This paper explores the impact of generative ar-tificial intelligence on text production, focusing on the use of prompt-based AI tools. We propose to move beyond the current use of Large Language Models (LLMs) as “ghostwriters” and advocate for their relevance as tools to enhance the development of epistemic-heuristic strategies to facilitate knowledge construction. Our planned study aims to gain insight into the cognitive processes and behavior of university students when interacting with content generated by LLMs using eye tracking technology. Based on these insights, we explore the potential of providing cognition-aware assistance to enhance writing skills in educational contexts.

Intelligent writing assistants use artificial intelligence to support the partial automation of the writing process. Existing research has investigated the interaction between humans and automated systems and has identified the maintenance of situation awareness (SA) as a key challenge for humans. Especially in the context of intelligent writing assistants, humans have to maintain SA as they are held responsible for the written text. Eye tracking is the key technology that enables the non-invasive detection of situation awareness based on eye movements. Building on existing research on human-robot/AI collaboration and their interplay with SA theory, we propose the augmentation of human interaction with intelligent writing assistants through the use of eye tracking technology. On this basis, writing assistants can be adapted to users’ cognitive states such as SA. We argue that for the successful implementation of intelligent writing assistants in the real world, eye-based analysis of SA and augmentation are key.

In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods have been proposed to explain individual predictions to users in order to increase the transparency of the underlying Artificial Intelligence (AI) systems. However, the user perspective has received less attention in XAI research, leading to a (1) lack of involvement of users in the design process of local model-agnostic explanations representations and (2) a limited understanding of how users visually attend them. Against this backdrop, we refined representations of local explanations from four well-established model-agnostic XAI methods in an iterative design process with users. Moreover, we evaluated the refined explanation representations in a laboratory experiment using eye-tracking technology as well as self-reports and interviews. Our results show that users do not necessarily prefer simple explanations and that their individual characteristics, such as gender and previous experience with AI systems, strongly influence their preferences. In addition, users find that some explanations are only useful in certain scenarios making the selection of an appropriate explanation highly dependent on context. With our work, we contribute to ongoing research to improve transparency in AI.

Surveys are a widely used method for data collection from participants. However, responding to surveys is a time consuming task and requires cognitive and physical efforts of the participants. Eye-based interactions offer the advantage of high speed pointing, low physical effort and implicitness. These advantages are already successfully leveraged in different domains, but so far not investigated in supporting participants in responding to surveys. In this paper, we present EyeLikert, a tool that enables users to answer Likert-scale questions in surveys with their eyes. EyeLikert integrates three different eye-based interactions considering the Midas Touch problem. We hypothesize that enabling eye-based interactions to fill out surveys offers the potential to reduce the physical effort, increase the speed of responding questions, and thereby reduce drop-out rates.

Interruptions are known as one of the big challenges in working environments. Due to improper resuming the primary task, such interruptions may result in task resumption failures and negatively influence the task performance. This phenomenon also occurs when users are working with information dashboards in working environments. To address this problem, an attentive dashboard issuing visual feedback is developed. This feedback supports the user in resuming the primary task after the interruption by guiding the visual attention. The attentive dashboard captures visual attention allocation of the user with a low-cost screen-based eye-tracker while they are monitoring the graphs. This dashboard is sensitive to the occurrence of external interruption by tracking the eye-movement data in real-time. Moreover, based on the collected eye-movement data, two types of visual feedback are designed which highlight the last fixated graph and unnoticed ones.