Skip to main navigation Skip to search Skip to main content

Trust, Privacy, and Context Dependence of Large Language Models in Educational Contexts: Technical Challenges and Opportunities

  • Noah L. Schroeder
  • , Shan Zhang
  • , Jessica R. Gladstone
  • , Suma Bhat

Research output: Contribution to journal › Editorial › peer-review

Abstract

Large language models (LLMs) are quickly being adopted in educational contexts. In this editorial, we draw attention to three technically-oriented challenges educators may face using LLMs in educational settings when considering student-facing deployment: trust, privacy, and context dependence. Although these challenges are not as well-known as others, such as hallucination and interpretability, they pose significant concerns in educational settings if they are not understood and mitigated before implementation. We discuss practical challenges, risks, and research opportunities associated with each of these technical challenges.

Original languageEnglish (US)
JournalJournal of Educational Computing Research
DOIs
StateAccepted/In press - 2026
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications

Keywords

  • LLM
  • benchmark
  • large language model
  • privacy
  • small language model
  • sycophancy
  • trust

Fingerprint

Dive into the research topics of 'Trust, Privacy, and Context Dependence of Large Language Models in Educational Contexts: Technical Challenges and Opportunities'. Together they form a unique fingerprint.

Cite this