Artificial Intelligence-based tools in the context of Open Science

PhD on Track as a resource




Open Science, PhD, artificial intelligence, PhD on Track


This poster presents possibilities and issues concerning the new wave of Artifical Intelligence (AI)-based services and tools entering academia aimed to help researchers and students.,,,,,,, and, are all examples of services part of a complex landscape of educational and research support resources driven by AI. Unfortunately, researchers and students have very few arenas to learn about all the aspects of using those resources. Trust, ethics, interpretability and reliability, are all topics to be addressed when using those tools (Guidotti et al., 2018). Moreover, the lack of possibility to test and influence how literature is analyzed and new knowledge created by AI-based services, is an emerging concern in various academic libraries (Gasparini & Kautonen, 2016). The increasing production of fake science in AI-based papermills implies new challenges for academic quality control and for the accountability and reliability of research as a whole (Løkeland-Stai, 2022).

The poster proposes to use the site PhD-on-track (, Faber et al., 2018), one of the preferred starting points for new PhD candidates and early career researchers, to contextualize AI-based services in researchers’ literature search and analysis. PhD on Track aims to enable PhD candidates from all academic fields to easily access information on different aspects of open science and support academic integrity in their research practices. By addressing AI-based services and tools in an early stage, PhD on Track will contribute to avoid opacity and clarify non-intuitive aspects of the use of technology in research. Furthermore, we argue for the possibility that these technical innovations will change the workflows of researchers and students. Academia needs to react to this development, offer a framework and support a shift of focus on AI-based services from the micro level (users) to a wider institutional one (the university).

The poster will present the following issues and questions, with respective possible reactions and solutions: 

  • What is the minimum level of competence PhD candidates should have about machine learning and deep learning? 
  • How should PhD candidates choose reliable AI-based services? 
  • When and where should PhD candidates and researchers gather reliable information about AI-based services? 
  • How should academic libraries support access to repositories of Open Access articles? 
  • How should universities ensure the correct use of AI-based tools? 
  • How can universities and academic libraries address ethical questions which may be raised by the increasing availability and use of AI-based tools?


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Author Biographies

Hege Charlotte Lysholm Faber, Norwegian University of Science and Technology Library

Hege Charlotte Faber finished her PhD on net art and interactivity in 2003. She is Senior Research Librarian at the Library Section for Research Support, Data and Analysis at NTNU University Library, The Norwegian University of Science and Technology (NTNU). Her most recent book is I dialog med maleriet (In Dialogue with Painting), a monograph on the work of the Norwegian artist Jon Arne Mogstad (2016). She is co-editor of Raw. Architectural Engagements with Nature (2014) and Wild. Aesthetics of the Dangerous and Endangered (forthcoming).

Andrea Alessandro Gasparini, University of Oslo Library

Andrea Gasparini finished in February 2020 his Ph.D. at the Department of Informatics, University of Oslo, Norway. His Ph.D. is about the use of Design Thinking and Service Design in academic libraries. Right after he had a 20% position in the same department as a senior lecturer for one year. In March 2022, he started in the same position, and from august 2022, he is also acting leader of the Regenerative technologies research group. Those are in the context of energy and Artificial Intelligence. He has also worked for more than 20 years as a chief engineer at the University of Oslo Library. 

Michael Grote, University of Bergen Library

Michael Grote has a PhD in German Literature Studies and works as a senior academic librarian at the University of Bergen Library. At the Library of Humanities, he is responsible for Comparative Literature, German Studies, and Philosophy. Publications on German literature in the 20th century, media esthetics, radio art and autobiographical writing. Jury member of The Karl Sczuka Prize for Works of Radio Art, Südwestrundfunk, Germany. Editor of


Faber, H. C., Kavli, F. A., Grote, M., & Låg, T. (2018). PhD on Track: Open Science as a new track. Septentrio Conference Series, 1, Article 1.

Gasparini, A., & Kautonen, H. (2016). Mind the Change!: Public Sector As an Arena for User Experience Design. Proceedings of the 9th Nordic Conference on Human-Computer Interaction, 147:1-147:3.

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51(5), 93:1-93:42.

Løkeland-Stai, Espen (2022). Det tok oss noen minutter å plagiere Curt Rice og lure universitetets kontroll. Khrono 12.08.2022 (




How to Cite

Faber, H. C. L., Gasparini, A. A., & Grote, M. (2022). Artificial Intelligence-based tools in the context of Open Science: PhD on Track as a resource . Septentrio Conference Series, (1).