A total of 57 respondents participated in the survey, with 44 (77.2%) working at university libraries, 11 (19.3%) at college libraries, and 2 (3.5%) at other institutions. When participants were asked to assess their own competence with AI-based tools, the majority indicated that they were familiar with available tools “to some extent” (68.4%), while 22.8% felt they knew the tools “fairly well”. 8.8% stated that they had no knowledge of these tools, and none considered their knowledge to be “very good.” (
How well do you think you know the AI-based tools available? (Very well / Quite well / Somewhat / Not at all).
The survey revealed that artificial intelligence receives significant attention at participants’ institutions, with 61.4% responding “to a high degree” and 31.6% “to a moderate degree.” Only 5.3% replied “to a small degree.” (
Is artificial intelligence getting attention at your institution? (To a high degree / To a moderate degree / To a low degree).
Many participants had already received questions about the use of AI-based tools (77.2%) from colleagues, researchers, and students. Furthermore, 36.8% of respondents had already taught or provided guidance on the use of AI-based tools. AI-based tools mentioned in this context included ChatGPT (56.1%), Rayyan (35.1%), and Keenious (24.6%). Several tools that were repeatedly mentioned in user comments included Grammarly, Transcribus, Litmaps, Iris, ASReview, and Elicit. Similarly, the responses regarding potential benefits of using AI-based tools also diverge. On one hand, there are benefits related to tools that “provide suggestions and ideas for further project development” (66.7%) and “assist with writing” (54.4%). On the other hand, there are benefits associated with tools that offer “increased efficiency” (63.2%) or support “better data analysis” (22.8%). “More accurate results” and “improved decision-making” were also considered as benefits (8.8% each). (
What advantages do you see in using AI-based tools? (Select all that apply) (Increased efficiency / More accurate results / Better data analysis / Improved decision-making / Assists with writing / Provides input and ideas for further project development / Other (please specify)).
The majority of participants (87.7%) identified ethical challenges associated with the use of AI-based tools, with 54.4% being “somewhat concerned” and 33.3% being “very concerned.” (
What challenges do you see in using AI-based tools? (Select all that apply) (Difficulties in understanding the technology / Limited availability of tools / Lack of resources for training and implementation / Integration with existing systems / Ethical challenges / Other (please specify)).
How concerned are you about the ethical implications of using AI-based tools in research? (Very concerned / Somewhat concerned / Not particularly concerned).
What ethical challenges do you see in the use of AI-based tools? (Select all that apply) (Copyright / Co-authorship / Plagiarism / Bias / Cheating / Other (please specify)).
In addition to ethical aspects, the difficulty in understanding the technology (64.9%) and a lack of resources for training and implementation (57.9%) were considered significant challenges when using AI-based tools. Integrating AI-based tools with existing systems (31.6%) and limited tool availability (28.1%) were also seen as obstacles (
The participants in the survey were also asked what types of training or resources they would prefer in order to improve their knowledge of AI-based tools. Collaboration with other library staff or researchers experienced with AI-based tools (66.7%), training programs (63.2%), workshops on artificial intelligence in research (63.2%), and training on specific AI-based tools (63.2%) were seen as valuable offerings. In their comments, participants expressed a desire for brief overviews of existing tools and examples for their use, the acquisition of specific tools for testing with staff and students, and basic education on how AI works. Several participants also focused on the risk aspect and “security clearance of available tools for various academic purposes”: “It is important that we preserve our critical thinking and avoid a ‘hallelujah’ approach!”
Finally, participants had the opportunity to provide their own comments on the use or thematic exploration of intelligent tools in PhD education. The speed of change and uncertain access to continually new tools were highlighted as significant challenges. There is a need for skills development, guidance, and a desire for a critical approach:
“Important to be able to distinguish the wheat from the chaff and choose a secure product for the right/suitable task.”
“Important to encourage sobriety: Some answers AI provides may seem impressive without actually being so.”
“Everything from AI must be treated with source criticism. Efficiency should not be confused with sheer laziness. Academic work will still be time-consuming!”
“The most important thing is probably that we keep up and see how we can use the tool in a good and sensible way as a resource in research.”
“There is a need for skills development among us working in the library. We need help and resources – it’s great if something comes on PhD on Track.”
Additional questions were asked about the national learning resource PhD on Track (
How important do you think it is that PhD on Track includes information about AI-based tools? (Very important / Quite important / Not particularly important).
What kind of information do you think should be included in PhD on Track? (Select all that apply) (Overview of different AI tools / Guidelines for using AI tools / Examples of using AI tools in research / Comparisons of different AI tools / Ethical aspects of using AI tools / Other (please specify)).
In their comments, participants recommend that PhD on Track should focus on a selection of the “most general” tools. Several propose a focus on “limitations of the various tools”, “examples of when AI doesn’t work.” Similarly, topics such as source criticism, information about the algorithms and sources used by AI-based tools, and data security when using AI are highlighted. 40.4% of participants suggest creating a separate module on artificial intelligence in PhD on Track, while others propose integrating information about AI-based tools into sections on Searching (66.7%), Writing (64.9%), Reviewing (57.9%), Copyright (45.6%), Data management (29.8%), and Co authorship (28.1%). In their comments, several participants mention the possibility of “creating a separate module on AI in an introductory phase when this is new and of great interest, and gradually integrating it into the other modules.”
According to the steps 3–5 in the thematic analysis method, the authors grouped the statements into 6 thematic categories, each containing aspects and perspectives of the participants wearing the red, the yellow and the green hat:
Theme 1: Technology
Theme 2: Knowledge about AI
Theme 3: Ethical issues
Theme 4: A need for more competence
Theme 5: The role of the library
Theme 6: Learning resources
We started with a look at the challenges, expectations and opportunities connected to rapidly changing technology, knowledge about AI, and ethics. Closely related to those themes are the need for more competence, the (future) role of the libraries – and the purpose and design of learning resources. Ethical issues were addressed throughout the workshop: When we looked at the material and notes from the workshop, we found that different aspects of ethics were addressed, and that mutually related themes were discussed, spanning from technology and knowledge, and from immaterial rights via plagiarism and source criticism to sustainability.
The answers linked to the keyword
Furthermore, this opens for issues with technical maintenance and changing legal rules. The first issue involves infrastructure that the library rarely has control over, and which is often expensive. The other points to the fact that existing laws may not be updated in line with current technological advancements and can thus be exploited by companies that want to position themselves in the market.
The technical aspect, highlighted by the participants of the workshop, contains elements in relation to where the information comes from, where the data that is fed into the system ends up and which companies are behind the services. In addition, as a reinforcement of possible bias, the English language and Western culture were pointed out to often set the tone in innovative technology. This was considered a greater challenge. Several of the participants were concerned about this, which they also related to ethical issues regarding ethnicity, gender, different languages and cultures. As the participants pointed out: “Language and the choice of words are never objective or neutral.” “The English language and American culture characterise the tools, and this might create problems in other languages and cultures.”
Another perspective that emerged from the design task regarding technology and development concerned the varying expectations participants had. The strategic aspect was central, as several participants expressed the view that the technological development within AI could not be stopped. In particular, there was an expectation that there would be more specific tools and not just general tools to support researchers and PhD candidates. There was also general agreement that the above-mentioned tools will be able to change the way research is conducted, for example processing large amounts of data. In conclusion, there were expectations that research institutions must be proactive to take action as artificial intelligence is developing rapidly and is always ahead of us with regard to ethics, pedagogy, and assessment.
When participants were asked what opportunities they saw in the new wave of AI services, the focus was primarily on research and teaching. Mentioned examples include using AI to check the quality of data, or give an overview of large quantities of materials. The feedback on opportunities also predicted a change in the way research will be done in the future, where proofreading, translation, disposition, and summarisation of knowledge will be more automated. AI was also seen as a potential sparring partner, offering unforeseen opportunities in writing support.
Most of the participants highlighted the challenge of general information on artificial intelligence quickly becoming outdated and the difficulty of conveying the complex nature of the topic. More specifically one participant stated: “Difficult to convey the complexity. People turn to websites for answers!”
Additionally, the participants expressed a need for more certainty about how to use artificial intelligence effectively. The participants agreed on the importance of defining and categorising the types of AI relevant to specific subjects and establishing a professional environment for their presentation.
The participants emphasised the difficulty of maintaining an overview and deciding which tools are relevant for presentation in library resources. This frustration is expressed in the distinctive statement: “How can we have the overview?” Some tools can be helpful, while others must be approached cautiously. Participants voiced their concerns: “Clarify pitfalls, show weaknesses.” Given the diversity of different policies regarding the access and use of artificial intelligence, the participants expressed uncertainty about how this could be conveyed.
Some of the participants were concerned with the complexity of legal aspects and copyright law when wearing the red hat: One group highlighted the “lack of laws and regulation” and emphasised the necessity “to take care of the copyright”. Another group mentioned that “tools are based on our work, and on our data, without us having insight into or control over the work”. As one group put it, “legal implications – do we understand everything?”, another was concerned with “intellectual property, personal data, plagiarism issues”.
One group mentioned that artificial intelligence-based tools implicate questions concerning “access, institutional policy, data processor agreement etc”. The implications of GDPR and the management of personal data were noted, with worries about “big tech, storage of data, all the profiles that are created”.
A red alert pointed out by some participants is that the use of AI-based tools may lead to plagiarism among PhD candidates. They express a concern: “Can an assignment be rejected?” Transparency is a keyword here. People are “insecure of guidelines, ethics, and legal issues”. It is also “an ethical challenge that lack of competence on artificial intelligence might implicate that we misinterpret the results and the starting point for them”.
AI-based tools are “developed by private companies for profit reasons”, and “commercially based systems might change the intended content without doing this in an academic way”. The problem is that we do not know “who is in charge of the content”.
Regarding evaluation of sources, there is obviously a need for transparency in the quality of text and data. There is an obvious need for guidance, as the libraries often experience patrons requesting literature and references that do not exist after using AI-based tools.
When they had a look at AI-based tools from another point of view, wearing the yellow hat and trying to indicate expectations and possibilities, several groups advocated good training and good websites as their main expectation regarding how to handle ethical issues: “Try to collect in one place what concerns ethical problems and limitations in AI-based tools and AI in general. Refer to that place when teaching different tools practically”. The groups suggested that it should be “clearly explained what source criticism implies for credibility with AI software” and that “good guidance in how to use/how not to use” the tools should be offered. There was a strong call to “link the guidance to ethical norms for good research practice” and to “promote the actual ideals of knowledge. For example, knowledge over publication, maturation over production. Give good examples of responsible use”.
With the green hat on their heads, the groups were expected to find (positive) possibilities concerning AI-based tools. Here, they pointed out that an ethical awareness might come through: “With good guidance in artificial intelligence, there is a good chance that ethics will become an important component for those who use tools”. “We can make ChatGPT better with our input (do we really have a responsibility here?)”, and “we can show the academic ideals in the face of AI: Originality, thoroughness, verifiability and so on”.
The discussion on ethical challenges and possibilities brings us to the next part, which is the need for competence enhancement for library staff.
Among the challenges pointed out by the groups is the need to “understand possibilities and limitations (i.e., to know what the tools are for)”. Further, there is a certain and “unpredictable risk of not being updated”, and of course, it is “difficult to maintain a competence which is sufficient”. Another issue is that there are “so many different tools, and it is difficult to have an overview of what you canNOT achieve with them”. It is also a “problem with provenance, and what you should trust when there is an increasing amount of fake news”.
Participants expressed uncertainty given the large quantity of tools. “There are a lot of tools. Which ones do we choose?”” Are they safe to use?” An important aspect of this is of course the rapid pace of development in AI technology: “Those who do not keep up with artificial intelligence will fall behind. This will cause big differences”.
To address these challenges, library employees clearly need enhanced competence regarding AI-based tools. Concerning expectations, some participants suggested the creation of “a discussion forum to raise awareness about tools and possibilities and limitations” and to share examples of best practice. “The technical aspects must be problematised,” they noted.
Looking at the opportunities, some groups indicated that one can help each other, and “use the skills of others to put together a good course”. They suggested “organise workshops for PhD candidates, and for their supervisors as well”. Of course, one has to train the trainers: There is a need for “workshops and training for librarians. We must be able to do it ourselves”. This implicates certain questions about time allocation and responsibility: “Who follows up?”
For academic libraries, the rise of AI brings along certain challenges, risks and concerns. Several participants highlighted the challenge that providing information and guidance on artificial intelligence entails responsibility in terms of selecting and evaluating specific (commercial) tools. This raises questions about the “boundaries between recognition and recommendations” and between information and “marketing”.” Presentation of such a complex topic: It is difficult to stay neutral”.
The role of the library is crucial in this context: Will the library be perceived as an “authority” with “credibility” and “reliability,” and what should be the “normative” nature of library learning resources? “Could the learning resource (i.e. PhD on Track) become an authority about something that the institutions do not agree on internally?” The question of competence comes with it: “Does one have enough expertise in the libraries to create good modules in a learning resource?” “Are librarians the right group to create such tools?”
“We expect people with technical expertise and those with a reflective and questioning approach to come together”: The library also plays a role in the development of artificial intelligence; it can “contribute to connecting academic principles to developments in artificial intelligence” and be involved in the development of policies from institutional to national levels.
Workshop participants also identified new opportunities in addressing artificial intelligence in research library learning resources: It can provide “greater confidence for the library if we can introduce artificial intelligence tools into education in a good way.” The focus on AI can be “positive for collaboration with other actors,” and “the library becomes involved in the disciplines.”
As the workshop was organised by several of the editors of PhD on Track and explicitly aimed to collect contributions to the development of the resource, some of the feedback focuses on the development of the website
The workshop participants identified several potential challenges when developing new content on artificial intelligence for learning resources aimed at PhD candidates and young researchers. The heterogeneity of the target audience makes it difficult to tailor the learning content to the users. “Challenges related to different academic traditions” and varying levels of prior knowledge and competency within the target group were emphasised. At the same time, the issue of creating false expectations was raised, such as assumptions of improved efficiency: “Artificial intelligence will create expectations, for example, about saving time, etc. But is that realistic?” “Can artificial intelligence be a time thief rather than a helper?” Furthermore, several participants highlighted the didactic consequences of extensive use of artificial intelligence. “Strong focus on the technical aspects, potentially overshadowing the educational aspect,” can lead to a de-prioritisation of learning and the development of fundamental skills, especially in the writing process: “One learns by writing; we must write ourselves,” “it is important to find one’s own voice, one’s personality (writing style).” Some participants expressed concerns that artificial intelligence may contribute to “intellectual laziness,” resulting in “uniform” and “fictionless” texts: “Everything ends up looking the same in the end.”
Similarly, participants’ expectations had a strong didactic focus. Research library learning resources “should have a critical, pedagogical approach that promotes responsible use and prevents misuse” and “enable people to engage with it in the best way possible (both find utility, motivate, and be critical),” “help people make informed choices.” A prerequisite for this is that the content is continuously updated: Users expect learning resources “to provide useful guidance that doesn’t become outdated, even if the tools do.” Existing skills should not be phased out either: New information about AI “must be balanced with information about ‘craftsmanship,’ including searching, writing, source criticism, etc.”
An online resource should also “acknowledge that different academic communities may have different usage/support needs” and provide “support for both PhD candidates and teachers and administrators.” To reach the target audiences, participants suggested to “invite PhD students to take part in the development” of learning resources, “hold webinars on artificial intelligence for PhD students,” and “follow up on the use of AI-based tools among PhD candidates through user surveys.” The participants recommended setting up “a reference group, a working group that develops resources on artificial intelligence.”
Developing AI-related content for library learning resources helps “highlight the positive aspects, the opportunities that exist, for example with cases, interviews, films, etc.” and “provide support on how to use AI-based tools.” Participants emphasised that it is important “to have contact with research communities to gain experiences,” “to find the users’ level” and to consider “differences between disciplines.” They expressed also an “expectation of democratisation/equalising some differences, establishing a common denominator (by helping many reach an OK level).”
Learning resources about AI should “explain how the technology itself works,” but also show “opportunities for developing ideas using tools.” The focus cannot be purely technical but must also consider perspectives like “learning theory and scholarly practice.” The presentation of AI should be “balanced: Not only writing and AI but also data analysis and other parts of the PhD life,” and “describe possibilities and limitations of what an artificial intelligence tool can assist with at all stages of the research process.” Different opinions were expressed about how information about AI should be integrated into learning resources like PhD on Track. While some “prefer it to be integrated rather than a separate track,” “addressing… opportunities and challenges in each of the main categories on the website,” others recommended a “more overarching module on artificial intelligence.”
When describing expectations and opportunities related to AI in learning resources, several participants mentioned concepts like a “tool compass,” a “selection tool,” a “guide” that could be “supportive in understanding which tools are available and what possibilities they offer”: “matrices of different tools,” an “up-to-date overview, clear, user-friendly,” preferably experience-based with concrete “examples of use with positive/negative outcomes” (e.g. “transcription, systematic review, text mining”) and “best practice tips.” Different opinions were expressed about how detailed such a presentation should be. Some suggested “showing the whole landscape with both pitfalls and opportunities,” while others recommended “not going into depth on specific tools or aiming to provide overviews of tools.” Additionally, it was suggested to provide lists of links to “relevant guidelines, resources, projects, research.”
Several participants described opportunities for more interactivity in learning resources. Firstly, AI related tasks can enhance active learning on webpages: “Select an example,” “discuss general usage,” “examples of where it went wrong,” “what is YOUR understanding of YOUR results,” “discuss something you are familiar with […], but ask to find limitations, be critical, ethical concerns”. Secondly, participants expressed a desire for more discussion of personal practices, both with PhD candidates and colleagues: Since artificial intelligence is new to everyone, it is important to have “an opportunity to share experiences with teaching/guidance related to artificial intelligence.” Discussions can be encouraged by “including cases that show different experiences […], discussing categories of artificial intelligence tools and their use in different phases: the search phase, the writing phase, and so on.” Formats suggested for this include a “forum wall for sharing experiences,” a “chat” or a “hotline,” or a link to “other actors that have forums” like BISON Dataverse. Intelligent technical support services were also proposed: “Chat based on artificial intelligence that responds to inquiries we receive at the library,” “using artificial intelligence to create a newsfeed linked to keywords in discussions about what’s happening in the artificial intelligence world. The feed with new academic articles.”