Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
This master’s thesis examines how Artificial Intelligence (AI) may support digital
workflows in small- to medium-sized archives, particularly in relation to repetitive and
time-consuming tasks while maintaining professional oversight and historical
accountability. The study adopts an exploratory qualitative approach, combining an
introductory research review, along with semi-structured interviews with archival
professionals and observational studies of archival workflows. The findings revealed
fragmented digitization processes, limited staffing and large volumes of non-digitized
material, creating both opportunities and challenges for a possible AI adoption. The study
identifies areas where AI tools could relive repetitive tasks such as metadata generation,
classification and transcription. At the same time, archival professionals express concerns
related to reliability, traceability, ethical responsibility and the preservation of human
expertise. In response to this, the thesis develops a modular and value-driven framework
intended to support gradual and responsible AI implementation in archival environments.
The proposed framework emphasizes human oversight, traceability, confidence
thresholds, feedback loops and clearly defined exit strategies. Grounded in the theories of
Human-Centered AI (HCAI) and Value Sensitive Design (VSD), the framework positions
AI as a supportive tool rather than as a fully autonomous system. A preliminary
practitioner review suggests that the framework may have practical relevance within
archival contexts, while also highlighting challenges related to institutional readiness,
technical competence, long-term maintenance and resource allocation. The assessment
further emphasizes the importance of organizational trust, policy development and
continuous staff involvement during implementation processes. The proposed framework
contributes a practical and context-sensitive foundation for discussions surrounding
responsible AI adoption in the cultural heritage sector. By addressing both technological
possibilities and organizational realities, the study provides a basis for future long-term
implementation research.
2026. , p. 74
AI-implementation, cultural heritage sector, digitization, framework, photographic collection