Agentic education · Learning science

Actionable companion · Retention & transfer

Learning with agency: Advancing clinical integration in undergraduate dental education through AI-enabled instructional design: a qualitative thematic analysis

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 Background Despite growing interest in artificial intelligence (AI) in health professions education, qualitative evidence for integarting AI-supported instructional redesign on teaching practices and students' experiences in undergraduate dental education remains limited. The aim of this study was to explore the educational impact of AI-supported instructional redesign of lessons in undergraduate dental education. Methods We conducted this inductive qualitative thematic analysis in an undergraduate dental education setting. Existing lessons were redesigned using AI-supported recommendations to strengthen alignment between learning objectives, authentic clinical contexts, and expected clinical outcomes. Data were collected through classroom observations and students’ focus group discussions. An inductive thematic analysis was performed, and findings from multiple data sources were curated, collecetd, and triangulated to enhance credibility. Results AI-supported instructional redesign transformed teaching by promoting authentic clinical scenarios, clinically oriented discussions, and explicit reasoning activities. Thematic analysis identified four interconnected themes: (1) a shift from teacher-centred knowledge delivery to facilitation of clinical reasoning; (2) transformation from passive attendance to active cognitive engagement; (3) building clinical meaning through authentic clinical contexts and collaborative dialogue; and (4) creating conditions for professional learning through psychological safety. AI functioned as a pedagogical design-support tool rather than replacing the educators’ instructional role. Overall, the observed educational changes appeared to arise primarily from the pedagogical redesign enabled by AI-supported planning rather than from students' direct interaction with AI. Conclusion This study endorses the role of AI-supported instructional redesign in enriching the existing undergraduate dental curriculum. This approach can potentially help educators align learning objectives, instructional strategies, and authentic clinical contexts. The reported educational modifications reflect primarily the implementation of learner-centred and clinically oriented pedagogical strategies, with AI functioning as a design-support mechanism rather than as a direct instructional agent. Article highlights AI supported educators in redesigning clinically oriented teaching. AI improved alignment between learning objectives and clinical outcomes. AI-supported teaching promoted active clinical reasoning. Authentic clinical contexts enhanced meaningful learning and knowledge transfer. AI-supported instructional redesign was feasible and educationally valuable.

What kind of evidence is this?

Supporting AI learning science · 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 employed a qualitative descriptive case study design to explore the feasibility and perceived value of integrating a AI-supported instructional approach into existing undergraduate dental lessons Guided by an interpretivist paradigm, the study sought to understand how educators and students experienced the enhancement of preclinical teaching through clinically oriented instructional design REF. We adopted a qualitative case study as this approach enables an in-depth exploration of complex educational phenomena within their real-world context while incorporating multiple sources of evidence [16,17].

Limitations and uncertainty · source passage

This study has several limitations. First, it was conducted at a single institution and involved two fourth-year preclinical dental lessons delivered by two faculty members, which may limit the transferability of the findings to other educational contexts. Second, although students provided rich insights into their learning experiences, the study did not include discussions with the participating faculty members. Consequently, educators' perspectives on the feasibility, usability, and instructional value of the AI-supported redesign process were not explored.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
  • 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

  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

Selçuk Akturan; Ömer Faruk Güdük; İpek Nur Berberoğlu; Elif Özlem Ardıç Kemerkaya; Salman Yousuf Guraya. Advancing clinical integration in undergraduate dental education through AI-enabled instructional design: a qualitative thematic analysis. Medical Education Online (2026-10-19). https://doi.org/10.1080/10872981.2026.2750709

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.