AI evidence extraction

The promise and challenges of generative AI in education

Michail N. Giannakos, Roger Azevedo, Peter Brusilovsky, Mutlu Cukurova, Yannis Dimitriadis, Davinia Hernández‐Leo et al. · Behaviour and Information Technology · 2024

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

6/10

Relevance

1/4

Quality (LMQS)

Evidence

339

Citations

101.54

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/0144929x.2024.2394886

Methodology & findings

Study design

Expert commentary and critical reflection bringing together contributions from nine experts working at the intersection of learning and technology to synthesize perspectives on generative AI in education..

Sample

N = 9, 1 group

Main result

The study acknowledges that "GenAI's capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment." However, the authors also emphasize that "we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses," indicating a balanced but cautionary perspective on generative AI adoption in educational contexts.

Reports effect sizes.

Research paradigm

Critical interpretivism / expert consensus approach

Author conclusions

The authors conclude: "Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices."

Risk of bias

Not applicable - this is a narrative expert commentary rather than an empirical study. No participant selection bias, attrition, or confounding variables are present.; Selection bias in expert panel composition; Potential confirmation bias in expert perspectives; Lack of systematic evidence synthesis methodology

Limitations

The authors highlight that "the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices" represents a significant limitation to current approaches
The commentary is based on expert opinion rather than empirical measurement of intervention effects.

Open questions raised

The authors identify avenues for further research including: (1) development of new insights into the roles human experts can play, (2) strong and continuous evidence, (3) human-centric design of technology, (4) necessary policy, and (5) support and competence mechanisms.
The identified avenues for further research include: (1) development of new insights into the roles human experts can play, (2) strong and continuous evidence generation, (3) human-centric design of technology, (4) necessary policy development, and (5) support and competence mechanisms for educational adoption.
Development of new insights into the roles human experts can play
Strong and continuous evidence on GenAI effectiveness
Human-centric design of technology
Necessary policy development

Data: not_statedCode: not_statedExtracted from: pdfAgreement 62%

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