When he was 55 years old, Jorge Luis Borges, the great Argentinian writer, lost his sight. At the same time, he became director of the National Library of Argentina. Suddenly in charge of Argentina’s literature at a time when he could no longer read any of it, he wasn’t entirely surprised. Blindness ran in his family, and he decided to follow the example of his father and grandmother and die “blind, laughing, and brave”.
He produced another 40 books before is death, and learned Old English orally. And yet, his relationship with letters had fundamentally changed. He never learned braille. Instead he preferred to rely on transcribers and readers (including his mother who had done the same for his father.)
Instead of finding a workaround, Borges embraced his new relationship with letters. If the internet had been around when he was working, he might have enjoyed the possibilities of conversational AI.
Conversational AI, leveraging advanced technologies such as natural language processing (NLP) and machine learning, offers immense potential to bridge gaps in user experience. By integrating conversational AI, web apps can become more intuitive, responsive, and personalised, catering to the needs and preferences of everyone from those who prefer a chat bot to help them increase the font size of online text to those like Borges, who “replaced the visible world with the aural world.”
It can also help to reduce cognitive load for neurodiverse users and help direct those who are less accustomed to common navigation practices.
Enhancing user accessibility through conversational AI
Conversational AI can transform the way users interact with web applications. Like a human guide, it can help adapt services, even where the user might not be aware that adaptations exist.
Task-oriented interaction: Whether it’s booking an appointment or purchasing a product, conversational AI can guide users through complex processes with ease. By focusing on user intent, such as finding the right shoe size or understanding a service, AI chatbots can provide direct, relevant responses, thereby reducing user effort and enhancing satisfaction.
Adaptive tone and empathy: The ability of AI to adapt its tone based on the user input can make interactions more engaging. An empathetic chatbot can respond to user frustrations or confusion with appropriate concern and suggestions, making the digital space more comforting and accessible.
Multiple interaction modes: Offering users the choice between texting, voice commands, or even video interactions can greatly enhance accessibility. This flexibility allows users with different abilities and preferences to choose the mode of communication that best suits their needs.
Personalised user experiences: By learning from each interaction, AI chatbots can tailor conversations and recommendations to individual user preferences, which is crucial for users who require more customised assistance.
Overcoming challenges with smart design
Integrating conversational AI into web apps comes with its own set of challenges. Thoughtful design and implementation – and loads of testing – can help mitigate this.
Complexity in conversation design: Ensuring that the AI can handle a range of topics without overwhelming the user requires careful design of conversation flows and continuous training with diverse datasets.
Avoidance of irrelevant responses: AI must be designed to stay on topic, provide concise and relevant information, and avoid overlong responses that might add an overwhelming cognitive load. Follow up questions (if necessary) should be designed to elicit precise contextual information to ensure the user gets the kind of answer they expected in as few steps as possible.
Integration with existing systems: To provide accurate information and seamless service, AI chatbots need to be well integrated with backend systems such as CRM and ERP. This integration ensures that the bot has real-time access to necessary data.
Privacy and ethical considerations: As conversational AI processes a large amount of personal data, maintaining transparency, ensuring data security, and protecting user privacy are crucial. Ethical AI practices must be a cornerstone of the design process.
Implementing best practices
If we want conversational AI to be effective, there are a few best practices we can follow.
Intent recognition: Utilising NLP to understand and prioritise user intent helps in crafting responses that are more likely to satisfy user queries.
Guided paths and escalation: While AI can handle many interactions autonomously, providing clear pathways for users to escalate issues to human agents is necessary for complex or sensitive issues.
Regular updates and feedback loops: Continuous monitoring and updating of AI models based on user feedback help improve accuracy and user satisfaction.
Universal design principles: Following universal design principles ensures that the conversational AI application is usable by people with a wide range of abilities and disabilities.
Conversational AI has the potential to make our web apps more efficient and effective, but also more inclusive and accessible. By focusing on user needs and continuously adapting to feedback, AI-driven chatbots can provide a supportive and engaging user experience that caters to everyone, regardless of their physical abilities or tech-savviness.
Even those, like Borges, whom God granted “books and blindness at one touch.”
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