2026 ASEE Annual Conference & Exposition

Laboratory automation is transforming modern science and engineering education by enhancing efficiency, precision, and reproducibility in experimental workflows. However, most bioengineering and biochemistry curricula lack exposure to the automated liquid-handling systems now prevalent in research and industry. To bridge this educational gap, this work presents a hands-on, interdisciplinary laboratory curriculum designed to teach engineering students the principles of automation, programming, and biochemical applications through the integration of computer vision and an open-source Opentrons OT-One pipetting robot. The curriculum consists of seven modular laboratory exercises that progressively build student competency in both hardware and software integration. The sequence culminates in a final exercise where students combine robot control, biomedical pipetting procedures, and visual feedback, to execute autonomous liquid transfer between well plates. Students begin by assembling and calibrating the OT-One pipetting robot, learning the fundamentals of its 3-axis gantry control and motor operation. Subsequent modules guide students through Python-based programming using the Opentrons API to automate pipette movement and liquid transfer procedures. The final modules introduce computer vision concepts using an Intel RealSense D435i depth camera, where students apply OpenCV in Python to process color and depth images for object and well-plate detection. Through these exercises, students gain practical experience in robotic control, computer vision processing, and automated liquid handling, skills that directly align with contemporary laboratory automation practices. Three-member student teams were assigned to conduct both laboratory modules and perform a four-week project with pre- and post-laboratory surveys assessing their knowledge of automation, programming, and biochemistry. Results from the pilot implementation show measurable gains in automation comprehension (average increase of 29%) and biochemistry understanding (average increase of 60%), supported by strong ratings for effectiveness, interactivity, and overall user experience. Students particularly valued the hands-on assembly and programming of the automation machine, finding these activities most engaging and directly applicable to real-world laboratory automation. This proposed curriculum has been demonstrated to show that open-source robotic platforms can be effectively leveraged to create cost-efficient, scalable, and pedagogically robust laboratory experiences that bridge engineering and biochemical disciplines. By combining experiential learning with modern automation technologies, the proposed curriculum prepares students for emerging fields such as biofabrication, pharmaceutical automation, and precision laboratory systems, fostering a deeper understanding of how cross-disciplinary engineering principles translate to life science innovation.