Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Harnessing Artificial Intelligence to Deliver Growth Mindset Education in a Pre-matriculation Curriculum for Incoming Medical Students

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:

Background: Growth mindset, the belief that intelligence and abilities can be developed through effort and strategy, underpins resilience and adaptive learning in the demanding context of medical education. Yet the intensity of medical curricula leaves little room for supplemental content, even when it addresses essential skills like resilience and adaptability. Emerging tools such as large language models (LLMs) now enable faculty to efficiently generate accessible, learner-centered educational materials that support scalable delivery of growth mindset content. Objective: The objective of this study is to test the feasibility, engagement, and impact of “Mindset Mondays,” a 10-week email and podcast curriculum for incoming medical students. Methods: Medical students at the University of Arizona College of Medicine - Tucson received weekly emails during the pre-matriculation period prior to the start of formal medical school. These emails featured thematic summaries of growth mindset concepts, key takeaways, quotes, and reflection prompts. Extended PDF summaries and 10-15 minute podcasts supplemented each email. Surveys assessed demographics, engagement, and retrospective pre/post growth mindset beliefs via Likert-based surveys. Results: Of 94 respondents, 76.6% were first-generation students and 51.1% reported prior growth mindset exposure. Students engaged with an average of 4.05 emails (SD = 3.28), 2.60 PDFs (SD = 2.89), and 1.03 podcasts (SD = 2.19). Composite growth mindset scores improved significantly (Pre: M = 4.92, SD = 0.61; Post: M = 5.17, SD = 0.60; p < 0.001; Cohen’s dz = 0.71). Seven out of eight Likert items showed significant improvement (p < 0.05; effect sizes 0.32-0.46). Greater engagement was associated with greater improvement across all formats (r = .339-.399, p < .001). Conclusion: Delivering growth mindset education through LLM-assisted emails and podcasts was feasible, scalable, and associated with small but significant self-reported growth mindset gains. Early, structured exposure to growth mindset principles may strengthen resilience, promote receptivity to feedback, and better prepare students for the demands of medical school.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

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

This study used a single-group, cross-sectional design with retrospective pre/post self-assessment to evaluate feasibility, engagement, and perceived changes in growth mindset beliefs. This study was reviewed by the University of Arizona Institutional Review Board (Submission ID: STUDY00006844) and approved as a Quality Improvement (QI) study, which does not constitute human subjects research.

Limitations and uncertainty · source passage

This study has several limitations. It was conducted at a single institution using a single-group, cross-sectional design with retrospective pre/post self-assessment, which limits causal inference and definitive attribution of observed changes to the intervention. The retrospective format is susceptible to recall and response-shift bias, and outcomes were self-reported rather than objective. Long-term academic or professional outcomes were not assessed. Baseline growth mindset scores were high, introducing potential ceiling effects that constrained the magnitude of observable change. Because the survey was study-developed and AI-assisted content was generated iteratively rather than via fixed prompts, exact replication of the intervention materials is limited. Future prospective, controlled, and multi-institutional studies are needed to more rigorously evaluate the durability and causal impact of pre-matriculation growth mindset interventions.

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.
  • 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.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

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

Troy Weinstein; Claire Pishko; Derek Honeyman; Claudio Cheuquen; Katie Kowalek; Daniel C Butler; Elaine Situ-LaCasse; Richard Amini. Harnessing Artificial Intelligence to Deliver Growth Mindset Education in a Pre-matriculation Curriculum for Incoming Medical Students. Cureus (2026-03-28). https://doi.org/10.7759/cureus.106023

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.