Steve is an AI career coaching platform that turns a resume and insights from an AI-enabled chat with a user into a personalized skill gap report and upskilling roadmap. The platform suggests a personalized course plan, supports continuous learning, and helps shape the user’s career trajectory. Steve is built around schema-constrained JSON artifacts and a configurable career-tree ontology. The system compares confirmed skills against role-specific requirements, prioritizes gaps (critical/important/beneficial), and translates the analysis into embedding-based queries over the course index. Steve has three personas (Interview Coach, Resume Evaluator, and Career Coach) that provide concise feedback tailored to the user’s goals and context. Steve also supports speech Input/Output (I/O) via Whisper-based speech-to-text and a dual-voice text-to-speech layer, enabling users to talk to Steve. The platform offers flexible adaptability across institutions, enabling them to configure deployments by substituting their own ontologies and course catalogs. Our demo uses STEM trajectories as a case study, but the pipeline is domain-agnostic by design. Users can edit inputs, check speech recognition accuracy, and observe consistent updates, illustrating a reproducible, human-in-the-loop pattern for deploying LLMs in career guidance. Steve is currently in its alpha stage and available for demonstration.

This paper reports on an experimental study that examined user perceptions of Amazon Alexa's personality, as well as influences of stressful or non-stressful interactions on personality perceptions. The study relied on the Five-Factor Model and the Stereotype Content Model personality frameworks. An online data collection instrument was designed to give Alexa's users (N=50) stressful and non-stressful tasks followed by questions related to Alexa's personality. The assumption was that stressful and non-stressful tasks would frame a user's preference for Alexa's personality. Quantitative data of the participants? ratings of Alexa's responses, and qualitative comments about their ratings, experiences, and thoughts about Alexa were collected and analyzed using descriptive statistics, tests of association, and thematic content analysis. The findings indicated that while the majority of users appreciate Alexa's personality as a way of making interactions more personal and enjoyable, some users prefer their conversational agent to be efficient, robotic-like, and devoid of a personality that might cause attachment. The participants described Alexa as competent in terms of information content and usability, and warm in terms of its personality manifestation, but wanted their ideal Alexa to be even more competent and warm. After experiencing stressful and non-stressful experimental conditions, the participants appreciated Alexa's highly competent and highly warm responses, while disparaging low-warmth/high-competence responses. The participants' comments indicated that after stressful tasks, they were more generous in their assessment of Alexa's performance, and valued manifestations of Alexa's warmth higher than after non-stressful tasks. Additionally, after the second group of tasks/towards the end of the study, the participants tended to score low warmth/high competence responses higher, which might indicate a slight preference for competence after longer interactions or a user's fatigued state. The implications of the findings on the design of conversational interfaces and study limitations are discussed.