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Personalised Training: Integrating Recommender Systems in XR Training Platforms

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dc.contributor.authorPretolesi, Daniele
dc.contributor.editorMarky, Karola
dc.contributor.editorGrünefeld, Uwe
dc.contributor.editorKosch, Thomas
dc.date.accessioned2022-08-30T10:27:44Z
dc.date.available2022-08-30T10:27:44Z
dc.date.issued2022
dc.description.abstractThe fast-paced growth of Extended Reality (XR) technologies in complex environments, such as training scenarios, has highlighted the need to implement Artificial Intelligence (AI) modules in the simulations to support trainers and trainees in these unfamiliar contexts. Among the possible AI solutions, recommender systems (RS) could be used to improve the users’ interactions and experience in immersive training environments. This work describes the integration of a RS in the framework of an XR training platform and how the design of interfaces to present recommendations can maximize acceptance of the suggestions in hybrid human-intelligent systems. By allowing trainers to adapt training scenarios during the execution of the exercise, successful and personalized training goals can be achieved.en
dc.identifier.doi10.18420/muc2022-mci-ws12-294
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39103
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2022 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjectExtended Reality
dc.subjectVirtual Reality
dc.subjectAugmented Reality
dc.subjectXR Training
dc.subjectArtificial Intelligence
dc.subjectRecommender System
dc.titlePersonalised Training: Integrating Recommender Systems in XR Training Platformsen
dc.typeText/Workshop Paper
gi.citation.publisherPlaceBonn
gi.conference.date4.-7. September 2022
gi.conference.locationDarmstadt
gi.conference.sessiontitleMCI-WS12: UCAI 2022: Workshop on User-Centered Artificial Intelligence
gi.document.qualitydigidoc

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