Anki Tagger: A Generative AI Tool for Aligning Third-Party Resources to Preclinical Curriculum

Abstract

Using large language models, we developed a method to efficiently query existing flashcard libraries and select those most relevant to an individual's medical school curricula.

A Use Case for Generative AI in Medical Education

“…A recent study explored the novel application of generative artificial intelligence’s (GAI’s) capabilities with regard to Anki using a new methodology (“Anki Tagger”), leveraging OpenAI’s ChatGPT-3.5 to tag and stratify flashcards from the AnKing deck, which are most aligned with a medical school’s curriculum and involve a minimal cost and time expenditure [ 1 ]. To the best of our knowledge, their work represents the first publication demonstrating early proof of concept of GAI applied to Anki, a spaced repetition flashcard application designed to promote long-term retention of learned content.…”

Authors’ Reply: A Use Case for Generative AI in Medical Education

“…We thank the authors for their thoughtful comments on our paper titled, “Anki Tagger: A Generative AI Tool for Aligning Third-Party Resources to Preclinical Curriculum” [ 1 2 ]. The authors’ discussion of the ethical issues and limitations of generative artificial intelligence is both timely and important.…”

“…We circumvented the issue of hallucination by embedding existing Anki flashcard decks in a large language model, rather than prompting ChatGPT to generate flashcards de novo from scientific literature [ 1 ]. Anki flashcard decks are among the third-party resources used by medical students to bridge perceived gaps in school curricula, especially regarding preparation for the USMLE (United States Medical Licensing Examination).…”

“…Therefore, it is beneficial to both teachers and students for medical education to be as efficient as possible. To this end, ChatGPT can organize and stratify third-party learning resources by relevance to lectures and other curricular elements [ 1 ].…”