An Artificial Intelligence-Enhanced Phenomenon-Based Learning Approach for Interdisciplinary Understanding and Speaking Skills

Abstract

Teacher education programs are critical for creating a new generation of skilled English teachers in Thailand. The interdisciplinary learning process is an important measure for developing expertise in English, enhancing competency-based learning, and improving the quality of learning and teaching skills. This study aimed to 1) determine whether AI-enhanced phenomenon-based learning instruction facilitates the development of preservice teachers’ interdisciplinary understanding and English-speaking skills, and 2) compare interdisciplinary understanding and speaking skills development between the experimental group and the control group. Participants were monitored through subjective and speaking tests conducted before and after learning instruction. Descriptive statistics (mean scores, standard deviations, and percentage) and inferential statistics through multivariate analysis of variance (normality test, Levene’s test, Bartlett’s test, and Box’s test applied for assumption test and Wilks’ lambda applied for hypothesis testing) were employed to analyze quantitative data collected in this study. Relative gain scores were also analyzed. The findings demonstrated that 1) AI-enhanced phenomenon-based learning instruction improved the interdisciplinary understanding and speaking skills of preservice teachers, and 2) the experimental group participants gained higher interdisciplinary understanding and speaking skills than the control group participants, as indicated by the increase in the performance mean scores across the pretest and posttest.