Modern chemical research increasingly relies on automation, high-throughput experimentation, and data-driven decision-making, yet undergraduate laboratory curricula rarely provide structured opportunities for students to develop these foundational competencies for AI-era chemical research. To bridge this gap, we developed an inquiry-based experiment in which first-year undergraduates assemble a low-cost, programmable automated liquid workstation and use it to explore 25 different synthesis conditions for a 3d–4f cluster, La3Ni6(IDA)6(OH)6(H2O)12(NO3)3·15H2O (where IDA = iminodiacetate). This process, which requires meticulous optimization of stoichiometry and pH, is traditionally time-consuming and prone to human error. In contrast, by engaging in the complete workflow of a modern intelligent laboratory, encompassing programming, hardware assembly, experimental design, and characterization, students in this experiment can rapidly explore 25 synthesis conditions. Consequently, most groups successfully obtained phase-pure cluster crystals and characterized them by using optical microscopy, single-crystal X-ray diffraction, and electrospray ionization mass spectrometry within a single laboratory session. A distinctive feature of the activity is the deliberate integration of code and hardware debugging as a learning outcome. This focus cultivates the diagnostic reasoning and system-level thinking essential for operating automated platforms and demonstrably enhances students’ programming self-efficacy and interdisciplinary problem-solving skills. Furthermore, this provides a tangible understanding of how automation transforms research. As one of the few reports in chemical education to systematically integrate AI-assisted cluster synthesis and characterization into a first-year laboratory, this work offers a timely and scalable model for modernizing introductory curricula.
Qi et al. (2026) studied this question.