
- [Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning](https://mlsanity.com/paper/2410.08922/) (2024)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024)
- [ChatGPT, Can You Generate Solutions for my Coding Exercises? An Evaluation on its Effectiveness in an undergraduate Java Programming Course](https://mlsanity.com/paper/2305.13680/) (2023)
- ["It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science](https://mlsanity.com/paper/2311.09651/) (2023)
- [How Do Programming Students Use Generative AI?](https://mlsanity.com/paper/2501.10091/) (2025)
- [LLMs Still Can't Avoid Instanceof: An Investigation Into GPT-3.5, GPT-4 and Bard's Capacity to Handle Object-Oriented Programming Assignments](https://mlsanity.com/paper/2403.06254/) (2024)
- [Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses](https://mlsanity.com/paper/2410.06865/) (2024)
- [Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023](https://mlsanity.com/paper/2411.11166/) (2024)
- [Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education](https://mlsanity.com/paper/2509.03171/) (2025)
- [From Automation to Cognition: Redefining the Roles of Educators and Generative AI in Computing Education](https://mlsanity.com/paper/2412.11419/) (2024)
- [Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption](https://mlsanity.com/paper/2308.12258/) (2023)

## Abstract (arXiv)

This full research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) courses. Generative artificial intelligence (GenAI) tools are increasingly used by students in programming education, yet evidence on their educational impact remains mixed. In particular, little is known about how students integrate GenAI tools when learning OOP, and how different patterns of use relate to students' learning experiences and outcomes. This study investigates patterns of students' self-directed GenAI use and their relationship with academic performance, perceived difficulty, understanding, and trust. Survey data were collected from 210 undergraduate students enrolled in a first-year OOP course in which the use of GenAI tools was permitted for coursework but prohibited in assessments. Results show that 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. While usage patterns were associated with differences in perceived assignment difficulty, self-assessed understanding, trust in AI-generated code, and norm-related attitudes, no significant differences in assessment performance were found across clusters. These findings suggest that self-directed GenAI use alone does not lead to measurable learning gains, underscoring the need for pedagogically guided and process-aware AI support. The study contributes empirical evidence on learner-AI interaction patterns and highlights the importance of pedagogically guided AI use in programming education.