"Don't Forget the Teachers": Towards an Educator-Centered Understanding of Harms from Large Language Models in Education
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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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2025.
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Citing papers per year: 2025: 3; 2026: 11.
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- 2025From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Educationcited by 9
- 2026Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detectioncited by 1
- 2026The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errorscited by 1
- 2026Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programmingcited by 1
- 2026OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarioscited by 1
- 2025Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Coursescited by 0
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practicescited by 0
- 2026Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Educationcited by 0
- 2026Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignmentcited by 0
- 2026Beyond "I Can't Help With That": How Child Safety Experts Evaluate AI Chatbot Safetycited by 0
- 2026From Interpretability to Control: Insights from Six Years of the TrustNLP Workshopcited by 0
- 2026When Youth Enter The Chat: An Epistemic Shift in the Validation of LLM-Based Measures of Student Talkcited by 0
- 2026Adapting for AI: How elementary teachers adjust their practices for an AI-integrated curriculum25 Sep 2026
- 2026When Youth Enter The Chat: An Epistemic Shift in the Validation of LLM-Based Measures of Student Talk24 Aug 2026
- 2026TutorTrace: A Dataset and Taxonomy for Classifying Learner Behavioral States during AI-Assisted Programming Education22 Aug 2026
- 2026From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop11 Aug 2026
- 2026Beyond "I Can't Help With That": How Child Safety Experts Evaluate AI Chatbot Safety8 Aug 2026
- 2026OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios5 Jun 2026
- 2026Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment3 May 2026
- 2026Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education8 Apr 2026
- 2026Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programming2 Mar 2026
- 2026The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors1 Mar 2026
- 2026Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection27 Jan 2026
- 2025From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Education26 Sep 2025
2 more
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices13 Sep 2025
- 2025Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Courses10 Sep 2025
- 2025From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Educationcited by 9
- 2026Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detectioncited by 1
- 2026Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programmingcited by 1
- 2026OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarioscited by 1
- 2025Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Coursescited by 0
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practicescited by 0
- 2026Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Educationcited by 0
- 2026Beyond "I Can't Help With That": How Child Safety Experts Evaluate AI Chatbot Safetycited by 0
- 2026From Interpretability to Control: Insights from Six Years of the TrustNLP Workshopcited by 0
- 2026When Youth Enter The Chat: An Epistemic Shift in the Validation of LLM-Based Measures of Student Talkcited by 0
- 2026TutorTrace: A Dataset and Taxonomy for Classifying Learner Behavioral States during AI-Assisted Programming Educationcited by 0
- 2026Adapting for AI: How elementary teachers adjust their practices for an AI-integrated curriculumcited by 0
- 2026Adapting for AI: How elementary teachers adjust their practices for an AI-integrated curriculum25 Sep 2026
- 2026When Youth Enter The Chat: An Epistemic Shift in the Validation of LLM-Based Measures of Student Talk24 Aug 2026
- 2026TutorTrace: A Dataset and Taxonomy for Classifying Learner Behavioral States during AI-Assisted Programming Education22 Aug 2026
- 2026From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop11 Aug 2026
- 2026Beyond "I Can't Help With That": How Child Safety Experts Evaluate AI Chatbot Safety8 Aug 2026
- 2026OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios5 Jun 2026
- 2026Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education8 Apr 2026
- 2026Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programming2 Mar 2026
- 2026Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection27 Jan 2026
- 2025From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Education26 Sep 2025
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices13 Sep 2025
- 2025Investigating Student Interaction Patterns with Large Language Model-Powered Course Assistants in Computer Science Courses10 Sep 2025