# Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions

arXiv: https://arxiv.org/abs/2501.09210 · HTML: https://mlsanity.com/paper/2501.09210/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education](https://mlsanity.com/paper/2506.19107/) (2025)
- [Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programming](https://mlsanity.com/paper/2603.20211/) (2026)
- [Flawed but Memorable: Student Critical Reception of Interest-Personalized GenAI Analogies in Computing Education](https://mlsanity.com/paper/2609.06095/) (2026)

## Abstract (arXiv)

As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention. However, one emerging concern is that students might get answers without learning from the LLM-generated content. In this work, we deployed the LLM-powered personalized Parsons puzzles as scaffolding to write-code practice in a Python learning classroom (PC condition) and conducted an 80-minute randomized between-subjects study. Both conditions received the same practice problems. The only difference was that when requesting help, the control condition showed students a complete solution (CC condition), simulating the most traditional LLM output. Results indicated that students who received personalized Parsons puzzles as scaffolding engaged in practicing significantly longer than those who received complete solutions when struggling.