Generative AI Effects on Academic Achievement and Sustainable Professional Development: A Quasi-Experimental Comparative Study in Chemistry and Mathematics

Najwa Abdulmunem Jasim Alnoori; Wasan Qasim Neamah; Ban Hassan Majeed; Wisal Hashim Abdulsalam; Zainab Aziz Ahmed Alamiry; Ilham Jabbar Faris

doi:10.3991/ijep.v16i3.61055

Authors

Najwa Abdulmunem Jasim Alnoori University of Baghdad, College of Education for Pure Science (Ibn Al-Haitham), Baghdad, Iraq
Wasan Qasim Neamah University of Baghdad, College of Education for Pure Science (Ibn Al-Haitham), Baghdad, Iraq
Ban Hassan Majeed University of Baghdad, College of Education for Pure Science (Ibn Al-Haitham), Baghdad, Iraq
Wisal Hashim Abdulsalam University of Baghdad, College of Education for Pure Science (Ibn Al-Haitham), Baghdad, Iraq https://orcid.org/0000-0003-0453-095X
Zainab Aziz Ahmed Alamiry University of Baghdad, College of Education for Pure Science (Ibn Al-Haitham), Baghdad, Iraq
Ilham Jabbar Faris University of Baghdad, College of Education for Pure Science (Ibn Al-Haitham), Baghdad, Iraq

DOI:

https://doi.org/10.3991/ijep.v16i3.61055

Keywords:

Keywords: Academic achievement, sustainable professional development, GenAI, AI-enhanced education, integrated learning strategies, comparative study, science education, chemistry, applied mathematics.

Abstract

This quasi-experimental study investigated generative AI (GenAI) tools—Copilot for chemistry and GitHub Copilot for mathematics—on academic achievement and sustainable professional development among 160 undergraduates (40 experimental/control per department) at the University of Baghdad’s Ibn Al-Haitham College of Education for Pure Sciences (2024–2025). Non-random assignment controlled for covariates. Pre/post validated tests (α ≥ .85; 15 MCQ + 5 essay items) measured outcomes. ANOVA revealed significant gains for experimental groups (p < .001, η2 = .41, Cohen’s d = 0.72 [95% CI: 0.45–0.98]). Chemistry excelled in affective domains; mathematics in cognitive/skills. Findings affirm GenAI’s domain-specific efficacy, providing datasets for AI-STEM pedagogy.

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