Actionable companion · Feedback & scaffolding

Learning with agency: Changes to Master Adaptive Learning in the Pre-Clerkship Phase of Medical School

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.

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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. Health-professions learning must be distinguished from K–12 instruction.

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

Abstract Introduction Adaptive expertise or the ability to self-assess, create a learning plan, incorporate self-assessment and feedback, adjust practices with newly learned skills, and address gaps is a core skill in medicine. The Master Adaptive Learner model (MAL) describes a framework to develop adaptive expertise, but studies have not measured adaptive learning during medical school, which limits our ability to design curricular strategies. This study investigated changes in master adaptive learning during the pre-clerkship phase of medical school. Materials and Methods This study was a single-cohort within-subject design. Medical students were administered the MAL Instrument at matriculation, end of M1 year and end of M2 year. The MAL instrument included 16 items measured on a 5-point Likert agreement scale measuring 4 factors representing adaptive learning, resilience, curiosity, and motivation. Across and within-subjects ANOVA was used to detect changes in MAL. Results There were 174 respondents at matriculation, 81 at the end of M1, and 150 at the end of M2 survey (response rates respectively 94%,41%,89%). 65 students had matched data across all three time points. MAL mean scores increased during the M2 phase for MAL overall and each subfactor. Adaptive learning decreased during the M1 phase but resilience, motivation, and curiosity factors were stable. Discussion Medical students MAL scores increased in the M2 phase of the curriculum. The transition into medical school may challenge students’ ability to apply adaptive learning strategies, particularly as they continue developing the metacognitive skills needed to navigate a more complex learning environment. Keywords: Master adaptive learner, Preclerkship curriculum, Self-directed learning and assessment

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Survey / observational 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

We used a longitudinal study design to evaluate changes in MAL at three time points during the pre-clerkship phase of medical school. A single cohort of medical students were administered the Master Adaptive Learner Instrument [8] at matriculation in July 2023, at the end of the M1 year in May 2024, and at the end of their M2 year in March 2025. Most students had completed their United States Medical Licensing Exam Step 1 exam at the time of M2 survey administration. The study was determined exempt by the Institutional Review Board IRB #HM20014899.

Limitations and uncertainty · source passage

This study has several limitations. First, this was a single-institution study of one medical school’s preclinical curriculum, which limits generalizability to other institutions with different curricular structures (e.g., problem-based learning). The structured design of the preclinical curriculum may have influenced MAL scores differently than in schools where there may be more emphasis on independent student attainment of foundational knowledge. The study relied on self-reported measures of MAL behaviors that may not accurately reflect the students’ actual adaptive learning behaviors or could be impacted by external factors. Although survey response rates were high at matriculation and at the end of M2, the end-of-M1 response rate was substantially lower (41%), which may introduce nonresponse bias. The M1 survey was administered asynchronously which may explain the lower response rate compared to the M2 survey where students were provided dedicated time to complete. While the between-subject analysis suggested no systematic sampling bias, the possibility remains that students who completed all three surveys differed in meaningful ways (e.g., motivation, study strategies) from those who did not. Finally, the student period concluded with a unique curricular and assessment context. Students were preparing for Step 1 in a pass/fail assessment approach, which may have influenced their learning approaches or their perception of adaptive learning behaviors. The original MAL model theorized 4 batteries, but the factor analysis of the revised instrument found 3 distinct batteries from the items included. This may be due to insufficient items to represent the full model or that the mindset and motivation factors have overlap and are difficult to measure as unique contructs. Additional studies should incorporate complementary approaches such as qualitative analyses or performance assessments and extend across multiple institutions and medical student cohorts to provide a more comprehensive and generalizable understanding of MAL development over time.

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.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
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. 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.
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. 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

Luan Lawson; Lisa Domico; Nicole Deiorio; Cana Curtis; Sally A Santen; Adam Garber; Kacie Lord; Moshe Feldman. Changes to Master Adaptive Learning in the Pre-Clerkship Phase of Medical School. Medical Science Educator (2026-06-20). https://doi.org/10.1007/s40670-026-02794-5

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.