Abstract:

Flashcards have been a staple in educational methodologies, facilitating active recall, spaced repetition, and the testing effect, thus enhancing learning and retention. Despite their proven efficacy, a gap in literature and practice persists regarding the optimal utilization of pre-made flashcards within specific educational frameworks, particularly in medical education where the volume and complexity of material present unique challenges.

This case study aims to bridge this gap by exploring the implementation of an innovative approach where first-semester medical students leverage assistive intelligence (AI) to map pre-existing flashcards to their course learning objectives (LOs). This mapping ensures the delivery of timely and relevant flashcards, tailored to the current curriculum without the need for manual searching or tagging. The integration of these flashcards into a platform that links them with multiple-choice questions (MCQs) aligned to the same LOs aims to provide a comprehensive learning tool that enhances both recall and understanding.

Through a qualitative analysis involving surveys, interviews, and observational methods, this study examines the implementation of AI-mapped flashcards specific LOs by the date the LOs were taught, enabling just-in-time delivery of pertinent study material. The learning experience platform was further designed to correlate flashcards with corresponding MCQs, facilitating an integrated learning experience. Formative data from interactions with both flashcards and MCQs were automatically displayed for students to identify their lowest performing LOs, offering insights for targeted review and remediation.

Preliminary qualitative findings indicate that this approach not only streamlines the study process by making relevant materials more accessible, but also enhances student engagement through a more personalized learning experience. Observations and participant feedback indicate a perceived improvement in material relevance and improved efficiency to access relevant material. Furthermore, the system's ability to identify and address students' lowest performing LOs in real time is highlighted as a significant advancement over traditional study methods.

This case study illustrates AI's transformative potential in educational practices, especially in information-dense high stakes fields like medical education. Mapping pre-existing flashcards to specific LOs and integrating them with MCQs offers a tailored, efficient learning experience. These findings invite further empirical research and suggest a broader application of integrated, adaptive learning technologies in professional education.

Keywords:

Flashcards, Medical Education, Artificial Intelligence, Learning Objectives, Adaptive Learning, Retrieval Practice, Learning Experience Platform.