Assessing The Impact of Enhancing AI-Driven Educational Applications Using Emotional Blackmail Prompts in Large Language Models
Sheng-Ming Wang, Chen Chuding · 2024
The rapid integration of Artificial Intelligence (AI) in educational systems has revolutionized teaching and learning methodologies, mainly through the advancement of Generative AI (GAI). This study evaluates the efficacy of emotional blackmail prompts-a novel interaction strategy designed to enhance the responsiveness of large language models (LLMs) like GPT4o, Kimi, and Gemini in educational applications. By leveraging a methodological framework that combines bibliometric and text analysis, our research reveals significant variations in how these models process and respond to emotionally charged prompts. The findings suggest that emotional blackmail can influence the quality and accuracy of AI-generated educational content, highlighting GPT4o's superior ability to adapt to emotional cues compared to other models. This study sheds light on the potential of emotional blackmail prompts to refine AI interactions. It also discusses such strategies' ethical implications and practical applications in improving AI-driven educational tools.