Robotics and automation significantly advance the life sciences, e.g., via academic, industrial, and pharmaceutical liquid-handling robots [1,2] and open source approaches [3]. Consequently, formal and informal education must convey these concepts. The Next Generation Science Standards (NGSS) and other national initiatives promote cross-disciplinary approaches for science, technology, engineering, and math (STEM) learning [4,5]. Many engaging and successful educational approaches to robotics exist, such as Lego Mindstorms or the FIRST Robotics Competition [6–11]. Naturally, these activities foremost focus on mechanical engineering, computer programming, and soft skills like teamwork. To a lesser extent, they are used to support experiments in Natural Science and Math education [12]. Crucially, integration of equivalent robotics approaches with the life sciences and chemistry for K–12 and college education are lacking, hence we expect significant value in bridging this gap.

Here, we designed simple yet powerful liquid-handling modules that can be integrated into various Lego Mindstorms robots (Fig 1), enabling a variety of engaging and educational life science experiments (Fig 2) (for building and experiment instructions see S1 Text, S1–S3 Movies). The most basic pipetting robot (Fig 1A) is built solely from parts in the standard Educational EV3 Lego kit (45544) (one robot per kit) and less than US$5 in plasticware, enabling easy reproduction. Up to 20 cuvettes are reversibly fixed with double-sided tape onto a motorized 1-D trolley (Fig 1A and 1B). Liquids are delivered and removed via a standard 1-ml plastic syringe with a pipette tip (Fig 1C), which is Lego compatible with minor modifications (Fig 1D). A motorized crankshaft drives the syringe plunger (Fig 1E), which can hold and deliver approximately 720 μl of liquid at a time. More complex calibration and delivery procedures achieve volumes down to 7 μl (20% precision, 30% accuracy, S1_2.2 Text). This liquid-handling module is inspired by professional pipettors (Fig 1E inset). This module can be operated by hand (Fig 3A) or incorporated into the robot (Fig 1A), in which another crankshaft motor lifts and lowers this module relative to the cuvettes. More advanced robot and pipette designs (Fig 1F, S1_3 Text) [13] enable even better liquid handling and 2-D operation with multiple 6-, 24-, or 96-well plates standard plasticware. Here, the syringe is driven by a linear rail system (Fig 1G). This enables convenient delivery of droplets of various sizes down to 2.5 μl (25% precision, 8% accuracy) using a 1-ml syringe and down to 0.15 μl (15% precision, 8% accuracy) using a 25-μl syringe, which is better than what we could obtain with a professional P2 pipette (Fig 1H and 1I, S1_3.3.3 Text). In general, the quality of liquid handling depends on many factors, including fluid characteristics, piston diameter, size of the outlet hole, tip coating, and the impulse of piston advancement [14]. Using sensors for homing enables positioning of the pipette tip with spatial precision of ±2 mm. These robots went through multiple design iterations, are mechanically stable over at least 1,000 pipetting cycles, and are controllable in real time by pressing buttons (Fig 1A) or by preprogrammed routines.

We developed a set of basic experiments and activities for the 1-D robot that cover a wide variety of science experiments and topics with standard, low-cost plasticware and common household or school consumables, ensuring accessibility and safe use (Fig 2). Mixing of colored liquids from two reservoirs into a third reservoir demonstrates basic liquid handling (Fig 2A). Serial dilutions illustrate the concept of concentration (Fig 2B). Aligning the cuvette with the Lego color sensor on the robot (Fig 1B) showcases the functionality of spectrophotometers (Fig 2C). Colored liquids with distinct salt densities can be sequentially layered in a single cuvette to model buoyancy (Fig 2D). Cabbage juice constitutes a pH indicator for various liquids, e.g., water, lemon juice, and bleach (Fig 2E). Sterilization is achieved by uptake and release of 80% alcohol or 10% bleach multiple times; in contrast, cross-contamination occurs without sterilization (Fig 2F). Adding baker’s yeast to serially diluted sugar solutions leads to foam formation due to CO₂ production and demonstrates optimal growth conditions at intermediate nutrient concentration (Fig 2G). Automated dilution series enable complex mixing and dilution series in cuvettes (Fig 2H) or multiwell plates (Fig 2I). Similar experiments are currently used in various school settings using Pasteur pipettes by hand.

In order to assess the potential for education and wider dissemination, we carried out two user studies (Fig 3A, S1_6 Text) focusing on the 1-D robot (Fig 1A). Specifically, we investigated whether these activities could successfully integrate robot building, programming, and wet-science experiments in an engaging way, whether elementary and middle school students could successfully complete these activities, and what the required time frame would be. Among the available science activities (Fig 2), we focused on liquid mixing, dilution series, and liquid density (Fig 2A, 2B and 2D), which aligns well with middle school learning content [5]. The robotic activities focused on building and programming, as is common in afterschool settings [11]. Given the unknowns associated with a first deployment, we significantly guided all activities using worksheets. We designed a curriculum (Fig 3B) that progresses from classic experiments with Pasteur pipettes, to real-time control of the pipettor module held by hand while pushing buttons (Fig 3A), to real-time control of the robot while pushing buttons (Fig 1A), all the way to programming the robot. Partial repetition of science experiments between sessions intended to deepen the concepts and to emphasize the differences between humans and liquid-handling robots, e.g., higher precision (session 2 versus session 4) and automation of repetitive tasks (session 2 versus session 5). Assessment was based on observed student activities as well as evaluation of worksheets, posttests, questionnaires, and self-reported learning.

Eight elementary school students (seven used for the study according to institutional review board (IRB) guidelines; 10–11 years old; all Girl Scouts, 4/7 had previous Lego Mindstorms experience) worked with the robots in groups of two over five 90-minute afterschool sessions (Fig 3B, S1_6.2 and S1_7 Text). In session 1, two instructors demonstrated a fully automated dilution series (Fig 2B) and explained the general importance of liquid handling and robotics for the life sciences. The participants manually handled and mixed water with food coloring (Fig 2A) using a plastic Pasteur pipette, then built their own robot pipette module (Fig 1E). In session 2, the participants used their module to carry out the color-mixing (Fig 2A), five-step serial dilution (Fig 2B), and salt solution density layering (Fig 2D) experiments. One partner always held and aligned the pipette with the cuvettes, while the other operated the two push buttons on the Lego brick to manipulate the liquids (Fig 3A). During session 3, each group built one or two of the five main structural robot modules (Fig 1A, S1_2.1 Text); the instructors helped assemble all modules to demonstrate one complete robot. For session 4, each group was provided with a completed robot. Groups then repeated all experiments from session 2 by manually controlling all motors via the buttons on the Lego brick (Fig 1A). During session 5, the participants programmed the robot to perform simple and complex dilution series, for example, through programs with inner and outer loop skipping of every second or third cuvette to generate patterns (Fig 2H). Here, participants also had to measure and convert degrees of motor rotation into linear distance between cuvettes. The course concluded with a postactivity quiz.

Participant feedback and our observations attest to the utility of these activities (S1_6.2 Text). Two instructors for eight participants working in pairs were able to provide sufficient guidance, to correct mistakes, and to discuss content in more depth. Participants were motivated and enjoyed these activities, e.g., they frequently repeated experiments with other (favorite) colors instead of moving directly on to the next experiment, they integrated sounds into their code on their own initiative, and multiple participants used the Pasteur and the Robot pipette to make colorful patterns on tissues that had been provided to clean up spills (Fig 3C). After the course, the participants rated the difficulty of each activity on a 1–5 Likert scale (very easy, easy, medium, hard, very hard): programming (2.8 ± 1.5) (always mean ± standard deviation [stdev]), building the robots (2.5 ± 0.8), density layering (2.5 ± 1.4), dilution series (1.8 ± 0.4), and color mixing (1.3 ± 0.5). Completed worksheets and quizzes demonstrated reasonable associations about why scientists pipette fluids (“liquids don’t need to be touched”), what robots are good for (“more controlled”), and the disadvantages of robots (“can have glitches”). The overall rating of the course on a scale of 1–5 (very bad–brilliant) was 4.2 ± 1.0. When asked what they had learned, all participants pointed to robots and programming; when asked what they liked, they pointed to both robots and the liquid experiments. We also tested whether density layering experiments would increase conceptual understanding of why objects float or sink, and of the three participants who had answered the prequestion incorrectly, two gave the correct answer afterwards.

A second user study included nine middle school students (aged 11–13 years; both genders, 8/9 had previous experience with Lego Mindstorms, S1_6.3 Text) working in groups of two or three over 16 afterschool sessions (~30 hours total). This course was taught by a single middle school teacher; it followed a similar layout as before (Fig 3B), but participants worked under less supervision, all groups built their own robot, participants had more time for self-motivated side projects including individual changes to the robot design, and the teacher inserted several lectures about liquids, densities, and dilution factors (S1_6.3 Text).

Outcomes were generally consistent with the first study. This course was rated 4.2 ± 0.4 on a scale of 1–5 (strongly unfavorable to strongly favorable), and all activities were reported as having medium difficulty (2.1 ± 0.9 to 3.3 ± 0.7) on a scale of 1–5 (very easy–very hard). All students self-reported that they learned something new—7/9 mentioned wet-work (“I learned about salt density” or “I learned about serial dilution”). When asked specifically what they learned about liquid solutions, 5/9 pointed to the density concept (“liquids with higher salinity fall”), 2/9 to solutions in general (“a solution requires care to make”), and the remaining 2/9 answered “nothing” and “It's not easy.” When asked posttest what would happen if two liquid samples of different color and salt content were put over each other in either sequence, 9/9 provided the correct outcome, and 6/9 correctly referred to the different densities. Students also self-reported to have gained competency in building (7/9) and programming (8/9). These activities seemed to have broadened students’ perception of what robots can do, extending from classic engineering into science (“I learned that robotics…can be used in different ways”; “it is a lot more impressive than moving robots.”) When asked what they liked most, all activities were mentioned (e.g., “I liked programming our first experiment”; “I like the density layers sessions the best”; “I liked building and programming the robot”), suggesting that robotics and wet-experimentation can be bridged successfully.

The impact of these activities is extendable in various ways in the future. Students could have more freedom, e.g., develop their own robot and pipette designs. Over a summer, three high school students implemented different robots, including one with moving plates and a stationary pipettor (S1_5 and S1_6.1 Text). Here, we identified as a major design challenge to build a pipettor module that is high performance in liquid handling but is also small enough to be supported by a multiaxis gantry robot. Students could perform quantitative experiments, utilizing more complex control and data-logging capabilities. For example, we used the Lego light sensors to measure the concentrations in a dilution series (Figs 1B, 2B and 2C), but where the limitations of this sensor required careful alignment to obtain reproducible readings (S1_2.4.6 Text). The compatibility with standard plasticware (Fig 2A and 2I) and our washing and sterilization techniques (Fig 2F) might even be sufficient for certain research applications and citizen science [15]. Whether a Lego Mindstorms robot could pick up and release disposable pipette tips with sufficient reliability is doubtful but an open question. Extensions beyond Lego could target lower cost or higher precision; furthermore, remotely controlled labs could be supported [13,16].

In summary, our Lego-based liquid-handling robots combine with a versatile set of science experiments to safely and robustly meet important cross-disciplinary endpoints, integrating robotics, biology, chemistry, and hands-on learning. Our initial user studies point to the validity of our approach; future studies should focus on larger cohort sizes, including control groups, more teachers, and dissemination and utility beyond afterschool programs. The foundation for these robots, the EV3 kit (~US$380), is already available in many schools, while additional reagents are low cost (~US$5) and easily accessible. A minimalistic activity focusing on the hand-held robot pipette (Fig 1A) and simple mixing and density experiments (Fig 3B; sessions 1 and 2) requires even less Lego parts, aligns with the NGSS sixth and eighth grade [5], and could be done within 2–3 hours. These activities may also help to extend Seymour Papert’s Mindstorms vison [6] to the life sciences and chemistry. We invite other stakeholders such as teachers, students, DIY learners, and educational and life science researchers to use, disseminate, and further develop these robots via open-source instructions and protocols.