Semantic Ambiguity in AI-Generated Texts: Challenges in Human Interpretation

This study explores the presence and effects of semantic ambiguity in texts generated by artificial intelligence (AI), particularly how such ambiguity challenges human interpretation. While AI-generated texts often appear fluent and grammatically correct, they frequently contain multiple possible meanings due to the lack of contextual understanding. Using a descriptive qualitative approach, this research analyzes three AI-generated sentences and collects interpretations from six respondents through a structured questionnaire. The findings reveal three main types of semantic ambiguity: referential, structural, and comparative. Each sentence was interpreted differently by respondents, demonstrating that AI-generated language can lead to confusion even in seemingly simple statements. The results highlight that ambiguity in AI output is not only a linguistic concern but also a practical obstacle to clear communication. This study emphasizes the need for greater awareness of semantic ambiguity in AI systems and encourages future development of models that better manage meaning through clearer context and structure.