Chatbot-Mediated Learning For Caregiving Relatives of People With Dementia: Empirical Findings and Didactical Implications For Mulitprofessional Health Care

Abstract

Supporting family caregivers is a major challenge for the healthcare system. The first points of contact are physicians, nurses and social services, which are not easily accessible. For this reason, an information platform has been developed to provide information for family caregivers caring for people with dementia at home. The aim of this article is to provide an insight into the didactic design of this platform. Sample and Methods: A didactic concept was developed based on didactic target group analysis and interviews with caring relatives (n=6). Results:The didactic concept of the digital platform takes into account the characteristics of family caregivers as learners, such as time constraints and reciprocity. Therefore two different learning paths, a long and a short version, are offered. Reciprocity is supported by information which are related to individual characteristics of the caring relation. This is made possible by an adaptation of the didactic method "anchored instructions": Family caregivers experience a problematic caring situation. They use the platform and central concepts related to this situation are offered as anchors. In chatbot mediated learning, these concepts are identified and, ideally, relevant information is provided in a short version. These concepts are displayed as a learning map and must be proactively selected. Chatbot mediated learning has the advantage that matching concepts are offered as a pre-selection. Especially for inexperienced carers who are not familiar with the concepts, this learning path seems to be suitable. Conclusion:The combination of learning through the "Information for Relatives" website and CML seems to meet all needs. In order to promote learner motivation, the chatbot should not only offer the identified concept, but also those related to this concept, in order to link new knowledge in one's own knowledge network.