ASSESSMENT OF AWARENESS OF ARTIFICIAL INTELLIGENCE IN COLLEGES OF EDUCATION AND ITS EFFECT ON ACADEMIC ACHIEVEMENT OF BIOLOGY STUDENTS

Abstract

This study examines the awareness of artificial intelligence (AI) in colleges of
education and its impact on biology students' academic achievement. As educational
institutions continually seek to leverage technology for enhanced learning
outcomes, understanding the practical implications and benefits of AI is critical. The
study employed a type pretest, posttest and control group quasi experimental
research design. Surveys are conducted with educators and students to gather
insights on the integration and awareness of AI tools in the educational process. This
study tested three hypotheses. The study's population consisted of all college of
education biology students in Ogun State, Nigeria. Stratified random sampling was
used to select 311 students from two colleges of education in Ogun State, Nigeria who
participated in the study. A Biology Achievement Test (BAT) containing 20 items was
used to collect data for both pre-tests and post-tests, while a Questionnaire on the
Awareness and Integration of AI Tools in Teaching Biology in Colleges of Education
(QAIAITBCOE) was used for collecting data on students' awareness of AI tools.
Experts validated the instruments. A Pearson product-moment correlation and
Cronbach's alpha formula were used to determine the reliability coefficients of BAT
and QAIAITBCOE, which yielded 0.88 and 0.91, respectively. The data were
analyzed using descriptive statistics (frequency and percentage), inferential
statistics, an analysis of variance (ANOVA), and an analysis of covariance
(ANCOVA) test. The hypotheses were tested at the 0.05 level of significance. The
results of the study revealed that there was no significant difference in the
effectiveness, accessibility, and integration of AI tools in teaching biology in colleges of education (F (2, 307) = 69.330, p > 0.05) and there is no significant difference in
the academic achievement of biology students who were taught using AI tools
compared to those who are taught using the lecture method (F (2, 308) =0.499, p >
0.05). Furthermore, there was no significant difference between students' awareness
of awareness of AI tools and their academic achievement in biology in colleges of
education (F (2, 307) =5.004, p < 0.05). The results indicate a significant increase in
student engagement and academic performance when AI was integrated into the
curriculum. AI-driven personalized learning platforms allow for tailored
educational experiences, accommodating individual learning paces and styles. This
personalized approach helps in addressing specific learning gaps, thereby
improving overall academic achievement. Teachers also benefit from AI
technologies through automated grading systems and data analytics, which reduce
administrative burdens and provide deeper insights into student performance trends.
Further research is recommended to explore the long-term effects and develop best
practices for AI integration in diverse educational contexts.