Research Document No. 11 Towards Archival Science 2040 Preparing archivists for the age of Artificial Intelligence, Data Science and Archival Algorithmic Governance
PDS & Ged/A | Research Notes
Research Document No. 11
Towards Archival Science 2040
Preparing archivists for the age of Artificial Intelligence, Data Science and Archival Algorithmic Governance
10 June 2026
🌎 Open Science
This Research Note presents an open reflection on the future of archival education. Rather than proposing a definitive curriculum, it offers a research agenda developed through the ongoing work of the PDS & Ged/A Research Group. Like every document in this series, it represents an invitation to discussion, collaboration and collective knowledge building within the international archival community.
📚 What have we learned so far?
Throughout the first ten Research Notes, our research programme gradually evolved.
We argued that digital preservation must be systemic.
We proposed the concept of Digital Archival Ecosystems.
We reinterpreted the OAIS Reference Model from an archival perspective.
We expanded the meaning of the Chain of Archival Digital Custody.
We explored the convergence between the European archival tradition, the iSchools movement and Computational Archival Science.
More recently, we investigated intelligent agents as active participants within archival environments.
Finally, we proposed Archival Algorithmic Governance as a possible framework for governing records, data, algorithms and institutional memory.
Looking back, one conclusion gradually became unavoidable.
Digital transformation is changing not only archival institutions.
It is changing archivists themselves.
🔄 How our research question evolved
Our questions have continually evolved.
First we asked:
How should digital records be preserved?
Then:
How can authenticity and documentary continuity be maintained?
Later:
How should intelligent agents operate within Digital Archival Ecosystems?
Most recently:
How can algorithmic processes be governed responsibly?
Today another question naturally emerges.
How should universities prepare future archivists for a world shaped by Artificial Intelligence, Data Science and computational infrastructures?
Perhaps this represents one of the most important questions facing Archival Science today.
Perhaps the greatest transformation is educational
For generations archival education has been organised around well-established foundations.
Diplomatics.
Appraisal.
Arrangement.
Description.
Records Management.
Preservation.
Those foundations remain indispensable.
Yet the professional environment has changed dramatically.
Archivists increasingly collaborate with:
Artificial Intelligence specialists.
Data scientists.
Software engineers.
Cybersecurity professionals.
Information governance teams.
Cloud infrastructures.
Digital preservation platforms.
Perhaps archival education must now evolve accordingly.
📓 Laboratory Notes
During one seminar a graduate student asked a simple question.
"Professor, do I need to learn programming to become an archivist?"
Our answer surprised everyone.
"Perhaps the better question is: how much computational knowledge will future archivists need in order to remain archivists?"
That small shift transformed our entire discussion.
🏛️ A Conceptual Proposal
We have begun exploring the hypothesis that future archivists will become boundary professionals.
Professionals capable of collaborating across multiple disciplines while preserving a strong archival identity.
Future archivists may increasingly work alongside specialists in:
Archival Science.
Information Science.
Data Science.
Computer Science.
Law.
Public Administration.
Digital Humanities.
Artificial Intelligence.
Importantly, interdisciplinarity should strengthen—not replace—Archival Science.
The future will require more Archival Science, not less
A common assumption suggests that Artificial Intelligence will reduce the importance of archivists.
Our research suggests precisely the opposite.
The more intelligent computational systems become,
the greater the importance of understanding:
Authenticity.
Provenance.
Archival context.
The Chain of Archival Digital Custody.
Evidence.
Institutional accountability.
Long-term preservation.
Public trust.
Artificial Intelligence does not diminish archival principles.
It increases their importance.
💡 One of our research findings
Digital transformation expands the archivist's professional responsibilities.
It does not replace the intellectual foundations of the discipline.
New competencies for future archivists
Future archival programmes may increasingly incorporate subjects such as:
Artificial Intelligence.
Data Science.
Computational Archival Science.
Generative AI.
Intelligent Agents.
Prompt Engineering.
Retrieval-Augmented Generation (RAG).
Vector databases.
Knowledge Graphs.
Local AI environments such as Ollama.
Explainable AI.
Trustworthy AI.
Archival Algorithmic Governance.
Privacy legislation.
Information Security.
Algorithmic auditing.
Data curation.
PREMIS.
OAIS.
Records in Contexts (RiC-CM).
Linked Open Data.
Open Science.
The objective is not to transform archivists into software engineers.
The objective is to enable meaningful interdisciplinary collaboration.
New professional responsibilities
Archivists will continue managing records.
Increasingly, however, they may also participate in decisions involving:
archival policies for intelligent agents;
metadata quality;
AI training datasets;
sensitive personal data;
privacy compliance;
algorithmic transparency;
explainability;
documentary auditing;
preservation of institutional AI models;
governance of automated documentary processes.
The profession itself is expanding.
⚖️ An Archival Dilemma
When an intelligent agent automatically classifies records,
who should validate that decision?
The software engineer?
The data scientist?
The institutional manager?
Or the archivist responsible for documentary governance?
We do not yet know.
But Archival Science should participate in answering that question.
🔬 An unexpected outcome of our seminar
The more we collaborate with Computer Science researchers,
the more we realise that Archival Science possesses concepts that Artificial Intelligence increasingly requires.
Authenticity.
Provenance.
Context.
Evidence.
Documentary continuity.
Institutional trust.
Perhaps Archival Science has far more to contribute to Artificial Intelligence than we initially imagined.
Universities may also need to change
Adding a single Artificial Intelligence course will probably not be sufficient.
Perhaps archival education itself requires transformation.
Interdisciplinary laboratories.
Joint research projects.
Collaborative teaching.
Hackathons.
Open Science initiatives.
Digital preservation laboratories.
AI experimentation environments.
Partnerships with Computer Science departments.
Applied research.
International collaboration.
Above all, universities may need to rethink how future archivists learn.
🤔 A hypothesis we partially revised
Initially we believed future archivists simply needed to learn new technologies.
Today we increasingly suspect that archival education itself requires a new intellectual framework.
🌱 A hypothesis under construction
Perhaps the archivist of 2040 will not be defined by the technologies they master.
Rather, future archivists may be recognised for their ability to integrate classical archival principles with intelligent digital environments.
That integration—not technology alone—may become the defining characteristic of the profession.
🧭 Research Agenda
During the coming years we intend to investigate several questions.
How should archival curricula evolve?
How should Artificial Intelligence be taught to archivists?
How should archival principles be introduced to computer scientists and data scientists?
How can interdisciplinary teams be strengthened?
How can Archival Science preserve its disciplinary identity while expanding internationally?
How can Computational Archival Science continue developing within Latin America while engaging with the broader international community?
Which competencies should become essential for every archival programme by 2040?
🔭 Looking toward 2040
If our hypotheses prove correct,
future archivists may become internationally recognised not only as records professionals,
but also as specialists in digital preservation,
data governance,
algorithmic transparency,
Artificial Intelligence governance,
institutional accountability,
and public trust.
Perhaps that future has already begun.
💬 Let's continue the conversation
How do you imagine the education of future archivists?
Which competencies should remain essential?
Which new capabilities should become part of archival education?
How can Archival Science preserve its identity while engaging deeply with Artificial Intelligence, Data Science and computational infrastructures?
We would genuinely value your perspective.
Your ideas may contribute to the next stage of this collective research programme.
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