This paper proposes a general approach for using conversational interfaces such as chatbots to offer adaptive learning of business processes in an environment involving different actors. Adaptivity concerns both the content being proposed, the sequence of learning items, and the way the conversation is conducted. The original approach allows the development of sustainable chatbots and empowers various non-technical actors (authors, teachers, publishers, and learners) to control the chatbot features directly. The aCHAT-WF framework (adaptive CHATbot for WorkFlows), proposed in this paper for managing conversational interfaces, conceptually represents all the aspects related to a conversation about business processes, with different facets for the user, the conversation flow, and the conversation contents, combining them to obtain a flexible interaction with the user. The paper focuses on the different preparation phases for instructional material based on Business Process Modeling Notation (BPMN) models, separating the different roles involved in the construction of a chatbot for teaching business processes and with the possibility of defining different styles for the interaction with the users. The proposed method is configuration-driven, to facilitate the separation of the different aspects of the control of the interaction and the delivery of contents.

Social Media provides a trove of information that, if aggregated and analysed appropriately can provide important statistical indicators to policy makers. In some situations these indicators are not available through other mechanisms. For example, given the ongoing COVID-19 outbreak, it is essential for governments to have access to reliable data on policy-adherence with regards to mask wearing, social distancing, and other hardto-measure quantities. In this paper we investigate whether it is possible to obtain such data by aggregating information from images posted to social media. The paper presents VisualCit, a pipeline for image-based social sensing combining recent advances in image recognition technology with geocoding and crowdsourcing techniques. Our aim is to discover in which countries, and to what extent, people are following COVID-19 related policy directives. We compared the results with the indicators produced within the CovidDataHub behavior tracker initiative. Preliminary results shows that social media images can produce reliable indicators for policy makers.

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