02323nas a2200253   4500000000100000000000100001008004100002260001200043653002800055653003700083653001500120653002200135653001500157100002000172700001300192700001700205700001400222245009600236856008200332300001200414490000600426520162300432022001402055       2021                            d  c12/202110aArtificial Intelligence10aHuman-Computer Interaction (HCI)10ae-learning10aVirtual Assistant10aMultimedia1 aS. Cobos-Guzman1 aS. Nuere1 aL. De Miguel1 aC. König00aDesign of a Virtual Assistant to Improve Interaction Between the Audience and the Presenter  uhttps://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_20_0.pdf  a232-2400 v73 aThis article presents a novel design of a Virtual Assistant as part of a human-machine interaction system to improve communication between the presenter and the audience that can be used in education or general presentations for improving interaction during the presentations (e.g., auditoriums with 200 people). The main goal of the proposed model is the design of a framework of interaction to increase the level of attention of the public in key aspects of the presentation. In this manner, the collaboration between the presenter and Virtual Assistant could improve the level of learning among the public. The design of the Virtual Assistant relies on non-anthropomorphic forms with ‘live’ characteristics generating an intuitive and self-explainable interface. A set of intuitive and useful virtual interactions to support the presenter was designed. This design was validated from various types of the public with a psychological study based on a discrete emotions’ questionnaire confirming the adequacy of the proposed solution. The human-machine interaction system supporting the Virtual Assistant should automatically recognize the attention level of the audience from audiovisual resources and synchronize the Virtual Assistant with the presentation. The system involves a complex artificial intelligence architecture embracing perception of high-level features from audio and video, knowledge representation, and reasoning for pervasive and affective computing and reinforcement learning to teach the intelligent agent to decide on the best strategy to increase the level of attention of the audience.  a1989-1660