Talking to Machines. Semiotic Analysis, Implications for Teaching and media Literacy

In the article "Talking to Machines. Semiotic Analysis, Implications for Teaching and Media Literacy", Pier Cesare Rivoltella explores the theoretical and practical implications of artificial conversation within the context of teaching and media literacy. Drawing on Niklas Luhmann’s communication theory and the Semiotic Theory of Audiovisual Conversation (TAC), the author examines how conversational agents powered by Large Language Models (LLMs) challenge traditional concepts of human communication and dialogue. The article analyzes how artificial communication, unlike human conversation, prioritizes message comprehensibility rather than intentionality, emphasizing syntactic coherence over semantic understanding. Rivoltella discusses how LLMs like ChatGPT and similar systems operate on a probabilistic basis, processing linguistic patterns without grasping the actual meaning. This raises fundamental questions about the nature of dialogue and interaction between humans and machines. Furthermore, the article addresses the role of prompt engineering as a strategic skill essential for effective use of conversational AI, highlighting the risks of "authority bias" and the "oracular effect" that can lead users to overestimate the reliability of AI responses. The paper also explores how artificial communication reshapes educational practices, suggesting the need to develop critical thinking and media literacy skills to navigate interactions with AI. Finally, Rivoltella calls for a pedagogical approach that integrates awareness of the limitations and affordances of artificial communication, promoting a reflective and critical use of dialogic systems in educational settings. The article positions artificial conversation as a critical literacy for contemporary media education, underlining the need to prepare students to engage responsibly and thoughtfully with AI technologies.