# Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments

arXiv: https://arxiv.org/abs/2606.15766 · HTML: https://mlsanity.com/paper/2606.15766/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://mlsanity.com/paper/2305.14536/) (2023)
- [Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces](https://mlsanity.com/paper/2309.14459/) (2023)
- [Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach](https://mlsanity.com/paper/2407.12687/) (2024)
- [MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors](https://mlsanity.com/paper/2502.18940/) (2025)
- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [Navigating Rifts in Human-LLM Grounding: Study and Benchmark](https://mlsanity.com/paper/2503.13975/) (2025)
- [Towards Modeling Learner Performance with Large Language Models](https://mlsanity.com/paper/2403.14661/) (2024)
- [EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios](https://mlsanity.com/paper/2505.16160/) (2025)
- [Thinking beyond the anthropomorphic paradigm benefits LLM research](https://mlsanity.com/paper/2502.09192/) (2025)
- [PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues](https://mlsanity.com/paper/2505.16931/) (2025)
- [RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education](https://mlsanity.com/paper/2403.08272/) (2024)
- [A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents](https://mlsanity.com/paper/2508.01503/) (2025)
- [Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks](https://mlsanity.com/paper/2604.18660/) (2026)
- [How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students](https://mlsanity.com/paper/2505.24126/) (2025)
- [PATS: Personality-Aware Teaching Strategies with Large Language Model Tutors](https://mlsanity.com/paper/2601.08402/) (2026)
- [Do AI tutors empower or enslave learners? Toward a critical use of AI in education](https://mlsanity.com/paper/2507.06878/) (2025)
- [Scaffolding Metacognition in Programming Education: Understanding Student-AI Interactions and Design Implications](https://mlsanity.com/paper/2511.04144/) (2025)
- ["Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbots](https://mlsanity.com/paper/2603.00057/) (2026)

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

- [Potential for Enhanced Learning in Machine Learning Classes by Using Wiki LLM Indexing](https://mlsanity.com/paper/2609.25303/) (2026)

## Abstract (arXiv)

A central pedagogical value evaluated in AI tutor benchmarks is scaffolding: guiding students through graduated steps toward a solution. Alignment and evaluation methods for embedding scaffolding behaviour into chatbots, however, rest on an implicit assumption: that students will take up the scaffolding and engage in the conversation. To examine whether this assumption holds, we introduce an evaluation pipeline around two metrics - Chatbot Scaffolding and Student Uptake - and apply them across nine datasets of 9,490 chats, spanning AI tutor benchmarks and real-world deployments of educational chatbots. Our analysis reveals that while benchmarks assume a high-scaffolding, high-student-uptake environment, students in real-world settings exhibit lower levels of uptake overall - frequently bypassing the chatbot's pedagogical framing to drive the interaction toward their own learning goals at little interpersonal cost. We argue that bypassing scaffolding is not necessarily detrimental; rather, it frequently highlights a mismatch between a chatbot's pedagogical framing and the student's learning goals. To meaningfully evaluate the effectiveness of a chatbot's assistance, future benchmarks must move beyond the assumption that students will simply take up the scaffolding, and instead evaluate how these chatbots navigate diverse learning contexts and student-driven interaction patterns.