Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
Signal [73] demonstrates environment-grounded VR communication training with LLM agents for autistic adults is technically feasible today. Signal [68] shows embodied conversational agents can reduce language anxiety by adapting to learner proficiency level. Signal [64] establishes that LLM-driven virtual patients with calibrated emotional expressiveness are ready for communication training. Together these capabilities have matured enough in 2024-2025 to combine into a deployable product without frontier-model API costs being prohibitive.
Autistic individuals and others with communication differences lack scalable, personalized practice environments for workplace soft skills, which are highly contextual and cannot be rehearsed through isolated verbal drills or traditional role-play. Existing solutions are either too generic or too expensive to deploy at scale.
Autistic young adults and adults in vocational transition programs, supported employment services, and HR-led disability inclusion initiatives at mid-to-large employers
A VR and LLM-powered simulation platform that embeds workplace communication training inside realistic task contexts (e.g., a warehouse, office, retail floor), generating adaptive dialogue scenarios grounded in the learner's actual environment. The system uses generative AI agents that adjust vocabulary, pacing, and emotional intensity based on real-time learner responses, while giving coaches and job developers structured analytics on communication patterns across sessions.
The real-world evidence the pipeline drew on to generate this idea.
arXiv:2607.21769v1 Announce Type: new Abstract: Autistic individuals often face barriers in workplace communication, where soft skills are embedded within ongoing tasks and surrounding environment context, not in isolated verbal exchange. Recent work has introduced LLM-driven agents into VR-based communication training and proposed prompting schemas that let agents generate dialogue grounded in the VR environment and the user's hand-based interactions. Building on this work, we explore how different levels of environmental grounding influence the training experience of autistic trainees and job coaches. We conducted an exploratory study with 9 autistic trainees and 7 job coaches across three modalities: conversation-only (C), conversation with environmental objects (C+O), and conversation with objects and grasp interactions (C+O+G). Usability and workload were comparable across modalities, while both trainees and coaches preferred the more interactive and environment-grounded condition
arXiv:2607.21887v1 Announce Type: new Abstract: Foreign language anxiety (FLA) can be a major barrier to second language acquisition (SLA), especially in conversational contexts. With the proliferation of large language models (LLMs) throughout all areas of life, recent work suggests that interacting with LLM agents can be instrumental within the field of SLA and foreign language education, especially for reducing FLA. Related work also suggests that linguistic demands and task complexity can be predictors of FLA, implying that the use of demanding, complex language could lead to learners experiencing higher FLA. In this paper, we propose a novel multi-agent embodied conversational system that generates level-appropriate dialogue for English language learners. These levels are based on those defined by the Common European Framework of Reference for Languages (CEFR) to describe non-native listener and speaker proficiency. Using a "generate-evaluate-regenerate" loop with multiple LLM age
arXiv:2607.22118v1 Announce Type: new Abstract: Interactive virtual patients driven by large language models (LLMs) offer scalable solutions for medical communication training, such as breaking bad news. However, designing their emotional expressiveness remains a challenge. This paper presents an AI-driven virtual patient framework combining LLM dialogue with real-time facial animation in virtual reality (VR). We conducted an exploratory, formative evaluation with seven medical experts to gather early feedback and elicit design requirements. The evaluation focused on how variations in facial expression intensity affect perceived realism and the virtual patient's emotion intelligibility. While descriptive quantitative ratings remained baseline across conditions, qualitative interviews provided deep insights into how experts perceive virtual emotional cues. The findings suggest that experts evaluate emotional realism holistically through multiple verbal and non-verbal channels; isolated
arXiv:2607.22005v1 Announce Type: new Abstract: AI methods promise to support autism spectrum condition (ASC) diagnostics in adults, a complex and time-consuming process, that is characterized by a shortage of specialized clinicians. To date, clinicians' needs and their interaction with such AI-based support remain underexplored. Our work aims to develop and evaluate an AI-based clinical decision support system (CDSS) for ASC assessment, and to investigate how it impacts clinicians' decision-making. By interviewing clinicians of varying experience levels, we identified five challenges and derived design strategies. Based on that, we developed SIT-CARE, a CDSS, which provides AI-based recommendations and data visualizations of clinically relevant nonverbal behavior. Through an evaluation study with newly recruited clinicians, we found that SIT-CARE led to different decision paths in regard to the ASC assessment, which are reflected in clinicians' mental models and decision changes. Over
arXiv:2607.21777v1 Announce Type: new Abstract: Artificial intelligence (AI) has become part of scientific inquiry. Scientists use AI to observe and measure phenomena, to identify patterns in data, and to build models. As AI moves into scientific inquiry, it gains relevance for science education: students should learn how AI is changing scientific practices, ideally by engaging in AI-integrated scientific inquiry themselves. How to design such instruction, grounded in authentic scientific practice rather than taught as a standalone topic, remains an open question. In our vision, which we describe in this article, AI is treated as a set of scientific instruments that students use within the scientific practices described by the Next Generation Science Standards. Each instrument is a genuine scientific tool, pedagogically bounded: its controls are simplified while its core scientific function is preserved. The approach has two aims: engaging students in authentic scientific inquiry, and