arXiv subjects

Ala Yankouskaya

Publications and source records attributed to Ala Yankouskaya.

8 recordsLinked to original sources

arXiv2026

Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)

There is a growing need for reliable and culturally validated instruments to assess psychological dependency on large language models (LLMs), particularly as LLMs are increasingly used for task execution, decision-making, and communication in organizational and work-related settings. This need is especially relevant for Spanish-speaking populations, where LLM adoption is rapidly expanding, yet validated psychometric tools remain scarce. The present study reports the first validation of the Spanish version of the Large Language Model Dependency Scale (LLM-D12-SP), extending prior validations conducted in English- and Arabic-speaking samples. The LLM-D12 is a two-dimensional instrument assessing Instrumental Dependency (reliance on LLMs for performing tasks and supporting decisions) and Relationship Dependency (psychological reliance on LLMs for companionship and social interaction). A total of 386 Spanish-speaking participants (M = 28.0 years, SD = 6.1; 55% male) completed the LLM-D12-SP. Confirmatory factor analysis supported the original two-factor structure. The scale demonstrated good internal consistency (Cronbach's alpha = 0.89 total; 0.86 Instrumental; 0.85 Relationship). Discriminant validity analyses indicated that the two subscales represent related but distinct constructs. External validation showed that both dependency dimensions were positively associated with internet addiction and perceived trustworthiness of LLMs, while showing weak or no association with need for cognition. Together with prior English and Arabic validations, these findings establish cross-linguistic support for the scale's structure and provide a psychometrically sound tool for investigating psychological aspects of LLM use in organizational contexts.

cs.CL↗

arXiv2026

Adaptation and Validation of the Turkish Version of the Large Language Model Dependency Scale (LLM-D12)

This study aimed to adapt the Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models (LLM-D12) into Turkish and evaluate its psychometric properties among regular LLM users. A sample of 387 participants (68.5% female; mean age = 25.22 +/- 7.13) completed the translated scale, which underwent cultural-linguistic validation through forward-backward translation and expert review. Confirmatory factor analysis supported the original two-factor structure after removing one item, with strong model fit (CFI = 0.993, RMSEA = 0.073). Internal consistency was high across both subscales: Cronbach's alpha = 0.831 (instrumental), 0.876 (relational), and 0.868 (total); McDonald's omega = 0.834, 0.880, and 0.900, respectively. Test-retest reliability and item monotonicity were satisfactory. External validity was demonstrated via significant associations with ATAI, IA, and PTLLM scores. Interestingly, the lack of association with need for cognition (NFC) suggests that LLM dependency may reflect strategic cognitive offloading rather than cognitive avoidance. The Turkish version of the LLM-D12 is a valid and reliable 11-item tool for assessing both instrumental and relational dependencies on LLMs.

stat.AP↗

arXiv2026

Protege Effect for Behaviour Change: Does Teaching Digital Stress Solutions to Others Reduce One's Own?

The protégé effect suggests that individuals learn a subject more effectively when they teach it to others. Can we therefore assume that when individuals with a problematic behaviour teach others about it and how to manage it, they become more likely to reduce and better manage that behaviour themselves? We address this question by evaluating a protégé based approach to reducing and managing digital stress (DS). Over three weeks, 137 participants with moderate-high DS were assigned to four groups. Two groups followed a protégé based approach: a passive group, given materials to teach, and an active group, given headlines and required to search for and prepare teaching content. Both groups completed three sessions, each focusing on one DS dimension: availability stress, approval anxiety, and fear of missing out. A digital literacy group received content and quizzes, alongside a control group. Outcomes included DS levels, problematic social media use, word of mouth, and issue involvement. Results showed no significant differences between groups. The findings highlight the difficulty of translating cognitive engagement into behavioural change. This study contributes empirical evidence to digital wellbeing literature by illustrating the challenges of enhancing digital wellbeing, potentially due to persistent digital habits and socially reinforced stressors.

cs.HC↗

arXiv2025

Developing and Validating the Arabic Version of the Attitudes Toward Large Language Models Scale

As the use of large language models (LLMs) becomes increasingly global, understanding public attitudes toward these systems requires tools that are adapted to local contexts and languages. In the Arab world, LLM adoption has grown rapidly with both globally dominant platforms and regional ones like Fanar and Jais offering Arabic-specific solutions. This highlights the need for culturally and linguistically relevant scales to accurately measure attitudes toward LLMs in the region. Tools assessing attitudes toward artificial intelligence (AI) can provide a base for measuring attitudes specific to LLMs. The 5-item Attitudes Toward Artificial Intelligence (ATAI) scale, which measures two dimensions, the AI Fear and the AI Acceptance, has been recently adopted and adapted to develop new instruments in English using a sample from the UK: the Attitudes Toward General LLMs (AT-GLLM) and Attitudes Toward Primary LLM (AT-PLLM) scales. In this paper, we translate the two scales, AT-GLLM and AT-PLLM, and validate them using a sample of 249 Arabic-speaking adults. The results show that the scale, translated into Arabic, is a reliable and valid tool that can be used for the Arab population and language. Psychometric analyses confirmed a two-factor structure, strong measurement invariance across genders, and good internal reliability. The scales also demonstrated strong convergent and discriminant validity. Our scales will support research in a non-Western context, a much-needed effort to help draw a global picture of LLM perceptions, and will also facilitate localized research and policy-making in the Arab region.

cs.HC↗

arXiv2025

Debiasing the Influence of Demographic and Appearance Cues in Social Engineering via Role-Taking: Negative Results

This study investigates the efficacy of role-taking and literacy-based interventions in reducing the influence of appearance cues, such as gender, age, ethnicity, and clothing style, on trust and risk-taking in social engineering contexts. A-4 (Group: Control, Literacy, Persuader, Persuadee) * 2 (Time: Pre, Post) mixed factorial design was implemented over two weeks with 139 participants. The control group received no material. The literacy group attended two sessions focused on how behavior can be similar regardless of appearance cues. The persuader group completed three sessions, learning how to use such cues to influence others. The persuadee group attended three sessions involving the selection, justification, and reflection on personas and scenarios. Scenarios centered on financial and rental advice. A one-week gap followed before post-intervention testing. In both pre- and post-tests, participants assessed personas combining appearance cues, offering mobile hotspots with potential risk. They rated trust and willingness to take the risk. Validated measures and scenarios were used, including word-of-mouth and issue involvement scales. It was expected that cue influence would diminish post-intervention. However, no significant within- or between-group differences emerged. Findings raise concerns about the effectiveness of debiasing efforts and call for reconsideration of approaches using literacy, role-taking, rehearsal, drama, and simulation.

cs.HC↗

arXiv2025

Measuring Large Language Models Dependency: Validating the Arabic Version of the LLM-D12 Scale

There is an urgent need for reliable, culturally validated instruments to assess psychological responses to AI in general and large language models (LLMs). This need is global issue, but it is especially urgent among Arabic-speaking populations, where AI and LLMs adoption is accelerating, yet psychometric tools remain limited. This study presents the first validation of the LLM-D12, a dual-dimensional scale assessing Instrumental and Relationship Dependency on LLMs, in an Arab sample. A total of 250 Arab participants completed the Arabic version of the LLM-D12. Confirmatory Factor Analysis confirms the original 2-factor structure of LLM-D12 with all items showing good loading of corresponding Instrumental and Relationship Dependency. The scale showed good to excellent internal reliability (Cronbach alpha is 0.90 for Total, 0.85 for Instrumental Dependency, and 0.90 for Relationship Dependency). External validation revealed that Instrumental Dependency was positively associated with AI acceptance and internet addiction, while Relationship Dependency was linked to lower need for cognition and greater trustworthiness of LLM, demonstrating sensitivity of this instrument to different use and personal factors. These findings confirm that Arabic LLM-D12 is a psychometrically sound, culturally appropriate instrument, offering a necessary tool for research, education, and policy concerning AI and LLMs engagement in Arab contexts.

cs.HC↗

arXiv2025

LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models

There is growing interest in understanding how people interact with large language models (LLMs) and whether such models elicit dependency or even addictive behaviour. Validated tools to assess the extent to which individuals may become dependent on LLMs are scarce and primarily build on classic behavioral addiction symptoms, adapted to the context of LLM use. We view this as a conceptual limitation, as the LLM-human relationship is more nuanced and warrants a fresh and distinct perspective. To address this gap, we developed and validated a new 12-item questionnaire to measure LLM dependency, referred to as LLM-D12. The scale was based on the authors' prior theoretical work, with items developed accordingly and responses collected from 526 participants in the UK. Exploratory and confirmatory factor analyses, performed on separate halves of the total sample using a split-sample approach, supported a two-factor structure: Instrumental Dependency (six items) and Relationship Dependency (six items). Instrumental Dependency reflects the extent to which individuals rely on LLMs to support or collaborate in decision-making and cognitive tasks. Relationship Dependency captures the tendency to perceive LLMs as socially meaningful, sentient, or companion-like entities. The two-factor structure demonstrated excellent internal consistency and clear discriminant validity. External validation confirmed both the conceptual foundation and the distinction between the two subscales. The psychometric properties and structure of our LLM-D12 scale were interpreted in light of the emerging view that dependency on LLMs does not necessarily indicate dysfunction but may still reflect reliance levels that could become problematic in certain contexts.

cs.HC↗

arXiv2025

So, I climbed to the top of the pyramid of pain -- now what?

This paper explores the evolving dynamics of cybersecurity in the age of advanced AI, from the perspective of the introduced Human Layer Kill Chain framework. As traditional attack models like Lockheed Martin's Cyber Kill Chain become inadequate in addressing human vulnerabilities exploited by modern adversaries, the Humal Layer Kill Chain offers a nuanced approach that integrates human psychology and behaviour into the analysis of cyber threats. We detail the eight stages of the Human Layer Kill Chain, illustrating how AI-enabled techniques can enhance psychological manipulation in attacks. By merging the Human Layer with the Cyber Kill Chain, we propose a Sociotechnical Kill Plane that allows for a holistic examination of attackers' tactics, techniques, and procedures (TTPs) across the sociotechnical landscape. This framework not only aids cybersecurity professionals in understanding adversarial methods, but also empowers non-technical personnel to engage in threat identification and response. The implications for incident response and organizational resilience are significant, particularly as AI continues to shape the threat landscape.

cs.CR↗