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AI evidence extraction

An Introduction to Large Language Models in Education

Yige Song, Mohammed Saqr, Sonsoles López‐Pernas · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10
Relevance
2/4
Quality (LMQS)
I
Evidence
1
Citations
3.32
FWCI
Top 10%
Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1007/978-3-031-95365-1_8

Methodology & findings

Study design

Narrative review and synthesis of literature on LLM mechanisms and educational applications.

Main result

The chapter examines how "LLMs facilitate applications such as automated feedback, question generation, sentiment analysis, and multilingual accessibility" in educational contexts. The authors highlight that "LLMs are opening new possibilities in education by transforming how students learn and how educators teach and research," with the ability to "dynamically generate test questions tailored to diverse learning levels, translate educational materials to improve accessibility, summarize complex concepts for clearer understanding, and simulate conversational practice to enhance language skills."

Research paradigm

Interpretivist/descriptive

Author conclusions

The authors conclude that "A careful balance between taking advantage of LLMs' strengths and mitigating their shortcomings is necessary to ensure they serve as tools that improve, rather than hinder, educational and research practices." They emphasize that "an important avenue for addressing these challenges is the integration of explainable AI (xAI) techniques with LLMs" and that "As the adoption of LLMs continues, embedding xAI into their workflows will be critical for ensuring ethical and equitable outcomes."

Risk of bias

LLM outputs may contain biases; Potential for fabricated or inaccurate information; Hallucination phenomena not systematically examined

Limitations

  • The authors acknowledge that "LLMs are not without limitations
  • Issues such as biased, inaccurate, or fabricated outputs highlight the need for critical human oversight." They further note that "LLMs may occasionally produce outputs that are factually inaccurate, contextually inappropriate, or overly verbose—a phenomenon known as 'hallucination'
  • This highlights the importance of human oversight to ensure the quality and reliability of their outputs."

Open questions raised

  • Need for critical examination of LLM limitations in education
  • Importance of addressing potential biases in outputs
  • Necessity of ensuring transparency and interpretability
  • Integration of explainable AI (xAI) techniques with LLMs
  • Development of methods to ensure ethical and equitable outcomes in educational applications
  • The authors identify the need for: (1) integration of explainable AI (xAI) techniques with LLMs to enhance transparency and interpretability; (2) critical examination of LLM limitations as their educational integration becomes widespread; (3) further development of education-specific models such as EduBERT and K-12BERT; and (4) continued research on human-in-the-loop approaches to ensure alignment with educational goals and ethical standards.
Code: Hugging Face Transformers: https://huggingface.co/models (over 1 million models available as of Dec 2024); elmer package for R: https://github.com/tidyverse/elmer; https://github.com/tidyverse/elmer (Hugging Face Transformers wrapper); https://github.com/tidyverse/elmerExtracted from: pdfAgreement 81%

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