Actionable companion · Retention & transfer
Learning with agency: A Faculty-Constructed AI Tutor for Personalized Learning and Remediation in a U.S. PharmD Immunology Course: An “In-House” Evaluation of New Learning Technology
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): Higher education. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
While generative AI becomes increasingly available in higher education, faculties find it challenging to design, implement, and evaluate AI-enabled personalized learning systems within accreditation-constrained professional curricula. This method paper describes ADAPT (Assessment-Driven AI for Personalized Tutoring), a home-grown AI tutoring and remediation ecosystem implemented in a required PharmD immunology course. Using standard learning management (Canvas) and assessment (ExamSoft) platforms, a 20-item quiz mapped to six immunology mastery domains (N = 34; mean 69.1%, SD 17.9; Cronbach’s α = 0.73) was used to trigger tiered, structured generative AI remediation at both individual student and cohort levels. Instructional impact was evaluated using reliability indices, item-level difficulty analyses, and paired pre/post-assessment comparisons. Following AI-guided remediation, mean performance increased to 79.8% (+10.7 percentage points), variability decreased (SD 14.4), and assessment reliability improved (ExamSoft KR-20 0.87) compared with the diagnostic exam, the first midterm exam, and the final exam, respectively. Item difficulty stabilized (mean ≈ 0.80), with sustained retention of targeted concepts on the final examination. ADAPT provides a replicable, low-cost methodological blueprint for faculties to independently construct assessment-driven AI tutoring systems and lays the foundational steps for future AI-based predictive analysis workflow for at-risk students.
What kind of evidence is this?
Supporting AI learning science · Design / feasibility 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
This study was conducted in the fall of 2025 in a required immunology course within the ACPE-accredited four-year Doctor of Pharmacy (PharmD) program at California Northstate University College of Pharmacy. The 3-credit-hour course enrolls a diverse cohort of professional students (N = 39), including a substantial proportion of first-generation, multilingual, and international students. Instruction is delivered using a combination of didactic lectures, active-learning exercises, and technology-supported formative assessments delivered through the Canvas learning management system and ExamSoft assessment platform.
Limitations and uncertainty · source passage
Several limitations are inherent to the methodological scope of this work. The study was conducted within a single course at one institution, without randomization or control conditions. These limitations are consistent with the paper’s intent as a methods demonstration, rather than an outcomes trial. As an evaluation, it is a before-and-after study, and therefore, it is not appropriate to assume causality, as it is indeterminate what would have happened if AI had not been introduced. It is possible that other external factors, not captured within this evaluation, may explain the differences seen. Like all LLM-based tools, AI tutors carry risks of probabilistic errors and output variability; therefore, ADAPT was deliberately implemented as a faculty-supervised, assessment-anchored support system, with instructional authority and evaluative responsibility remaining solely with the instructor.
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.
- 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
Ashim Malhotra. A Faculty-Constructed AI Tutor for Personalized Learning and Remediation in a U.S. PharmD Immunology Course: An “In-House” Evaluation of New Learning Technology. Pharmacy (2026-04-03). https://doi.org/10.3390/pharmacy14020059
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.