Entertainment chatbot for the digital inclusion of elderly people without abstraction capabilities

TL;DR

The paper tackles digital inclusion for elderly individuals with limited abstraction capabilities by introducing EBER, an intelligent radio chatbot that reads news in the background and adapts its dialogue to the user’s mood. It integrates AIML-based personality, natural language generation, and sentiment analysis to deliver short, contextually relevant conversations that connect news content with user opinions. The authors demonstrate feasibility through experiments with 31 elderly participants, showing high satisfaction and mood-adaptive engagement, and they quantify abstraction improvements via keyword extraction and semantic enrichment. This approach offers a practical, accessible conduit to digital content and a framework for measuring cognitive engagement in aging populations, with implications for loneliness mitigation and caregiver monitoring.

Abstract

Current language processing technologies allow the creation of conversational chatbot platforms. Even though artificial intelligence is still too immature to support satisfactory user experience in many mass market domains, conversational interfaces have found their way into ad hoc applications such as call centres and online shopping assistants. However, they have not been applied so far to social inclusion of elderly people, who are particularly vulnerable to the digital divide. Many of them relieve their loneliness with traditional media such as TV and radio, which are known to create a feeling of companionship. In this paper we present the EBER chatbot, designed to reduce the digital gap for the elderly. EBER reads news in the background and adapts its responses to the user's mood. Its novelty lies in the concept of "intelligent radio", according to which, instead of simplifying a digital information system to make it accessible to the elderly, a traditional channel they find familiar -- background news -- is augmented with interactions via voice dialogues. We make it possible by combining Artificial Intelligence Modelling Language, automatic Natural Language Generation and Sentiment Analysis. The system allows accessing digital content of interest by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This approach permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space. To prove the suitability of the proposed solution we present results of real experiments conducted with elderly people that provided valuable insights. Our approach was considered satisfactory during the tests and improved the information search capabilities of the participants.

Figures (7)

• Figure 1: Tree-stage NLG module.
• Figure 2: Flow diagram of the sa module.
• Figure 3: Decision Tree classifier scheme.
• Figure 4: eber chatbot interface. The muted microphone indicates that it is eber's turn to speak.
• Figure 5: eber chatbot flow diagram. Example of a real dialogue.