Abstract:

[Purpose/Significance] Under the strategic background of national cultural digitization and the high-quality development of public services, artificial intelligence generated content (AIGC) has become a core engine driving the digital and intelligent transformation of galleries, libraries, archives, and museums (GLAM). While AIGC offers unprecedented opportunities for content production and knowledge dissemination, current implementations often suffer from fragmentation, leading to new "data islands" and service barriers. Unlike previous studies, which treat GLAM institutions as a homogeneous whole, this paper aims to clarify the differentiated application paths of AIGC by distinguishing the unique "resource-technology-service" logic of each institution type. It seeks to reveal the structural causes of current collaborative dilemmas and construct a systematic collaborative development mechanism. This research is significant for breaking down institutional barriers, promoting the deep integration of cultural resources, and guiding GLAM institutions to shift from isolated technological upgrades to a clustered, symbiotic development model. [Method/Process] Adopting a digital ecosystem perspective, this study constructs a "Resource Attributes - Technology Adaptation - Service Goals" framework to systematically analyze the heterogeneous characteristics of the four institution types. The analysis reveals how distinct data morphologies - ranging from structured texts in libraries and semi-structured records in archives to multimodal artifacts in museums and unstructured works in art galleries - fundamentally dictate the differentiated deployment of generative text or vision models. By examining core capabilities including intelligent content twinning, editing, and creation, the study demonstrates how service goals strictly regulate technical choices: the emphasis on "access" and "trust" in libraries and archives necessitates technologies that ensure semantic accuracy and historical authenticity, whereas the pursuit of "experience" and "creativity" in museums and art galleries favors generative tools for immersive interaction and open-ended aesthetic expression. [Results/Conclusions] To address the identified challenges of fragmented development, the study proposes a tripartite collaborative development architecture consisting of a "Front-end Resource Layer," a "Mid-platform Technology Layer," and an "End-user Service Layer." The Front-end Resource Layer focuses on constructing a unified multimodal data foundation and standardized semantic ontology to bridge the semantic gap between heterogeneous institutional data. The Mid-platform Technology Layer advocates for the co-construction of an industry-specific general large model and a knowledge reasoning engine; by sharing API interfaces and computing power, this layer solves the high technical threshold and cost issues for smaller institutions, acting as a ubiquitous "industry capability hub." The End-user Service Layer aims to build a one-stop knowledge exploration portal and cross-domain expert workbenches, eliminating service isolation and creating integrated cultural scenarios. The study concludes that GLAM institutions must transition from "cultural containers" to "knowledge engines" through this architecture. Future research should further focus on copyright ethics, algorithmic governance, and new modes of human-machine collaboration to ensure the sustainable and trustworthy development of the digital cultural community.

Key words: AIGC, artificial intelligence generated content, GLAM, collaborative development