Generative artificial intelligence has changed the conditions under which users encounter and evaluate machine-produced communication. In systems based on large language models, conversational output is no longer merely functional; it can be fluent, context-aware, and socially legible, inviting judgments that extend beyond usefulness, accuracy, or efficiency. Yet the literature still tends to treat anthropomorphism, perceived authenticity, and trust as largely separate constructs, without sufficiently explaining how human-like cues become credible in the eyes of users and how this credibility shapes trust. To address this problem, the present structured literature review synthesizes recent research on anthropomorphism, authenticity-related evaluations, and trust in AI-mediated interaction, with particular attention to conversational AI, generative AI, and LLM-based systems. A structured search in the Web of Science Core Collection identified 655 records; after duplicate removal, screening, and eligibility assessment, 96 studies were retained for qualitative synthesis. The analysis identifies several interrelated thematic patterns, including anthropomorphic cues and social presence, authenticity-related mechanisms in AI-mediated communication, trust formation and calibration, boundary conditions, and behavioral responses to conversational AI. On this basis, the review develops an integrative conceptual framework and proposes perceived anthropomorphic authenticity as a review-derived construct for examining how users evaluate human-like AI communication in relation to trust. The study contributes to human–AI interaction research by clarifying the role of authenticity-related evaluations between anthropomorphic cues and calibrated trust and by identifying conditions under which human-like communication may be associated with trust, skepticism, overreliance, or trust erosion.

This paper presents the design and development of four educational platforms intended to support industrial logistics students in understanding industrial data acquisition concepts and technologies, using data acquisition and control principles from real industrial applications. To differentiate from other platforms, several features were combined: use of industrial grade automation devices and engineering software environment, while focusing on the monitoring part of an automation system by using data acquisition (DAQ) modules rather than PLCs. A common hardware infrastructure was implemented for all platforms, consisting of a multifunctional printed circuit board (PCB) and a unified communication solution for interfacing with a computer. For each platform, different industrial data acquisition and control modules were used, depending on the types of sensors and actuators. A modular software framework was developed using LabVIEW, enabling the creation of dedicated applications tailored to the educational objectives of each system. The resulting platforms have been implemented in a laboratory in POLITEHNICA Bucharest University, obtaining small-scale industrial monitoring and control systems, for third-year students from Industrial Logistics.