Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Development of a course based on BEAM robots to enhance STEM learning in electrical, electronic, and mechanical domains

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Secondary school. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract In this work, BEAM robotics is proposed to enhance the STEM knowledge and skills of engineering students in the electrical, electronic, and mechanical domains. To evaluate the proposal, a course is designed and implemented based on a curriculum with objectives and learning activities centered on the design, construction, and operation of the BEAM robots. In addition, the connection between this proposal and computational thinking is explored. Students learn to recognize each part of robots and how they are related, abstract useful information from an electronic scheme and concretize it in a machine by systematizing their behavior. In addition, thanks to an evaluation of the behavior of the robot, identify the faults and apply the solution, as in the debugging process carried out in software programming. It should be added that BEAM robotics has a sustainable and low-cost aspect, which is used in learning activities where Waste Electrical and Electronic Equipment (WEEE) is recycled, and students are taught to value and integrate these parts into the design of the robots. A pre and post survey and a respective statistical analysis and evaluation of curricular activities are presented as evidence of the improvement observed in students’ STEM knowledge and skills. In general, the results show that this new teaching tool can promote the STEM curriculum in engineering students and motivate implementation, as a new educational robot, at other academic levels such as secondary and pre-secondary education. Keywords: STEM, Educational robotics, BEAM, WEEE

What kind of evidence is this?

Embodied learning agents · Survey / observational study

Stage: Secondary school. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Design the system before the pilot

Compare the embodied interaction with an equivalent human-led or screen-based activity. Separate the effects of novelty and social presence from learning. Review physical access, sensory needs and adult supervision.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Carlos Boya-Lara; Doris Saavedra; Aaron Fehrenbach; Angel Marquez-Araque. Development of a course based on BEAM robots to enhance STEM learning in electrical, electronic, and mechanical domains. International Journal of Educational Technology in Higher Education (2022-02-03). https://doi.org/10.1186/s41239-021-00311-9

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.