# Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education

arXiv: https://arxiv.org/abs/2506.19107 · HTML: https://mlsanity.com/paper/2506.19107/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 87 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [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)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences](https://mlsanity.com/paper/2405.04645/) (2024)
- [How Beginning Programmers and Code LLMs (Mis)read Each Other](https://mlsanity.com/paper/2401.15232/) (2024)
- [Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System](https://mlsanity.com/paper/2404.17460/) (2024)
- [Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception](https://mlsanity.com/paper/2310.13712/) (2023)
- [Student-AI Interaction: A Case Study of CS1 students](https://mlsanity.com/paper/2407.00305/) (2024)
- [CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming](https://mlsanity.com/paper/2401.12125/) (2024)
- [Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices](https://mlsanity.com/paper/2404.02213/) (2024)
- [Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course](https://mlsanity.com/paper/2406.06451/) (2024)
- [Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions](https://mlsanity.com/paper/2501.09210/) (2025)

## Built on by (most cited first, 2 of 2)

- [How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures](https://mlsanity.com/paper/2601.06101/) (2026)
- [Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants](https://mlsanity.com/paper/2609.20143/) (2026)

## Abstract (arXiv)

With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes. This work aims to teach students how to effectively prompt LLMs to improve their learning. We first proposed pedagogical prompting, a theoretically-grounded new concept to elicit learning-oriented responses from LLMs. To move from concept design to a proof-of-concept learning intervention in real educational settings, we selected early undergraduate CS education (CS1/CS2) as the example context. We began with a formative survey study with instructors (N=36) teaching early-stage undergraduate-level CS courses to inform the instructional design based on classroom needs. Based on their insights, we designed and developed a learning intervention through an interactive system with scenario-based instruction to train pedagogical prompting skills. Finally, we evaluated its instructional effectiveness through a user study with CS novice students (N=22) using pre/post-tests. Through mixed methods analyses, our results indicate significant improvements in learners' LLM-based pedagogical help-seeking skills, along with positive attitudes toward the system and increased willingness to use pedagogical prompts in the future. Our contributions include (1) a theoretical framework of pedagogical prompting; (2) empirical insights into current instructor attitudes toward pedagogical prompting; and (3) a learning intervention design with an interactive learning tool and scenario-based instruction leading to promising results on teaching LLM-based help-seeking. Our approach is scalable for broader implementation in classrooms and has the potential to be integrated into tools like ChatGPT as an on-boarding experience to encourage learning-oriented use of generative AI.