Trust is a fundamental concept in human decision-making and collaboration that has long been studied in philosophy and psychology. However, software engineering (SE) articles often use the term trust informally; providing an explicit definition or embedding results in established trust models is rare. In SE research on AI assistants, this practice culminates in equating trust with the likelihood of accepting generated content, which, in isolation, does not capture the full conceptual complexity of trust. Without a common definition, true secondary research on trust is impossible. The objectives of our research were: (1) to present the psychological and philosophical foundations of human trust, (2) to systematically study how trust is conceptualized in SE and the related disciplines human-computer interaction and information systems, and (3) to discuss limitations of equating trust with content acceptance, outlining how SE research can adopt existing trust models to overcome the widespread informal use of the term trust. We conducted a literature review across disciplines and a critical review of recent SE articles with a focus on trust conceptualizations. We found that trust is rarely defined or conceptualized in SE articles. Related disciplines commonly embed their methodology and results in established trust models, clearly distinguishing, for example, between initial trust and trust formation and between appropriate and inappropriate trust. On a meta-scientific level, other disciplines even discuss whether and when trust can be applied to AI assistants at all. Our study reveals a significant maturity gap of trust research in SE compared to other disciplines. We provide concrete recommendations on how SE researchers can adopt established trust models and instruments to study trust in AI assistants beyond the acceptance of generated software artifacts.