Conference Agenda

The opportunities offered by artificial intelligence (AI) and machine learning (ML) for the archive sector have been addressed in several scholarly articles in recent years. Some of the articles describe projects (e.g. Carter et al., 2022; Han et al., 2022) featuring a collaboration between archivists, generally defined for this paper as persons working in the archival sector, and technologists, defined as persons with a professional technical background. Some other archival projects have been undertaken by computer scientists or digital humanities scholars without the involvement of archivists (e.g. Luthra et al., 2022). Providing a qualitative angle, some articles have looked at digital archivists’ opinions on the implementations of AI techniques in archives (e.g. Cushing & Osti, 2023). Finally, more general articles have been published, both by archivists and technologists on how AI may be implemented in the archival sector (e.g. Colavizza et al., 2022; Hutchinson, 2020; Sabharwal, 2017).

This paper presents a qualitative analysis of how the perspectives of archivists and technologists on AI in archives are presented in a selection of recent articles. The selection includes both articles describing case studies, such as project descriptions and interview studies, as well as literature reviews. The articles are categorized according to the type of project described or suggested: articles written from an archival perspective, articles written from a technologist perspective, and articles that describe or propose joint projects run by archivists and technologists in cooperation. Differences can be observed in these research articles regarding 1) how archival expertise is valued, 2) the proposed importance of archival theory for successful AI implementation and 3) the degree of influence ascribed to the archivists in collaborations.

The results indicate that viewpoints clearly differ depending on the professional role and background of the contributors to the articles. Articles written from a technologist perspective are more likely to criticize archivist work, and in some cases even blame them for obstructing access and “perpetuating silences” in archives (Luthra et al., 2022). Randby & Marciano (2020) describe the goals of AIC (Advanced Information Collaboratory), a project in which Randby is involved, as aiming towards information professionals learning “to think computationally and rapidly adapt new technologies”; the article goes through a computational workflow without further mentioning archivists on a larger scale.

Articles written by archivists, on the other hand, emphasize the importance of incorporating archival principles and taking advantage of archivists’ knowledge in the AI implementation process (Hutchinson, 2020). Cushing & Osti (2023) highlight the expertise of archivists and stress how their professional background is a requisite to control AI decision-making. An important distinction is also that articles published in archivist journals such as Murphy et al.’s (2015) highlight the perspective of archivists by portraying them as subjects (“archivists”) which is contrasted with a passive portrayal of the technological aspects (“technology” instead of “technologists” or “machine learning experts”). Other articles point out the challenges of using AI in archives; for example, Jaillant & Caputo (2022) mention “ethical challenges” and problems with bias in AI. Cushing & Osti (2023) describe how participants in their study (archival experts) are confident about the possibilities of AI, but also skeptical about practical integration into their work. Lee (2018) argues that certain AI tools are not supportive of the “holistic view” that archivists have of their work.

Those articles that present a mutual collaboration between archivists and technologists highlight the importance of combining the expertise of both fields for successful AI implementation (e.g. Murphy et al., 2015; Carter et al., 2022; Han et al., 2022). Poole & Garwood (2018), an information scientist and computer and informatics researcher respectively, call for the involvement of archivists and librarians in Digital Humanities projects. Tsabedze (2023) offers an important perspective on archival professionals in Eswatini, highlighting the need for digital education; Tsabedze argues that the interview participants in Eswatini are afraid to lose their jobs without proper training. Nevertheless, Marciano et al. (2018) are positive about the disciplines achieving more together than each would have on their own. As a contrasting addition, Jo & Gebru (2020) argue not for implementing AI technologies in archives but for implementing archival expertise in AI development. They propose that archival document collection practices can inform data collection in sociocultural ML, because archivists possess the language and procedures to address issues of consent, transparency, inclusivity etc.

Finally, Marciano et al. (2018) believe that archival expertise of the future will involve knowledge of digital systems. Sabharwal (2017) suggests that in the future, the distinction between archivists and technologists will not be as clear anymore. Jaillant & Caputo (2022) also highly encourage collaboration between archivists and technologists in order to address future challenges. Cushing & Osti (2023) suggest that AI technology in archives can even change how archivists describe “digital archival expertise”. In conclusion, our findings suggest that there is a great diversity of opinions; as Poole & Garwood (2018) suggest, more research is necessary on how the collaboration between professions, as well as implementation of AI in archives, can be improved.

1. Introduction

Responding to the DNHB’s call for innovative approaches using digital tools and methods in teaching, this we propose a paper and presentation that shares results and key insights from the upcoming educational workshop on the use of Artificial Intelligence, specifically Language Model Interfaces (LMIs) and Generative Pretrained Models (GPT), e.g. ChatGPT. The workshop, funded by Fjölbrautaskóla Suðurlands (South Iceland College), presents technology as an integral tool for supporting English language teaching and learning in the Icelandic educational context, where learners are adept at understanding English through media, yet struggle with formal academic language. Bridging this gap is imperative for equipping students with the necessary skills to achieve academic and professional success.

1. Workshop Context

Recent research in Iceland (cf. (Jeeves, 2022; Prinz & Arnbjönsdóttir, 2021) has highlighted the influential role of informal media in English language acquisition, but the potential role of GPT technology as a formal education tool has not been thoroughly investigated. This study expands upon the existing research by exploring how actively engaging students with AI can help scaffold the execution of complex language tasks, such as academic writing, by providing a practical and interactive learning environment reflecting the digital nature of contemporary education.

The workshop develops teachers' capacity to apply GPT tools as both a supplementary aid and a substantial pedagogical tool. Emphasis is placed on facilitating students’ understanding of key communication principles by focusing on content – students’ own opinions, perceptions, and arguments – and discourse, i.e., the forms these ideas naturally take. We hypothesize that incorporating these natural language processing tools will enable students to navigate the intricate landscape of text as a social interaction, while reducing emphases on language formalism.

We aim to deepen students’ understanding of the “norms based on social purpose and the expectations of a community” (Prinz & Arnbjörnsdóttir, 2021) and empower them to convey their views, opinions, and observations successfully. In addition, we seek to empower educators to effectively implement this technology and facilitate meaningful communication with their students.

1. GPT Language Models and their Applicability in Language Teaching.

When considering Generative Pretrained Transformer (GPT) models in education, it is imperative to differentiate between the overarching concept of Artificial Intelligence and the specific functionalities of GPT. AI represents a broad field with various applications, while GPT models, such as Open AI’s ChatGPT, represent a more focused area within this domain. These models belong to a larger framework of neural network-based software characterized by their attention mechanism. This mechanism ensures that the output generated is not random. Instead, it adheres to a specialized set of rules that increase the likelihood that the output produced is comprehensible to humans.

To illustrate, consider the sentence, “The ocean’s color is pink.” While grammatically correct, it conveys false information and thus lacks real-world reference. GPT models, trained on vast amounts of text, typically avoid such ‘hallucinations’1 by correlating terms like ‘ocean’ and ‘blue’ via statistical analysis: this word-pair appears in closer proximity more frequently than the pair ‘ocean’ and ‘pink.’ Although often successful, this programming is not immune to generating false statements, underlining the importance of understanding its capabilities and limitations.

GPT models can be viewed as interactive texts that expect users to provide expert input or ‘prompts’, to then receive an output which they can respond to with further prompting. This interaction highlights three skills that teachers and students require:

1. The ability to understand and describe tasks logically, such that a computer program can efficiently produce the required outcomes.

2. The ability to critically evaluate the relevancy, accuracy, and utility of the responses generated.

1. The ability to gauge the depth of users’ comprehension by analyzing the quality of both the prompts and the users’ responses to AI output.

The workshop offers teachers guidelines for developing these skills by demonstrating their usefulness for developing competencies outlined in the Icelandic National Curriculum. Given the interactive nature of GPT models and their text-processing capabilities, we aim to explore how these tools support higher-level tasks, i.e. essay writing and information processing. We furthermore aim to stimulate an evidence-based discussion on how teachers and students currently use this tool to support language development with a lookout on ethical guidelines for general use.

1. English Language Education at Fjölbrautaskóla Suðurlands (FSu).

English at FSu is taught across three levels of competence, using an approach that integrates students from different tracks into one class. This creates an inclusive learning environment where students with diverse educational and professional aspirations, neurodivergent diagnoses, and varying degrees of English proficiency learn together. This diversity underscores the importance of implementing malleable learning tools, like GPT models, into the curriculum.

1. Workshop Outcomes and Future directions.

Our paper will present the FSU workshop and observed affordances and constraints when harnessing the potential of GPT language models for English language teaching. This initiative aligns with the innovative spirit called for by the DNHB while also addressing notable challenges in the Icelandic context. The digital intervention helps narrow the gap between students’ informal familiarity with English and the formal academic proficiency required for success in higher education and professional pursuits.

The workshop focuses on developing educators’ competencies for interacting with GPT models as the first step towards improving our learners’ experience. The ability to efficiently formulate prompts and critically evaluate AI-generated content is a fundamental aspect of digital literacy in the 21st century. Insights gained from this workshop can provide a valuable blueprint for how such technologies can be ethically and effectively integrated into Icelandic educational settings.

GPT modeling presents an opportunity to support a diverse range of learning needs. Strategies shared in the workshop support both language and democratic participation by making advanced learning tools accessible to all students, regardless of their background or proficiency. Thus, the FSU workshop can serve as a model for other institutions grappling with similar challenges in language education and contribute to the ongoing conversation about the role of AI in Icelandic education, particularly in terms of how educators can leverage technology to enhance learning and prepare students for the evolving demands of the 21st century.

Facilitating access to archives via metadata creation and enrichment can be a monumental task for large archival collections. The past decade has witnessed an increasing use of artificial intelligence (AI) and machine learning (ML) to assist in these tasks in automatic or semi-automatic workflows (Colavizza et al., 2022). While technologies such as named entity recognition and topic modeling are useful in many different archival contexts, they have found special relevance for colonial archives and archives pertaining to underrepresented communities. Recent projects have for example explored the possibilities of using AI and ML to optimize information discovery in under‑utilized, Holocaust‑related records (Carter et al., 2022), extract mentions of underrepresented people in Dutch colonial records (Luthra et al., 2023), and transform Indigenous and Spanish colonial archives originating from Mexico into Linked Open Data repositories (Candela et al., 2023).

This paper presents, firstly, an overview of current state-of-the-art approaches for AI in archives, and secondly, a project in progress intended to align with the three first goals of the ongoing InterPARES Trust AI project (Duranti et al., 2021): to identify specific AI technologies that can address critical records and archives challenges; determine the benefits and risks of using AI technologies on records and archives; and ensure that archival concepts and principles inform the development of responsible AI. The opportunities offered by these technologies are contrasted with the risks of using automated approaches in general and AI in particular for improving access to archives. The paper also discusses a related approach for increasing discoverability in collections, i.e. semantic search, and compares the pros and cons of these approaches. Furthermore, the potential uses of generative pre-trained transformers (GPTs) for both indexing and retrieval, and the risks associated with these, are addressed.

Bringing the overview to a Swedish context, the paper describes an ongoing project aiming to explore the risks and possibilities of using AI and ML to provide up-to-date, enriched metadata for Swedish archives pertaining to the Sámi, an Indigenous population of the Nordic countries. Managing Indigenous heritage material demands distinct sensitivities that acknowledge the colonial past and the voice of the community in creating and maintaining the cultural record. While AI technologies can be a means for promoting cultural heritage from a Sámi perspective, using them without properly addressing colonial aspects runs the risk of reifying and perpetuating the colonial dynamics of past history writing. When properly applied, however, the hope is that these technologies may be of assistance in the remediation of the digital cultural record to counter the colonial dynamics of the analog cultural record (see Risam, 2019).

The project aims to gauge the potential of these technologies for improving the searchability and usability of records pertaining to the Sámi, of both colonial and Indigenous origin. The research is intended to follow an iterative approach, with continuous evaluation and feedback from experts and end users ensuring the suitability of the metadata generated by the technologies and its usefulness in facilitating search. The expected outcome of the project as a whole is a framework for assessing how to safely and effectively implement selected AI techniques in archival and related institutions while maintaining authenticity.