Understanding Users’ Acceptance of Conversational AI for Teaching Deepfake Video Detection: An Extended TAM Approach

The advancement of artificial intelligence algorithms has led to the emergence of deepfakes, which are generated through these algorithms to create realistic images, videos, or audio. Although studies have shown that humans can recognise certain types of deepfakes, such as those with political figures, detection accuracy among general individuals remains only marginally above random guessing. Conversational AI presents a promising approach to interactive learning, which is characterised by systems that simulate human-like dialogue through Natural Language Processing. Chatbots are a prominent application of this technology, which can act as scalable and real-time educational tools for guiding users in specific tasks. However, there is limited research on adopting conversational AI systems specifically designed to educate individuals in deepfake video detection. In this study, a chatbot based on ChatGPT named “Deepfake Fighter” was developed to teach people about deepfake video identification. Subsequently, semi-structured interviews with 30 participants were conducted to evaluate the chatbot’s acceptance factors. The study examined the three key factors of trust, ease of use, and usefulness derived from the extended Technology Acceptance Model. Importantly, specific antecedents were identified for each factor, providing insights into how these elements shaped user acceptance.