Actionable companion · Cognitive load & attention
Learning with agency: A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment
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.
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): Age / stage not established. Health-professions learning must be distinguished from K–12 instruction.
Original authors’ abstract, reproduced under the linked source license:
Abstract We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom’s taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients’ attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients’ understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners. Keywords: patient education, educational learning, VARK, Bloom’s taxonomy, personalization, ontology, procedural reasoning system, precompiled planning
What kind of evidence is this?
Intelligent tutoring & adaptive learning · Qualitative / mixed methods
Stage: Age / stage not established. 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
Our proposed framework represents a new eLearning tool to enhance patient knowledge about and adherence to treatment. Using several representative use cases, we conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the tool among the target patient population and medical practitioners. The framework is inspired by ITS and uses an ontology (eLearning ontology) to express domain (condition and treatment) and learner (patient) models. It personalizes educational materials to the patient’s characteristics, teaches patients how to manage their treatment within daily life constraints, and provides a means to independently solve issues such as missed medication doses. It creates a personalized course composed of the lessons that cover all the knowledge associated with a patient’s condition and treatment options, customized to a patient’s skills (i.e., learning capabilities), learning styles, and their progress.
Limitations and uncertainty · source passage
To address the above limitations, our future work will focus on several key features. First, we will allow patients to select the starting point within a course and provide them with the ability to start learning about a specific concept within a lesson. Second, we plan to develop rewind and fast-forward functionality to support an additional layer of learning and customizing lessons to patients’ learning styles. Third, we will update the model of a BUK to be a template so their textual content can be customized to the patient as the course is being assembled. Fourth, we will learn a patient’s preferred VARK presentation mode through their interactions with BUK presentation modalities and consider using one of the measures provided by the Health Literacy Toolshed (https://healthliteracy.bu.edu/, accessed on 7 July 2021) to establish a patient’s comprehension level. Finally, we will design and implement a pilot study for multiple disease conditions and involving actual patients. We will test patients’ ability to retain knowledge about their treatment and their adherence to this treatment using the mobile application at home for a longer period. We will then evaluate the uptake of our application at different time points during the study.
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.
- Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
- Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
A 90-day district pilot
- 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.
- Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- 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.
- Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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
Martin Michalowski; Szymon Wilk; Wojtek Michalowski; Dympna O’Sullivan; Silvia Bonaccio; Enea Parimbelli; Marc Carrier; Grégoire Le Gal; Stephen Kingwell; Mor Peleg. A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment. International Journal of Environmental Research and Public Health (2021-07-09). https://doi.org/10.3390/ijerph18147355
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.