# "Don't Forget the Teachers": Towards an Educator-Centered Understanding of Harms from Large Language Models in Education

arXiv: https://arxiv.org/abs/2502.14592 · HTML: https://mlsanity.com/paper/2502.14592/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2025.

## Built on by (most cited first, 14 of 14)

- [From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Education](https://mlsanity.com/paper/2509.21972/) (2025)
- [Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection](https://mlsanity.com/paper/2601.19245/) (2026)
- [The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors](https://mlsanity.com/paper/2603.00925/) (2026)
- [Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programming](https://mlsanity.com/paper/2603.20211/) (2026)
- [Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment](https://mlsanity.com/paper/2605.01899/) (2026)
- [OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios](https://mlsanity.com/paper/2606.06959/) (2026)
- [Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Courses](https://mlsanity.com/paper/2509.08862/) (2025)
- [Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices](https://mlsanity.com/paper/2509.10780/) (2025)
- [Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education](https://mlsanity.com/paper/2604.07253/) (2026)
- [Beyond "I Can't Help With That": How Child Safety Experts Evaluate AI Chatbot Safety](https://mlsanity.com/paper/2608.07902/) (2026)
- [From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop](https://mlsanity.com/paper/2608.11171/) (2026)
- [When Youth Enter The Chat: An Epistemic Shift in the Validation of LLM-Based Measures of Student Talk](https://mlsanity.com/paper/2608.23780/) (2026)
- [TutorTrace: A Dataset and Taxonomy for Classifying Learner Behavioral States during AI-Assisted Programming Education](https://mlsanity.com/paper/2608.26184/) (2026)
- [Adapting for AI: How elementary teachers adjust their practices for an AI-integrated curriculum](https://mlsanity.com/paper/2609.31569/) (2026)

## Abstract (arXiv)

Education technologies (edtech) are increasingly incorporating new features built on large language models (LLMs), with the goals of enriching the processes of teaching and learning and ultimately improving learning outcomes. However, the potential downstream impacts of LLM-based edtech remain understudied. Prior attempts to map the risks of LLMs have not been tailored to education specifically, even though it is a unique domain in many respects: from its population (students are often children, who can be especially impacted by technology) to its goals (providing the correct answer may be less important for learners than understanding how to arrive at an answer) to its implications for higher-order skills that generalize across contexts (e.g., critical thinking and collaboration). We conducted semi-structured interviews with six edtech providers representing leaders in the K-12 space, as well as a diverse group of 23 educators with varying levels of experience with LLM-based edtech. Through a thematic analysis, we explored how each group is anticipating, observing, and accounting for potential harms from LLMs in education. We find that, while edtech providers focus primarily on mitigating technical harms, i.e., those that can be measured based solely on LLM outputs themselves, educators are more concerned about harms that result from the broader impacts of LLMs, i.e., those that require observation of interactions between students, educators, school systems, and edtech to measure. Overall, we (1) develop an education-specific overview of potential harms from LLMs, (2) highlight gaps between conceptions of harm by edtech providers and those by educators, and (3) make recommendations to facilitate the centering of educators in the design and development of edtech tools.