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TRUVACE RECORD VERSION
record: TRV-2026-2857
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:35:42.167643Z
status: published
lens: g_space
sector: education
headline: Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis
dek: Abstract Artificial intelligence (AI) and computational sciences have aroused a growing interest in education. Despite its relatively recent history, AI is increasingly being introduced into the classroom through different modalities, with the aim of improving student achievement. Thus, the purpose of the research is to analyse, quantitatively and qualitatively, the impact of AI components and computational sciences on student performance. For this purpose, a systematic review and meta-analysis have been carried…
gain_title: A systematic review and meta-analysis of 25 articles found that AI and computational sciences had a positive impact on student performance, with higher attitude towards learning and motivation, especially in STEM subjects.
problem_title: (none)
trace_subject: (none)
gain_reading: A systematic review and meta-analysis of 25 articles found that AI and computational sciences had a positive impact on student performance, with higher attitude towards learning and motivation, especially in STEM subjects.
gain_evidence: The results support the positive impact that AI and computational sciences have on student performance | finding a rise in their attitude towards learning and their motivation, especially in the STEM
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis searched the Web of Science and Scopus databases for research on how AI components and computational sciences affect student performance. After inclusion and exclusion criteria were applied, 25 articles remained in the sample.

The results support a positive impact on performance, with a rise in students' attitude towards learning and motivation, especially in STEM (science, technology, engineering and mathematics) areas. The findings were consistent at all educational stages.

The authors say using these technologies in teaching brings a large educational and ethical challenge for teachers in how the tools are designed and implemented, which calls for further educational research.
limitation: The authors say that implementing these technologies in instruction poses a great educational and ethical challenge for teachers regarding design and implementation, which requires further analysis.
tag: Evidence-backed gain
key_points: The review searched two databases, Web of Science and Scopus. | After inclusion and exclusion criteria, 25 articles were analysed. | The analysis combined quantitative and qualitative approaches. | Student performance improved, along with attitude towards learning and motivation. | The effects were strongest in STEM areas. | Findings were consistent across all educational stages. | Teachers face educational and ethical challenges in designing and implementing the technologies.
rundown: The study set out to analyse, both quantitatively and qualitatively, how AI components and computational sciences affect student performance. It used a systematic review and meta-analysis of literature from the Web of Science and Scopus databases.

Applying inclusion and exclusion criteria left a sample of 25 articles. The authors report that results support a positive impact on student performance, including higher motivation and a better attitude towards learning, especially in STEM subjects.

The authors report consistent findings at all educational stages. They also say that putting these technologies into instruction poses a great educational and ethical challenge for teachers, particularly in design and implementation, and call for further educational research.
sources:
- peer_reviewed | Journal of New Approaches in Educational Research | https://doi.org/10.7821/naer.2023.1.1240 | 2023-01-01
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