This year's IB Computer Science Case Study focuses on the fascinating topic of . The case study introduces you to the challenges faced by an company named RAKT and their attempt to improve their chatbot system. The chatbot was initially implemented to handle customer queries but has faced several issues leading to customer dissatisfaction. Reducing latency, or the delay between user input and chatbot response, is crucial for providing a smooth, real-time experience. As user demand and query volume increase, this delay can become more pronounced, leading to slower interactions and diminished .
Handling linguistic nuances is a core challenge for chatbots, as they often struggle with , emotion, and context in user conversations. By improving the chatbot's ability to detect and respond to subtle variations in tone, emotion, and phrasing, it becomes more adept at personalizing responses and managing . The architecture underlying a chatbot plays a significant role in its language processing capabilities. Traditional models like recurrent neural networks (RNNs) may struggle with maintaining long-term dependencies in conversation, limiting the chatbot's ability to process language patterns.
Ensuring that the dataset is diverse, representative, and free from is essential to avoid skewed results that could alienate users or perpetuate stereotypes. By curating datasets that reflect a wide range of languages, cultural contexts, and conversational styles, developers can improve the chatbot’s ability to engage with a broad . The development and deployment of chatbots come with a host of ethical considerations, particularly around data privacy, security, and the potential spread of . Safeguarding user data through secure protocols and ensuring the chatbot provides unbiased and accurate information are critical steps to building .
Keywords
misinformation | user base | chatbots | performance | ambiguity | insurance | user satisfaction | biases | complex dialogues | complex | user trust |
