AI-Powered Engagement Shots: Major-Specific Introductions, Applications, and Games to Spark Interest in Organic Chemistry

This study examines artificial intelligence (AI) not only as a student resource but as a pedagogical enabler—capable of operationalizing strategies such as context-based learning, narrative framing, and gamification that enhance motivation and relevance but are often difficult for instructors to sustain. By automating the generation of tailored scenarios, prompts, and examples, AI can make it feasible to embed these approaches consistently across large, multi-major classrooms. We applied this design in an undergraduate organic chemistry course for non-majors (N = 69) including Biomedical Laboratory Sciences, Nutrition, and Biology students. Organic chemistry for non-majors typically presents both conceptual challenges and low motivation due to limited career relevance, making this cohort well suited for examining AI-assisted pedagogies. Within this context, AI chatbot was integrated into chapter introductions, career-aligned scenarios, real-time activities, take-home assignments linking molecules to real-world contexts, and a game-based challenge—allowing the instructor to shift from sole source of personalization to a facilitator who guided and validated AI-generated materials. Surveys administered at the start and end of the semester revealed notable gains: student interest in organic chemistry increased from 42.0% to 73.3%, perceived relevance to majors rose from 24.6% to 85.0%, and importance for careers grew from 20.3% to 83.3%. Feedback after each activity indicated stronger awareness of real-life applications, greater confidence, and appreciation for AI’s role in making chemistry approachable. Students valued the clarity of introductions, the applied focus of the “Celebrity Molecules” assignment, and the engaging, collaborative nature of the game. Findings suggest AI can make evidence-based strategies more feasible and scalable, enhancing motivation and relevance in courses where students often struggle. Future work should examine long-term learning outcomes and transferability across disciplines.