# AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective

arXiv: https://arxiv.org/abs/2606.22809 · HTML: https://mlsanity.com/paper/2606.22809/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 73 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 28 of 28)

- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [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)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Is ChatGPT the Ultimate Programming Assistant -- How far is it?](https://mlsanity.com/paper/2304.11938/) (2023)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment](https://mlsanity.com/paper/2309.14049/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses](https://mlsanity.com/paper/2306.10073/) (2023)
- [Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors](https://mlsanity.com/paper/2306.17156/) (2023)
- [Large Language Models (GPT) for automating feedback on programming assignments](https://mlsanity.com/paper/2307.00150/) (2023)
- [Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant](https://mlsanity.com/paper/2310.16984/) (2023)
- [The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers?](https://mlsanity.com/paper/2305.02878/) (2023)
- [Student-AI Interaction: A Case Study of CS1 students](https://mlsanity.com/paper/2407.00305/) (2024)
- [How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course](https://mlsanity.com/paper/2407.20792/) (2024)
- [Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT](https://mlsanity.com/paper/2405.19132/) (2024)
- [StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions](https://mlsanity.com/paper/2407.12423/) (2024)
- [Generative AI Perceptions: A Survey to Measure the Perceptions of Faculty, Staff, and Students on Generative AI Tools in Academia](https://mlsanity.com/paper/2304.14415/) (2023)
- [Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming](https://mlsanity.com/paper/2312.11567/) (2023)
- [Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course](https://mlsanity.com/paper/2403.15472/) (2024)
- [Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education](https://mlsanity.com/paper/2509.03171/) (2025)
- [ChatGPT impacts in programming education: A recent literature overview that debates ChatGPT responses](https://mlsanity.com/paper/2309.12348/) (2023)
- [Design Implications for Student and Educator Needs in AI-Supported Programming Learning Tools](https://mlsanity.com/paper/2603.22673/) (2026)

## Abstract (arXiv)

Generative AI tools provide novice programmers with instant, personalized support, but also raise concerns about whether AI use supports or bypasses students' regulation of problem-solving. Existing work has largely focused on correctness, usability, or overall usage frequency, with less attention to how student--AI help-seeking unfolds. This study addresses this gap by analyzing AI-assisted help-seeking trajectories in university-level programming. Using an SRL-informed analytical framework that links prompt-level help-seeking codes to conceptual, implementation, debugging, and reflective forms of support, we analyzed 1,290 task-specific student prompts linked to 17,190 code submissions from 71 students in introductory Python programming courses. Specifically, we examined how help-seeking interactions were structured across turns and attempts, and how trajectory patterns related to task scores and the number of code submissions. Results indicate that many students primarily used AI for reactive troubleshooting rather than for planned, self-regulated problem-solving. Although trajectory patterns were not associated with significant differences in task scores, they differed substantially in the number of code submissions required. These findings suggest that the educational significance of AI support lies not only in whether students use AI, but in how their help-seeking trajectories develop during programming problem-solving.