Embedded Files
Attendees who submitted papers are expected to produce and extended and revised version of their submission for the post workshop proceedings (Springer LNCS)
Introduction
Automation is pervasive in interactive systems [13]. While automation varies in nature and objectives, it is present in every layer of interactive systems architectures, from hardware input device driver level (e.g., mouse acceleration [3]), to interaction technique level (e.g., multimodal fusion such as finger clustering [20] or more sophisticated ones such as the bubble cursor [15] integrating in a single design automation at both input and output levels) as well as at the interactive application level (e.g., auto-completion [14], or the automatic generation of visual or textual components).
AI technologies (e.g., machine learning, rule-based systems) enable complex tasks to be performed, targeting the ultimate goal of autonomous systems, e.g., self-driving cars, autonomous cooking chefs [21]. On the other hand, having more automation might induce more potential for failures (known as the lumberjack analogy ). Moreover, many AI technologies bring issues at the operation level due to their black-box nature, i.e., when users interact with an interactive application embedding them [2]. To address this issue, a recent contribution [24] has demonstrated the potential benefit of opening up that box and adding explanations.
Generative AI, for example, can generate various types of data (images, text, layout, persona’s,. . . ) driven by models that are trained with specific training data. Generative AI is best known for its capability to generate text and images. Still, it is also becoming more common as a supporting tool during the engineering life cycle for building interactive systems. Researchers have started exploring the use of Large Language Models for various aspects of the Human-Centred Development Process [25]. So far Generative AI has been used to support data-driven designs and generating prototypes [8, 17, 26], human-robot interaction [28], integrating multi-modal interaction techniques [29], and even feedback on User Interface designs [12] and usage within a broader social context [23].
Integrating AI technologies can be performed at various levels, from micro to macro, requiring different (and maybe conflicting) engineering approaches. Thus, at the engineering level, different issues appear depending on the type of AI-related technologies used and the type of interactions provided to the users of such systems. Indeed, beyond explanations, issues related to display/visualization [18] and control/command [19] arise. At the dependability level, even though multiple iterations have been performed, reliability remains very low (at about 80% accuracy for simple datasets) but much lower in some domains, such as food allergies, which might be considered critical in case of severe pathology [24].
Despite the many challenges AI technologies bring in interactive systems, the potential for effortless and seamless interaction with systems far outweighs the cost of designing usable systems. With this workshop, we aim to offer a platform for scientists interested in the design, development, evaluation, and use of interactive systems involving AI technologies to address these engineering challenges. This platform will offer idea exchange, discussions, and collaborations and, thus, drive the development of AI-powered interactive systems in an interdisciplinary manner.
Goals and Objectives
The main goal of this workshop is to offer a platform for scientists who are interested in the design, development, evaluation, and use of interactive systems involving AI technologies.
The first objective is to identify and gather information about knowledge and practice in the workshop’s domain:
· Get an overview of current practices in R&D practices (methods/notations/tools) to engineer usable interactive systems embedding AI technologies, as well as lessons learned and recommendations;
· Get an overview of current practices in R&D practices (methods/notations/tools) to architect usable interactive systems embedding AI technologies, as well as lessons learned and recommendations;
· Identify a systematic approach for describing AI technologies and assessing their impact on properties such as users’ UX and systems’ usability;
· Understand how the multiple stakeholders involved in interactive systems design and development identify properties, how they describe them, and how they assess their relative importance when they embed AI technologies (going beyond the classical UX and usability but also addressing performance, dependability, safety, ...)
Identify an engineering approach to find an equilibrium between (AI-based) automation and human interaction.
The second objective is to elicit the main gaps in AI technologies which hinders their exploitation in the design and development of interactive systems, especially if a user-centered design process is followed
The third objective proposes to focus on the specific issue of engineering interaction-driven AI. User interaction with AI technologies can occur at very different levels, ranging from perception-based techniques for input recognition to application-level tasks such as recommending and decision support. Conversational techniques are also gaining ground as components of interactive systems such as e-commerce websites or data analytics applications [16]. Engineering interaction-driven AI includes, among others, the design of suitable user interfaces [19], the integration of AI components with other interactive parts of the system, or the engineering of prompts for the application of large foundation models. The workshop aims to identify engineering methods to make the interaction with AI components transparent, controllable, explainable, and predictable
The fourth (and last) objective focuses on the design and use of AI technologies in methods, processes, and tools for building interactive systems in all stages of the engineering lifecycle. Generative AI such as LLMs are used to generate code, AI can be used for automatic training and validation of interactive systems, and design and prototyping of user interfaces and interaction techniques are driven by pre-trained models that generate “creative” solutions. In the near future, AI tools could gain the potential to create and integrate AI components in interactive systems too.
Globally, the activities carried out during the workshop aim to identify the current state of knowledge in the scope of the workshop but also to outline a research agenda by bringing together diverse and sometimes competing views from multiple stakeholders.
Post-workshop publications
Workshop attendees will be offered the opportunity to revise and extend their submission, which will, after reviewing, be included in the post-workshop proceedings to be published by Springer in the LNCS series. Paper length will be maximum 20 pages LNCS and minimum 10 pages LNCS format.
How to Contribute?
Submit your contribution by sending uploading your submission to easychair dedicated page
Template: Springer Lecture Notes in Computer Science proceedings
Contributions should be submitted in a PDF document of 2-6 pages in length (references excluded). Upon acceptance, at least one author will be required to register to the EICS conference and attend (physically) the workshop. Notifications will be sent to the authors according to the dates indicated below.
Do you have any questions about the workshop? Contact us at palanque@irit.fr
Important dates
Paper due: Friday May 9th, 2025 23:59 AoE (upload title, abstract and authors - papers can be updated until May 16th)
Notification to authors: Friday May 23rd, 2025
Final version-ready: Friday, May 30th, 2025
Registration to the workshop (for accepted authors and participants) May 31st 2025 - EICS early bird deadline
Final program for workshops: Friday June 6th, 2025
Workshop day: Tuesday June 24th, 2025
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