# Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programming

arXiv: https://arxiv.org/abs/2603.20211 · HTML: https://mlsanity.com/paper/2603.20211/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 89 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [Next-Step Hint Generation for Introductory Programming Using Large Language Models](https://mlsanity.com/paper/2312.10055/) (2023)
- [Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception](https://mlsanity.com/paper/2310.13712/) (2023)
- [Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms](https://mlsanity.com/paper/2009.11100/) (2020)
- ["Don't Forget the Teachers": Towards an Educator-Centered Understanding of Harms from Large Language Models in Education](https://mlsanity.com/paper/2502.14592/) (2025)
- [CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming](https://mlsanity.com/paper/2401.12125/) (2024)
- [Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators](https://mlsanity.com/paper/2502.09799/) (2025)
- [Partnering with AI: A Pedagogical Feedback System for LLM Integration into Programming Education](https://mlsanity.com/paper/2507.00406/) (2025)
- [Automating Personalized Parsons Problems with Customized Contexts and Concepts](https://mlsanity.com/paper/2404.10990/) (2024)
- [Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions](https://mlsanity.com/paper/2501.09210/) (2025)
- [Scratch Copilot: Supporting Youth Creative Coding with AI](https://mlsanity.com/paper/2505.03867/) (2025)
- [From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education](https://mlsanity.com/paper/2506.15955/) (2025)
- [Comparative Analysis of STEM and non-STEM Teachers' Needs for Integrating AI into Educational Environments](https://mlsanity.com/paper/2509.16276/) (2025)
- [Evaluating the Effectiveness of Large Language Models in Solving Simple Programming Tasks: A User-Centered Study](https://mlsanity.com/paper/2507.04043/) (2025)

## Built on by (most cited first, 1 of 1)

- [How Children Design and Reason about Trustworthy AI Chatbots](https://mlsanity.com/paper/2609.25244/) (2026)

## Abstract (arXiv)

AI-based chatbots have the potential to accelerate learning and teaching, but may also have counterproductive consequences without thoughtful design and scaffolding. To better understand teachers' perspectives on large language model (LLM)-based chatbots, we conducted a study with 11 teams of middle school teachers using chatbots for a science and computational thinking activity within a block-based programming environment. Based on a qualitative analysis of audio transcripts and chatbot interactions, we propose three profiles: explorer, frustrated, and mixed, that reflect diverse scaffolding needs. In their discussions, we found that teachers perceived chatbot benefits such as building prompting skills and self-confidence alongside risks including potential declines in learning and critical thinking. Key design recommendations include scaffolding the introduction to chatbots, facilitating teacher control of chatbot features, and suggesting when and how chatbots should be used. Our contribution informs the design of chatbots to support teachers and learners in middle school coding activities.