CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming
CSTutorBench is a new benchmark for evaluating small language models as CS tutors in VEX VR, a block-based robotics environment. Initial tests show models perform well on surface-level criteria like vocabulary and tone but struggle with deeper pedagogical behaviors such as avoiding answer leakage and engaging with student debugging history. Prompt engineering improvements boosted scores for most models.
-->
[Submitted on 6 Jul 2026]
Title:CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming
View a PDF of the paper titled CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming, by H. Chad Lane and 1 other authors
View PDF HTML (experimental)
Abstract:Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on proprietary models. Small language models (SLMs) offer a promising alternative, but selecting the right model for a specific educational context remains difficult, particularly when the target domain, such as block-based programming, is largely absent from model training data. We introduce CSTutorBench, a benchmark for evaluating language models as CS tutors in VEX VR, a block-based robotics environment. The benchmark comprises 17 scenario-based questions scored against a pedagogical rubric grounded in established tutoring and feedback research, with a human-in-the-loop LLM-as-judge pipeline for evaluation. Preliminary findings across 11 models (4B-120B parameters) reveal that models perform well on surface-level criteria such as vocabulary and tone but struggle with deeper pedagogical behaviors, particularly avoiding answer leakage and engaging with student debugging histories. In our sample, model family and instruction-tuning approach appear to be better predictors of tutoring quality than parameter count alone, though the small number of models limits the strength of this conclusion. A targeted prompt revision grounded in recent educational prompt engineering research improved scores for 10 of 11 models. These results underscore the value of context-specific, pedagogically grounded benchmarks for SLM selection in educational deployment.
Subjects:
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2607.05571 [cs.AI]
(or arXiv:2607.05571v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.05571
arXiv-issued DOI via DataCite (pending registration)
Journal reference: SLM4ED'26: The 1st Workshop of Small Language Models for Education (SLM4ED). AIED 2026. Seoul, Republic of Korea
Submission history
From: H Chad Lane [view email] [v1] Mon, 6 Jul 2026 19:15:07 UTC (1,440 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming, by H. Chad Lane and 1 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-07
Change to browse by:
cs cs.HC
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)