This paper presents findings from a thinking-aloud protocol exploring mental models in 28 elementary school math teachers during their initial attempt at composing and testing trigger-action rules for a smart tangible educational device. In the study, two sets of event-driven primitives were implemented in an End-User Development platform for guiding teachers with no programming experience in defining new functions of the device: "concrete", based on actual actions performed on the device, and "abstract", based on general definitions of events/actions. With a thematic analysis, we identified three different metaphors that drive participants’ interaction with the device. We discuss how the metaphors influenced performance and how the order of exposition to the two primitive sets impacted their grasping of the trigger-action logic. Our findings suggest the importance of guiding teachers in assuming effective metaphors for performing End-User Development tasks, to empower them to adopt an active role toward digital devices in education.

Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers’ mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers using co-design workshops to elicit their mental models when interacting with CCTs. Different important findings that might drive the interaction design with CCTs emerged. For example, developers expressed diverse preferences on when and how code suggestions should be triggered (proactive, manual, hybrid), where and how they are displayed (inline, sidebar, popup, chatbot), as well as the level of detail. It also emerged that developers need to be supported by customization of activation timing, display modality, suggestion granularity, and explanation content, to better fit the CCT to their preferences. To demonstrate the feasibility of these and the other guidelines that emerged during the study, we developed ATHENA, a proof-of-concept CCT that dynamically adapts to developers’ coding preferences and environments, ensuring seamless integration into diverse workflows. • Users want flexible triggers: balance control with smart automation • Inline works for short code; sidebar/chatbot for longer suggestions • Start minimal, let users expand from single lines to full files • Keep explanations short, contextual, opened by click or shortcut • Let users tune timing, style, detail level, and coding format

Phishing is one of the most prolific cybercriminal activities, with attacks becoming increasingly sophisticated. It is, therefore, imperative to explore novel technologies to improve user protection across both technical and human dimensions. Large Language Models (LLMs) offer significant promise for text processing in various domains, but their use for defense against phishing attacks still remains scarcely explored. In this paper, we present APOLLO, a tool based on OpenAI’s GPT-4o to detect phishing emails and generate explanation messages to users about why a specific email is dangerous, thus improving their decision-making capabilities. We have evaluated the performance of APOLLO in classifying phishing emails; the results show that GPT-4o has exemplary capabilities in classifying phishing emails (97% accuracy) and that this performance can be further improved by integrating data from third-party services, resulting in a near-perfect classification rate (99% accuracy). To assess the perception of the explanations generated by this tool, we also conducted a study with 20 participants, comparing four different explanations presented as phishing warnings. We compared the LLM-generated explanations to four baselines: a manually crafted warning, and warnings from Chrome, Firefox, and Edge browsers. The results show that not only the LLM-generated explanations were perceived as high quality, but also that they can be more understandable, interesting, and trustworthy than the baselines. These findings suggest that using LLMs as a defense against phishing is a very promising approach, with APOLLO representing a proof of concept in this research direction.

The cybersecurity domain faces a critical disparity between the escalating demand for skilled professionals and the limited talent pool. This gap is primarily driven by the surge in cyberattacks perpetrated by malicious actors seeking financial gain or disruption. These attacks, encompassing tactics like Distributed Denial-of-Service (DDoS) and ransomware, pose significant threats to data security and public safety. To bridge this gap, several challenges must be addressed. We need to prioritize initiatives that enhance people’s awareness and education in cybersecurity. Additionally, exploring innovative training methods and leveraging technology to equip cybersecurity professionals with the necessary skillsets is crucial.

Assessing human vulnerability in cybersecurity is critical to understanding the relationship between human factors and the security of digital systems. This issue is exacerbated in areas such as public administration due to the sensitive nature of the data and services handled by government agencies, including citizen records, financial data, and national security details. Robust cybersecurity measures and understanding the human factors contributing to incidents are essential to securing public administration systems and maintaining public trust in government institutions. This poster presents ongoing work to develop a new psychometric tool to assess the human factors that play a critical role in cybersecurity incidents.

In recent years, the availability of data has increased across various fields, with a greater focus on the medical sector. Medical records, clinical charts, and diagnostic reports provide a large amount of information. The complexity of this data requires the use of advanced analytical methods to extract reliable and important information. This study aims to test Text Mining techniques, particularly Dynamic Topic Modeling (DTM), to monitor the evolution of medical practices within surgical records related to the descriptions of surgeries over time. By applying DTM to a large dataset of medical reports from a urology clinic, we were able to identify emerging topics, highlighting changes in topics over time and whether one or more surgeries are more or less frequent in the specific years 2022 and 2023. This method not only extracted emerging topics from the descriptions of surgical interventions but also provided a precise overview of how clinical practices and patient outcomes have evolved. Through this analysis, we can gain a more detailed understanding of therapeutic strategies and how they have evolved during the specified years, contributing to future clinical decision-making and improving patient care. The findings of this research provide detailed information on the importance of using advanced techniques to better understand medical data and its implications for healthcare practices.

This paper presents a pilot study for an initial assessment of a tangible tool to support end-user programming (EUP) by teachers to facilitate learning mathematics in primary school. The study aimed to explore teachers’ reasoning strategies and mental representations during trigger-action rules composition. The pilot study provided initial insight into the strengths and weaknesses of this approach and useful hints for designing a larger and more robust study

In this paper, we present the preliminary implementation of SMARTER, a tangible tool for supporting children in learning the basics of mathematics, which has been loosely inspired by the Cuisenaire rods. The tool exploits the power of tangible manipulation offered by physical materials coupled with the possibility of providing contextual and engaging feedback provided by digital tools. An original and important goal of the SMARTER tool is to allow teachers to fully customize the experience of use by exploiting end-user development.