AI evidence extraction

Use of ChatGPT as a supplemental learning tool in mathematics

Maria Rhoda Damos Sosas, Arcelli Faith Getalla Fat, Jody May Minao Dangculos, Harvey Rex Pitogo Anoba, Charlene Go Remiscal, Judith M. Aleguen · Contemporary Mathematics and Science Education · 2026

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

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

7.33

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.30935/conmaths/17989

Methodology & findings

Study design

Mixed-method approach combining quantitative surveys (n=91) with qualitative interviews (n=20), grounded in diffusion of innovation theory, connectivism learning theory, technology acceptance model, and community of inquiry framework.

Sample

N = 111, 2 groups

Primary method

Statistical analysis of survey data grounded in diffusion of innovation theory, technology acceptance model, and connectivism learning theory. Thematic analysis of qualitative interview data.

Main result

The pre-service teachers "exhibited a positive perception of using ChatGPT as a supplemental learning tool in mathematics, noting it as very easy to use, beneficial for efficient learning and task completion, and cognitive development, and compatible with learning citing its adaptability to learners' needs and demands in education." However, they also identified challenges including "occasional inaccuracies, functional limitations, the risk of over-reliance, and academic integrity" concerns.

Reports effect sizes.

Research paradigm

Mixed methods (pragmatism) - combining quantitative and qualitative approaches

Author conclusions

The authors conclude that "ChatGPT is a valuable supplementary tool in mathematics education provided that it is used responsibly and in balance with other learning resources." This follows their finding that pre-service teachers held positive perceptions while simultaneously identifying concerns about accuracy and academic integrity.

Risk of bias

Selection bias: Participants were secondary mathematics pre-service teachers, potentially self-selected; Confounding variables: No control group mentioned; Interview bias: Small qualitative sample (n=20) may not be representative; Social desirability bias: Teachers may provide favorable responses about technology adoption; Selection bias: Self-selected participants (secondary mathematics pre-service teachers); Potential social desirability bias in interviews; Limited sample size for interviews (n=20); Unequal group sizes between quantitative and qualitative components; Self-selection bias in interview participants (20 of 91 survey respondents); Potentially positive bias given pre-service teachers' likely interest in educational technology; No control group for comparison

Data: not_statedCode: not_statedExtracted from: pdfAgreement 65%

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