The rapid expansion of artificial intelligence offers new opportunities to transform undergraduate research by shifting classrooms from passive learning spaces to dynamic, inquiry-driven environments. This study presents a course-based undergraduate research experience (CURE) model that integrates AI-enhanced educational technologies to strengthen research capacity, critical thinking, and problem-solving skills among engineering undergraduates. The initiative was implemented in multiple classroom settings through intelligent systems that supported automated feedback, multimodal content generation, personalized learning pathways, and real-time conceptual reinforcement. Undergraduate students engaged in micro-level research cycles—problem identification, hypothesis formation, dataset exploration, model selection, and interpretation—using accessible AI tools and cloud-based platforms. The pedagogy emphasized low-barrier, high-impact research practices such as prompt engineering, sentiment analysis, basic machine-learning classification, and AI-supported data visualization, enabling students to produce measurable research outputs within a single academic term. Findings indicate a substantial improvement in research confidence, analytical depth, and willingness to participate in long-term academic projects. Students reported higher engagement, enhanced understanding of research methodology, and increased ability to independently design and interpret experiments. The model demonstrates that AI-driven classroom innovation can democratize research participation, expand faculty bandwidth, and empower large cohorts of undergraduate learners to contribute meaningfully to institutional research productivity. This work provides an adaptable blueprint for higher education institutions seeking to scale undergraduate research through AI-supported inquiry, while ensuring alignment with CURE principles of collaboration, authenticity, and real-world impact.