The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)-driven conversational agents as potential supports for mental health care. In this exploratory pilot study, we examined the safety and feasibility of an intelligent virtual agent (IVA) designed to simulate psychotherapeutic interactions, with a focus on high-risk situations involving suicidality and substance use. Two licensed psychotherapists engaged in scripted interactions with the IVA across 12 predefined scenarios addressing suicidality and substance abuse. The IVA was powered by GPT-4omni and embedded in a Unity-based avatar. After each interaction, testers evaluated acceptance, usability, and human-robot interaction. Two independent psychotherapists rated the IVA's responses using a structured scale assessing guideline adherence, risk recognition, help provision, de-escalation, and empathy. No real patients were involved; all interactions were simulated for safety testing purposes. The IVA showed preliminary indications of good usability and generally empathic responses. However, problematic responses occurred in 29% of conversations, with 12.5% rated as highly critical. Responses rated as "critical" or "highly critical" referred to outputs that failed to provide adequate support, showed insufficient risk recognition, or included ethically problematic suggestions. Key concerns included inadequate recognition of risk, normalization of substance use, and insufficient referral to crisis resources, particularly in scenarios involving underage alcohol access and suicide-related inquiries. In this small, expert-based pilot safety evaluation, the findings suggest that although AI-based agents may improve access to mental health support, rigorous safety evaluation, clinical oversight, and robust safeguards are essential prior to clinical deployment. No clinical conclusions can be drawn from this simulated study.
We introduce and investigate the computational complexity of a novel physical problem known as the Pinball Wizard problem. It involves an idealized pinball moving through a maze composed of one-way gates (outswing doors), plane walls, parabolic walls, moving plane walls, and bumpers that cause acceleration or deceleration. Given the initial position and velocity of the pinball, the task is to decide whether it will hit a specified target point. By simulating a two-stack pushdown automaton, we show that the problem is Turing-complete even in two-dimensional space. In our construction, each step of the automaton corresponds to a constant number of reflections. Thus, deciding the Pinball Wizard problem is at least as hard as the Halting problem. Furthermore, our construction allows bumpers to be replaced with moving walls. In this case, even a ball moving at constant speed - a so-called ray particle - can be used, demonstrating that the Ray Particle Tracing problem is also Turing-complete. 2012 ACM Subject Classification Theory of computation -> Problems, reductions and completeness
There is little doubt that the persisting integration of algorithmic – and, more recently, AI-driven – systems into public opinion formation processes continues to spark lively discussion in the cross-disciplinary debate on critical algorithmic studies and the digital society. These issues raise fundamental questions about the interplay between human and technological actors and the evolving power dynamics that shape the visibility, circulation and interpretation of digital content. In this response, we welcome the opportunity to engage with the insightful commentaries we have received, which raise important issues about terminology, conceptual framing, user agency, technological determinism, and the broader political economic dynamics that concern the conceptual construction of the ‘algorithmic public opinion’ that we provide in our article.
The main concern of this research is to expose the Perceptions of Novice EFL Teachers on Addressing Comprehension Issues in Students’ Reading in junior high school setting. The research objective is to figure out the strategies of novice EFL teachers use to help students overcome reading comprehension issues. Qualitative research used as the research method with the case study design, involving two novice EFL teachers with advanced level and fewer years of experiences. Interview was used as the main instrument of this research, and Interactive Model by Miles and Huberman was also used as the technique of data analysis. The findings of this research expose that the novice EFL teachers face challenges in addressing students reading comprehension issues due to limited experiences and training. Despite these obstacles, they adapt by using strategies like pre-reading activity and vocabulary support. This highlights the need for targeted professional development to better equip novice EFL teacher in supporting students’ comprehension.
