Introducing Preceptor-Guided AI Case Studies

A competency gap should lead to more than a recommendation. It should start a remediation loop: identify what a learner needs to improve, give them focused practice, review their progress, and determine whether the gap has been addressed.

HealthTasks Intelligence connects those steps through Personalized Learning Plans (PLPs) and HealthTasks Assessments. When a PLP identifies a competency gap, the learner can work on it through two complementary assessment modalities: a CJMM-aligned quiz or a Preceptor-Guided AI Case Study. HealthTasks Assessments introduced the quiz modality; case studies add an interactive way to apply clinical reasoning.

From identified gap to targeted practice

Each assessment is connected to the learner's PLP, so remediation focuses on the competencies they need to develop. CJMM-aligned quizzes offer structured knowledge checks. Preceptor-Guided Case Studies provide an interactive way to apply clinical reasoning in a realistic, fictional scenario.

In a case study, the learner reviews a mock chart, responds to questions, and receives guidance and feedback as the scenario unfolds. The case is tailored to the learner's healthcare discipline and PLP, with focused objectives to guide clinical reasoning and next steps. A SOAP-format record develops alongside the conversation, helping learners connect their decisions to the patient's changing situation.

The AI provides guidance and feedback, while educators retain oversight of student progress. After the encounter, educators can review the case record, objective-level results, and debrief.

Performance informs what happens next

Completing an assessment is not the endpoint. Student performance and progress are recorded alongside the competency requirements in the plan. Learners and educators can review that evidence over time and assess whether the gap has been resolved or whether more practice is needed.

If sufficient improvement is demonstrated, the gap can be closed. If it remains, the learner can continue remediation through another targeted assessment. That progress also informs subsequent PLP evaluations, connecting earlier evidence of need with new evidence from practice.

This is the remediation loop: HealthTasks identifies a competency gap, connects it to a relevant learning experience, records how the learner performs, and uses that progress to guide the next evaluation and next steps.

One loop, two assessment modalities

CJMM-aligned quizzes and Preceptor-Guided Case Studies support different kinds of practice within the same remediation process. Quizzes provide structured knowledge checks. Case studies let learners reason through a clinical scenario in conversation with an AI preceptor.

Because both are connected to the learner's PLP and competency record, educators can see more than whether an activity was completed. They can review progress against the gap that prompted the remediation and determine whether it remains open.

The case studies also extend HealthTasks' agentic capabilities directly into the student learning experience. Powered by OpenAI's Codex harness, the AI preceptor guides the interaction while HealthTasks connects the experience to the learner's goals and competency record.

A continuous view of learner and program progress

For students, the loop turns a learning need into focused practice and a visible path forward. For educators, it provides a way to assign targeted remediation and review evidence of progress. For institutional leaders, connected evidence across PLPs, assessments, clinical evaluations, skills checkoffs, and other educational activities supports longitudinal competency tracking and continuous quality improvement.

Preceptor-Guided AI Case Studies add a new way to practice within HealthTasks Assessments. Their value is not just in generating a case; it is in connecting the case to an identified competency gap, recording the learner's performance, and bringing that evidence back into the ongoing remediation process.