# Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education

arXiv: https://arxiv.org/abs/2509.03171 · HTML: https://mlsanity.com/paper/2509.03171/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators](https://mlsanity.com/paper/2604.01114/) (2026)
- [Scaffolding Metacognition in Programming Education: Understanding Student-AI Interactions and Design Implications](https://mlsanity.com/paper/2511.04144/) (2025)
- [Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education](https://mlsanity.com/paper/2605.15850/) (2026)
- [AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective](https://mlsanity.com/paper/2606.22809/) (2026)
- [Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course](https://mlsanity.com/paper/2607.24755/) (2026)

## Abstract (arXiv)

The growing adoption of generative AI in education highlights the need to integrate established pedagogical principles into AI-assisted learning environments. This study investigates the potential of metacognitive theory to inform AI-assisted programming education through a hint system designed around the metacognitive phases of planning, monitoring, and evaluation. Upon request, the system can provide three types of AI-generated hints--planning, debugging, and optimization--to guide students at different stages of problem-solving. Through a study with 102 students in an introductory data science programming course, we find that students perceive and engage with planning hints most highly, whereas optimization hints are rarely requested. We observe a consistent association between requesting planning hints and achieving higher grades across question difficulty and student competency. However, when facing harder tasks, students seek additional debugging but not more planning support. These insights contribute to the growing field of AI-assisted programming education by providing empirical evidence on the importance of pedagogical principles in AI-assisted learning.