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Background/Objectives: Ghosting is an emerging phenomenon in technology-mediated relationships; the lack of theoretical cohesion surrounding it justifies systematizing the available evidence to propose an explanatory model for this form of relating. The primary objective of this systematic review was to propose an explanatory model to articulately understand the phenomenon of ghosting within the context of digital communication. Methods: The PRISMA statement was used to formulate the criteria for selecting the 21 articles analyzed, which were sourced from the Scopus and Web of Science databases. The elements considered were: study period, area of study, document type, article language, keyword, publication stage, and open access. The only exclusion criterion was that, although the article included the keyword, it did not demonstrate psychological content related to ghosting. The included studies were evaluated by three reviewers, who worked independently using respective matrices in Microsoft Excel. To present and synthesize the results, a flow diagram of the selection process and a table of the articles included in the study were used. Results: The analysis identified four elements upon which the study was developed: (i) unilateral relationship dissolution and communication avoidance; (ii) communicative bond and digital relationship; (iii) relational exclusion and loss of belonging; and (iv) social isolation and psychological threat. The main limitation was that, although the term “ghosting” has been used since 2006, the number of related studies remains limited. Conclusions: Based on a review and systematization of the evidence, an explanatory model of the ghosting phenomenon within the context of digital communication was developed, termed the ‘silent relationship dissolution model’.

Generative artificial intelligence is increasingly being adopted in doctoral education to support writing, literature synthesis, translation, feedback, research planning, and administrative work. While emerging scholarship has examined generative AI in higher education, academic integrity, and student writing, its specific implications for doctoral supervision remain comparatively underexplored. This review argues that generative AI should not be understood merely as a technical aid, but as a socio-technical force that may reshape ethical boundaries, feedback practices, doctoral student agency, and supervisory relationships. Drawing on literature from doctoral education, supervision studies, academic writing, feedback literacy, and AI-mediated learning, the review examines three interrelated thematic areas of AI-augmented doctoral supervision. First, it considers ethical issues related to authorship, originality, academic integrity, transparency, privacy, confidentiality, bias, data protection, and overdependence. Second, it analyses how generative AI may influence feedback preparation, interpretation, timing, and dialogic use in doctoral writing and research development. Third, it explores implications for supervisory relationships, particularly trust, power, responsibility, emotional support, and autonomy. The review also discusses institutional implications, including AI literacy, supervisor development, doctoral policy, and responsible use guidelines, and positions generative AI as a human-centred support mechanism rather than a substitute for supervisory judgement, ethical responsibility, or relational mentoring.