Object detection based on point clouds has been widely used for autonomous driving, although how to improve its detection accuracy remains a significant challenge. Foreground points are more critical for 3D object detection than background points; however, most current detection frameworks cannot effectively preserve foreground points. Therefore, this work proposes a hybrid attention-based 3D object detection method with differential point clouds, which we name HA-RCNN. The method differentiates the foreground points from the background ones to preserve the critical information of foreground points. Extensive experiments conducted on the KITTI dataset show that the model outperforms the state-of-the-art methods, especially in recognizing large objects such as cars and cyclists.

Artificial intelligence (AI) integration in computing education is often evaluated as tool adoption, although course performance emerges from interdependencies among students, instructors, tasks, platforms, assessment, governance, and environmental constraints. This study conceptualized AI-enabled computing courses as socio-technical instructional systems and examined associations within a theoretically proposed readiness–quality–engagement–satisfaction pathway. The primary dataset comprised 122 responses from courses such as Algorithms and Data Structures, and Principles and Applications of Databases at a Chinese science and engineering university; 108 responses remained for inferential analysis after a conservative joint quality screen. A separate 57-response survey database (GaussDB) provided implementation-layer evidence. Factorability diagnostics, formative and reflective measurement assessment, partial least squares structural equation modeling (PLS-SEM), 5000-resample bootstrap indirect-effect analysis, sensitivity analysis, and correlation-based item-level importance–performance analysis were applied. Course readiness was associated with instructional system quality, which was associated with engagement. Quality was directly associated with satisfaction and indirectly associated through engagement, explaining 55.2% of satisfaction variance. Assessment innovation, AI/digital-skill weighting, teaching experience, case and courseware integration, resource renewal, and personalization were priorities; the GaussDB evidence highlighted connectivity and diagnostic usability. The results support a six-stage redesign loop centered on high-quality learning rather than maximum AI use and provide a reproducible baseline for longitudinal, multi-institutional evaluation.