Design and Practice of a BOPPPS-Based Teaching Model for Junior High School Artificial Intelligence Courses

Abstract

Artificial intelligence (AI) has been incorporated into China's compulsory education curriculum; however, junior high school AI classrooms frequently overemphasize operational demonstration at the expense of inquiry, thinking, and process-oriented evaluation. This study constructed a six-link, dual closed-loop teaching model based on the BOPPPS (Bridge-in, Objective, Pre-test, Participatory Learning, Post-test, Summary) framework and verified its effectiveness through a quasi-experiment. Two parallel seventh-grade classes (60 students) were taught for six weeks, with the experimental group receiving the BOPPPS-based model and the control group receiving conventional lecture-based instruction. Data collected from pre- and post-tests, a core literacy scale, questionnaires, and classroom observation were analyzed with SPSS. The results show no significant between-group difference at baseline, whereas after the intervention the experimental group significantly outperformed the control group in academic achievement (p < 0.01) and across all dimensions of core literacy. The findings indicate that the BOPPPS-based model effectively enhances learning outcomes, classroom engagement, and information technology core literacy in junior high school AI courses, providing a replicable paradigm for frontline teaching.