Antiparasitic Pharmacology Goes to the Movies: Leveraging Generative AI to Create Educational Short Films

Medical education faces the dual challenge of addressing cognitive overload and sustaining student engagement, particularly in complex subjects such as pharmacology. This study introduces Cinematic Clinical Narratives (CCNs) as an innovative approach to teaching antiparasitic pharmacology, combining generative artificial intelligence (genAI), edutainment, and mnemonic-based learning. The intervention involved two short films, Alien: Parasites Within and Wormquest, designed to teach antiparasitic pharmacology to first-year medical students. A control group of students only received traditional text-based clinical cases, while the experimental group engaged with the CCNs in an active learning environment. Students who received the CCN material scored an average of 8% higher on exam questions related to the material covered by the CCN compared to students in the control group. Results also showed that the CCNs improved engagement and interest among students, as evidenced by significantly higher scores on the Situational Interest Survey for Multimedia (SIS-M) compared to traditional methods. Notably, students preferred CCNs for their storytelling, visuals, and interactive elements. This study underscores the potential of CCNs as a supplementary educational tool, and suggests the potential for broader applications across other medical disciplines outside of antiparasitic pharmacology. By leveraging genAI and edutainment, CCNs represent a scalable and innovative approach to enhancing the medical learning experience.

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Publication Details

Journal
AI
Published
2025-03-17
DOI
https://doi.org/10.3390/ai6030060
Citations
5
Primary Topic
Simulation-Based Education in Healthcare
Type
article
Field-Weighted Citation Impact
5.82
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article

Antiparasitic Pharmacology Goes to the Movies: Leveraging Generative AI to Create Educational Short Films

Meize Guo, Tyler Bland, Luke Sheneman, Benjamin Worthley
5 citations
AI
Simulation-Based Education in Healthcare
5.82
article

Antiparasitic Pharmacology Goes to the Movies: Leveraging Generative AI to Create Educational Short Films

Meize Guo, Tyler Bland, Luke Sheneman, Benjamin Worthley
article en
5 citations

Abstract

Medical education faces the dual challenge of addressing cognitive overload and sustaining student engagement, particularly in complex subjects such as pharmacology. This study introduces Cinematic Clinical Narratives (CCNs) as an innovative approach to teaching antiparasitic pharmacology, combining generative artificial intelligence (genAI), edutainment, and mnemonic-based learning. The intervention involved two short films, Alien: Parasites Within and Wormquest, designed to teach antiparasitic pharmacology to first-year medical students. A control group of students only received traditional text-based clinical cases, while the experimental group engaged with the CCNs in an active learning environment. Students who received the CCN material scored an average of 8% higher on exam questions related to the material covered by the CCN compared to students in the control group. Results also showed that the CCNs improved engagement and interest among students, as evidenced by significantly higher scores on the Situational Interest Survey for Multimedia (SIS-M) compared to traditional methods. Notably, students preferred CCNs for their storytelling, visuals, and interactive elements. This study underscores the potential of CCNs as a supplementary educational tool, and suggests the potential for broader applications across other medical disciplines outside of antiparasitic pharmacology. By leveraging genAI and edutainment, CCNs represent a scalable and innovative approach to enhancing the medical learning experience.

AIVol. 6(3)
University of Idaho (US), University of Washington (US), University of Florida (US)
Openalex Percentile: Top 4%
Simulation-Based Education in Healthcare
5.82
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Antiparasitic Pharmacology Goes to the Movies: Leveraging Generative AI to Create Educational Short Films — Meize Guo, Tyler Bland, et al. · AI (2025) | TGRS Research Map | TGRS