Page 534 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

will be enriched by implementation guidelines to support replicability and further exploitation
of results in terms of design of intelligent software algorithms and advanced human-machine
interfaces.

In particular, XTENDIT might apply to all the different place-based training scenario, e.g. on-
the-job physical experiences, that offer the possibility to both scan and create the 3D model
of a system (e.g. machine, vehicle, industrial line), and position markers to implement points
of interests around which is possible to create the interactive XR training. Replicability is thus
assured by the XR training authoring tool that makes the 3D contents both available by means
of the adoption of the AR and VR apps.

Partners
Alma Mater University of Bologna, with the involvement of the Department of Architecture,
through the Advanced Design Unit (ADU), and the Department of Industrial Engineering
collaborated on the project. The UNIBO Advanced Design Unit (ADU [14]) operates in the
Department of Architecture through the tracks on Industrial Design and Advanced Product
and Service Design, which have been established since 2013 to train students on design-
driven approaches to innovation. The Department of Industrial Engineering of the University
of Bologna has been working for years on issues related to human-machine interaction and
augmented and virtual reality systems as new typologies of user interfaces. The UNIBO team
has contributed to the design of novel interaction paradigms and adaptive interfaces, and to
the iterative and incremental implementation of XTENDIT field experiments.

2�2 Benefits of the use case

1. By considerably improving workers upskilling and capability of managing automated
machines, XTENDIT promote operators that are resilient and creative problem solvers,
and that impacts on improvement of number of components produced per unit of time
after scaling up, reduction in production downtime due to technology adoption and
integration, number of error reduction and number or rework, reduction of onboarding
time for new workers;
2. By enhancing industry training capability, XTENDIT aims to increase the technological
literacy rate in the target population, the upskilled employees rate (pre and post skills
assessments), and the rate of talents attracted by the industry.

2�3 Future Work

XTENDIT aims to extend its exploitation of adaptive distributed XR HMI, by implementing
and introducing to the market a module for XR gamification strategies for Human-Centered
Augmentation. Based on personalised, gamified, adaptive, distributed and context-sensitive
user interfaces, XTENDIT will include gamification in AI-driven personalized strategies that
adapts dynamically to user needs, engagement and experience levels, by means of collecting
and analyzing human-machine interaction data to continuously optimize the training experience
and interface personalization.

The ultimate scope is to enhance the work and training environment to make manufacturing
jobs more safe and attractive for young generations. The accomplishment of this result will be
informed by the assessment of the impact of training scenarios in XR environments on problem-
solving capabilities within industrial settings, through gamified strategies, such as challenges,
score system and informational hints, by measuring time-to-resolution, training effectiveness,

498