12,445 papers · continuously updated · last export: 10 Aug 2026livingmeta.ai
← Browse all papers
AI evidence extraction

Machine Learning in Education

Georgios P. Georgiou · Preprints.org · 2026

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

7/10
Relevance
0/4
Quality (LMQS)
I
Evidence
0
Citations

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.20944/preprints202603.0087.v1

Methodology & findings

Study design

Narrative literature review synthesizing current research on machine learning applications in education

Main result

The paper argues that "Machine Learning (ML) is fundamentally reshaping education, offering tools to personalize instruction, automate assessment, and predict student outcomes." Key applications identified include "intelligent tutoring systems, early warning systems for at-risk students, and automated essay scoring, highlighting their potential to address the long-standing challenge of individualized learning at scale." However, the authors emphasize that "this technological integration is fraught with significant challenges" including "ethical concerns regarding algorithmic bias, data privacy, and the 'black box' nature of complex models" that "threaten to exacerbate existing educational inequities."

Research paradigm

interpretivist/critical

Author conclusions

The authors conclude that "while ML holds immense transformative promise, its successful and equitable implementation depends not on technological prowess alone, but on a concerted, ethically-grounded effort involving educators, researchers, and policymakers to ensure these tools augment human expertise and serve all learners."

Open questions raised

  • The paper identifies urgent questions about the nature of learning itself in the context of generative AI tools like ChatGPT, and emphasizes the need for ethically-grounded implementation frameworks involving educators, researchers, and policymakers.
  • The paper identifies urgent questions regarding "the nature of learning itself" in the context of generative AI disruption of assessment and academic integrity paradigms. It also highlights the need for ethically-grounded implementation frameworks involving educators, researchers, and policymakers.
  • The paper identifies urgent questions prompted by generative AI disruption: "The recent proliferation of generative AI, exemplified by tools like ChatGPT, has further disrupted traditional paradigms of assessment and academic integrity, prompting urgent questions about the nature of learning itself."
Data: not_statedCode: not_statedExtracted from: pdfAgreement 89%

Explore related topics

Hallucination ControlAcademic WritingLiterature Review

Related papers

← Browse all papers