Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics

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): Higher education. Verify the participant population and task before transferring the findings.

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

Background The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. Results A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. Conclusions The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Design / feasibility study

Stage: Higher education. 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.

Study methods · source passage

Dragoon has been evaluated in several studies, but not yet as part of the ElectronixTutor. In a college class on system dynamics modeling (focused on teaching skill in model construction), one half of the students were randomly assigned to Dragoon and the other half used an ordinary system dynamics editor to complete their homework. Of the students who completed their homework, those who used Dragoon scored significantly higher on the post-test than those in the control condition (VanLehn et al. 2016a, 2016b).

Limitations and uncertainty · source passage

The integration of the learning resources and Recommender System experienced some changes throughout the project when we leveraged an ongoing companion project between USC, ASU, and Memphis called the Personal Assistant for Life-Long Learning: PAL3 (Swartout et al. 2016). The design process for the ElectronixTutor architecture required generalizable solutions for integrating multiple pre-existing ITSs and conventional learning resources into a coherent user experience. In particular, this effort integrated learning resources from AutoTutor, Dragoon, LearnForm, BEETLE, ASSISTments, Point & Query, readings, and videos. Performance needed to be updated in the Student Model and associated Learning Record Store. Recommendations on the next learning resource and topic to cover needed to be addressed, based on local and global recommendations by ElectronixTutor. Integrating multiple asynchronous intelligent systems presented serious challenges: (1) the system control is distributed rather than governed by a single ITS, (2) different learning resources provide and require different kinds of information, and (3) data from different learning resources need to influence actionable representations for a Student Model and Recommender System.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

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. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. 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. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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

Arthur C. Graesser; Xiangen Hu; Benjamin D. Nye; Kurt VanLehn; Rohit Kumar; Cristina Heffernan; Neil Heffernan; Beverly Woolf; Andrew M. Olney; Vasile Rus; Frank Andrasik; Philip Pavlik; Zhiqiang Cai; Jon Wetzel; Brent Morgan; Andrew J. Hampton; Anne M. Lippert; Lijia Wang; Qinyu Cheng; Joseph E. Vinson; Craig N. Kelly; Cadarrius McGlown; Charvi A. Majmudar; Bashir Morshed; Whitney Baer. ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics. International Journal of Stem Education (2018-04-16). https://doi.org/10.1186/s40594-018-0110-y

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.