“Synthetic Map”: A Graphic Organizer Inspired by Artificial Neural Network Paradigms for Learning Organic Synthesis

• Published in: Journal of chemical education Vol. 101; no. 10; pp. 4256 - 4267
• Main Authors: Luque-Corredera, Carlos, Bartolomé, Elena, Bradshaw, Ben
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society and Division of Chemical Education, Inc 08.10.2024; American Chemical Society
• Subjects: Artificial neural networks; Chemical synthesis; Educational Benefits; Learning; Machine learning; Neural networks; Organic chemistry; Students; Synthesis; Teaching; Teaching machines; Teaching methods; Second-Year Undergraduate; Neural Networks; Problem Solving/Decision Making; Learning Theories; Organic Synthesis; Organic Chemistry
• ISSN: 0021-9584; 1938-1328
• DOI: 10.1021/acs.jchemed.4c00592

Organic Chemistry is widely recognized as a challenging subject, with the design of syntheses and retrosyntheses identified as particularly difficult tasks. Inspired by the success of artificial neural networks in machine learning, we propose a framework that leverages similar principles to enhance the teaching and learning of organic synthesis. In this paper, we introduce a novel teaching tool, the “Synthetic Map”, that attempts to visually recreate an expert’s mental map and conceptual understanding of organic synthesis built over years of experience. The educational benefits of the Synthetic Map were evaluated through its implementation in an Organic Chemistry course of a Pharmacy degree over two years. The new tool promoted students’ learning by providing a mental organizer fostering a deeper understanding of the subject and empowering students to design and execute effective synthetic strategies.