Comparison of AI-Assisted Learning in a Collaborative Environment with Conventional Teaching Methods on Pre-service Teachers’ Mathematics Performance

Vivian Maanu (), Francis Ohene Boateng () and Ernest Larbi ()

International Journal of Scientific Research and Modern Technology, 2025, vol. 4, issue 9, 146-153

Abstract: Conventional teaching methods still dominate in Colleges of Education in Ghana, relying on rote learning rather than critical thinking or problem-solving. This study adopted a positivist research philosophy, employing scientific and quantitative methods to objectively explore the causal effect of AI-assisted instruction on pre-service teachers’ mathematics performance. Utilizing a quasi-experimental design with non-random assignment, two public Colleges of Education in Ghana's Ashanti Region were selected, involving 100 Level 400 students split evenly into experimental and control groups. Pre-tests established baseline performance, and the group with the lower average became the experimental group. The intervention consisted of eight AI-assisted mathematics lessons on linear equations delivered in a collaborative learning environment, while the control group received conventional instruction. Post-test results analyzed using SPSS and Independent Samples t- tests revealed a statistically significant improvement in the experimental group’s performance. These findings indicate that AI-assisted instruction in a collaborative setting significantly enhances mathematics achievement among pre-service teachers, highlighting the transformative potential of integrating technology in teacher education.

Keywords: Artificial Intelligence [AI]; AI-Assisted; Collaborative; Performance; Conventional (search for similar items in EconPapers)
Date: 2025
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