AI evidence extraction

Artificial intelligence in science education: A systematic review of applications, impacts, and challenges

Albinа R. Fayzullina, А. А. Филиппова, Garnova N.Yu., Dmitry V. Astakhov, Nadezhda Kalmazova, Zulfiya F. Zaripova · Contemporary Educational Technology · 2025

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

Citations

0.89

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.30935/cedtech/17519

Methodology & findings

Study design

Systematic review of 17 published studies from 2020-2024 analyzing AI applications in science education across different scientific fields and educational settings

Sample

N = 17, 1 group

Main result

The investigation demonstrates "the favorable impact of AI on student performance, motivation, and engagement in science education, particularly in the areas of personalized learning and the development of self-regulated learning skills." The findings revealed "a diverse range of AI applications, including chatbots, intelligent tutoring systems, and AI-enhanced textbooks" serving educational functions from teaching to assessment.

Reports effect sizes and confidence intervals.

Research paradigm

Mixed methods (qualitative and quantitative synthesis)

Author conclusions

The study's findings indicate that "while AI has the potential to greatly improve science education, its successful application necessitates thoughtful evaluation of technological, pedagogical, ethical, and social elements to ensure fair and efficient integration across all educational levels." The study also emphasizes that "the importance of teacher preparation in achieving the successful integration of AI and expresses the necessity of comprehensive professional development."

Risk of bias

Publication bias (only published studies included); Study selection bias (17 studies from 2020-2024 may not represent all relevant research); Language bias (likely English-language studies only); Recency bias (narrow time window 2020-2024); Publication bias (only studies from 2020-2024 included); Potential selection bias in included studies; Heterogeneity in study designs and AI applications

Open questions raised

Investigating the enduring consequences of AI utilization
Exploring its applicability in diverse educational settings
Fostering the growth of AI literacy
Long-term effects of AI use in science education
Cross-cultural and context-specific applications
Long-term effects of AI implementation

Data: not_statedCode: not_statedExtracted from: pdfAgreement 73%

Explore related topics

Evaluation & Benchmarks Scholarly Infrastructure Literature Review