Norsk IKT-konferanse for forskning og utdanning
type: channel
description: This journal publishes peer-reviewed papers accepted and presented at the Norwegian ICT Conference for Research and Education. The journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. The Journal is registered as a level 1 publication in the Norwegian Register for Scientific Journals, Series and Publishers. The NIKT-stiftelsen is registered as an ideal foundation in the Norwegian organisation registry org.number 974 443 627. Web site: https://nikt.org/ NIKT Journal online ISSN: 1892-0721 Former printed publications ISSN: 1892-0713
publisher: NIKT-stiftelsen
language: en
publication name: Norsk IKT-konferanse for forskning og utdanning
issn: 1892-0713
items: Forord til UDIT 2025
items: The Norwegian Conference for Organizations’ Use of IT (NOKOBIT) 2025 Preface
items: Norsk Informatikkonferanse (NIK) 2025 Forord
items: How and Why Novices Use LLM-Based Tools to Solve CS1 Coding Tasks
items: Gradert karakterskala eller Bestått/Ikke bestått? Refleksjoner fra studenter i høyere IT utdanning
items: Learning Agile with Generative AI: Student Experiences in a Project-Based Course
items: Veiviser: scoping challenges in the deployment of augmented reality navigation for impaired individuals
items: Konsortiemyopi: Hvordan kollektivt eierskap skaper strategisk vranglås i digitale plattformer
items: Designing sustainable ICT platforms
items: Interpretable AI for Fisheries Data
items: Bruk av Kunstig Intelligens (KI) i Norsk Offentlig Sektor: Status 2025
items: Kunstig intelligens som medspiller i kunnskapsarbeid
items: An Automated Solution to Exam Scheduling at Østfold University College
items: Generative AI for Reducing Language and Administrative Barriers for Immigrants in Public Services
items: Emerging Knowledge Networks in the Architectural Transformation of Digital Infrastructures
items: A Representative Sampling Approach to Few-Shot Transfer Learning
items: Data Replication for Distributed Emergency Management Systems
items: Passive bandwidth estimation for fog computing
items: Flexible Visualization of Eye Tracking Data to Aid Assessment of Visuospatial Neglect
items: Revisiting Structured Representation Use in LLM
items: Explaining Monocular Depth Estimation - Diving into Regional Differences in the Prediction
items: Selective Temporal Fusion using Recurrent Attention for End-to-End Autonomous Driving
items: FragFM: Fragmented Fourier Matrix for multi-seasonality extraction in Univariate Long-Term Time Series Forecasting
items: A rubric on Core Game Mechanics
items: Generative AI tools to propose summative assessment tasks
items: Advancing Cybersecurity Education in the Era of Generative AI
items: Introducing Coding Co-Pilots in an Introductory Programming Course - the Beginning of the End?
items: Tilbakemelding frå ChatGPT i Moodle
items: Fra gjetting til refleksjon på eksamen med flervalgsoppgaver
items: Kva seier generativ KI om kvalitet i IT-utdanning?

Forord til UDIT 2025
type: item
description: Det er med stor glede vi ønsker velkommen til UDIT 2025! Siden starten i Ålesund i 2015 har konferansen vært en møteplass for alle undervisere som er mer enn gjennomsnittlig interessert i utvikling av IT-utdanning. Årets bidrag sier noe om hvor vi står i 2025: Generativ kunstig intelligens har gått fra å være et spennende tema til å bli en integrert del av undervisning, vurdering og læringsprosesser. Vi ser eksempler på hvordan språkmodeller og copiloter påvirker programmeringsemner, hvordan KI kan gi tilbakemelding i læringsplattformer, og hvordan studentene selv reflekterer over nytten av bruken av disse verktøyene.
subject: Forord
  topic: section
subject: UDIT
  topic: keywords
subject: Forord
  topic: keywords
subject: Undervisning
  topic: keywords
creator: Per Lauvås
creator: Torstein Strømme
rights: Copyright (c) 2025 Per Lauvås, Torstein Strømme https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/03sy8s56

The Norwegian Conference for Organizations’ Use of IT (NOKOBIT) 2025 Preface
type: item
description: This is the 33rd Norsk Konferanse for Organisasjoners Bruk av Informasjonsteknologi (Norwegian Conference for Organizations’ Use of IT - NOKOBIT) since its inception in 1993. In 2025, NOKOBIT takes center stage as one of the four primary tracks at the prestigious 37th Norwegian ICT Conference for Research and Education (NIKT 2025), hosted by Østfold University College in Norway. NOKOBIT 2025 encompasses a wide range of topics, focusing on the development, implementation, and utilization of ICT in organizations, while also addressing sustainability and societal concerns. The conference serves as a collaborative platform for researchers and practitioners to share and expand knowledge in these critical areas. NOKOBIT 2025 received 14 full paper submissions, of which 9 were accepted for oral presentations. All submissions underwent a rigorous blind peer review process, with each paper evaluated by two to three independent reviewers. We extend our sincere gratitude to everyone who submitted their work. Our heartfelt thanks also go to the program committee members and reviewers for their essential role in assessing the submissions. Additionally, we would like to express our appreciation to the NIKT chairs and the local organizing committee for their exceptional contributions in making this conference possible. Welcome, and we hope you have an inspiring and productive conference! Miroslav Bachinski & Mary Sánchez-Gordón NOKOBIT 2025 Chairs
subject: Preface
  topic: section
subject: preface
  topic: keywords
creator: Miroslav Bachinski
creator: Mary Sánchez-Gordón
rights: Copyright (c) 2025 Miroslav Bachinski, Mary Sánchez-Gordón https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/y1q1rv22

Norsk Informatikkonferanse (NIK) 2025 Forord
type: item
description: NIK (Norsk Informatikkonferanse) is a national conference that aims to spread the knowledge of research and advanced development work in the field of infor- matics. The conference encourages the submission of papers from various genres and topics within theoretical and applied informatics. NIK welcomes diversity and cross-disciplinary approaches in methodological techniques and application areas.
subject: Preface
  topic: section
subject: NIK
  topic: keywords
subject: Informatics
  topic: keywords
subject: Computer Science
  topic: keywords
subject: Preface
  topic: keywords
creator: Maben Rabi
creator: Patrick Stünkel
rights: Copyright (c) 2025 Maben Rabi, Patrick Stünkel https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/kaw3p498

How and Why Novices Use LLM-Based Tools to Solve CS1 Coding Tasks
type: item
description: As Large Language Models (LLMs) gain popularity in programming education, understanding how novice programmers use these tools and their motivations is essential. Although many studies have explored AI tools like ChatGPT in computing education, few focus specifically on why students turn to these tools. Through 16 lab sessions incorporating participant observation and a questionnaire, we investigate factors driving novice programming students to use AI tools while completing CS1 coding assignments and examine how students engage with these tools. We provide findings from a thematic analysis examining (a) what learners ask from AI tools; (b) characteristics of learner-written prompts in terms of clarity and crafting patterns; and (c) how learners use AI-generated responses for learning, modifications, and verification. Our analysis identifies key motivations driving novice programmers to use AI tools: timesaving and task management, ease of access, aided learning, and cognitive miserliness. Furthermore, our analysis reveals diverse engagement patterns, including a novel "AI Conceptual-Hybrid" approach, where students use AI to clarify concepts and task requirements but write code independently. We offer insights on how inexperienced learners utilize AI tools, emphasizing self-regulation, indications of overreliance, and opportunities for improving AI-assisted learning tools in computing education.
subject: Articles
  topic: section
subject: Programming education
  topic: keywords
subject: Generative AI
  topic: keywords
subject: AI tools
  topic: keywords
creator: Christian Garmann Sørli
creator: Trond Aalberg
rights: Copyright (c) 2025 Christian Garmann Sørli, Trond Aalberg https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/acwbcm12

Gradert karakterskala eller Bestått/Ikke bestått? Refleksjoner fra studenter i høyere IT utdanning
type: item
description: Denne studien undersøker IT-studenters oppfatninger om karakteruttrykkene gradert karakterskala (A–F) versus Bestått/Ikke bestått (B/IB) i norsk høyere utdanning, et tema som har fått økt aktualitet etter at flere prestisjeutdanninger nylig har vedtatt overgang til B/IB-vurdering. Gjennom kvalitative fokusgruppeintervjuer med 34 studenter fra tre utdanningsinstitusjoner identifiserer studien en preferanse for gradert skala blant IT-studenter. Denne preferansen tilskrives primært karakterers innvirkning på akademisk motivasjon, konkret tilbakemelding på mestringsnivå, samt oppfattet merittverdi for framtidig karriere og videre studier. Samtidig avdekker funnene noen mulige fordeler ved B/IB, herunder redusert akademisk stress og økt potensial for interessedrevet læring framfor karakterjag. Egnethet for karakteruttrykk kan også være domeneog kontekstavhengig. Forskningsresultatene utgjør et empirisk fundament for individuelle og institusjonelle beslutninger om valg av karakteruttrykk innen informasjonsteknologiutdanning og bidrar til den pågående debatten om valg av karakteruttrykk i høyere utdanning.
subject: Articles
  topic: section
subject: Bestått/Ikke bestått
  topic: keywords
subject: Gradert karakterskala
  topic: keywords
subject: Høyere IT utdanning
  topic: keywords
subject: Fokusgruppeintervjuer
  topic: keywords
subject: Studentrefleksjoner
  topic: keywords
creator: Per Lauvås Jr
creator: Omid Mirmotahari
creator: Tengel Sandtrø
creator: Yngvar Berg
creator: Yngve Lindsjørn
rights: Copyright (c) 2025 Per Lauvås Jr, Omid Mirmotahari, Tengel Sandtrø, Yngvar Berg, Yngve Lindsjørn https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/bf1td211

Learning Agile with Generative AI: Student Experiences in a Project-Based Course
type: item
description: This exploratory study investigates how students experience the use of Generative AI (GenAI) in a project-based Agile course within higher education. Through a combination of interviews and survey, we examine how GenAI tools support learning, decision-making, and team dynamics. Our findings point out that students used GenAI primarily for understanding and applying Agile practices, validating decisions, and fostering teamwork. For some students, GenAI served as a scaffold for applying theory to practice, and boosted their confidence. We conclude that GenAI can be effectively integrated into project-based learning, provided that students receive guidance on responsible use and evaluation. We offer implications for teaching strategies and contribute to emerging discussions on GenAI in high education.
subject: Articles
  topic: section
subject: AI in education
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Computer science education
  topic: keywords
subject: Project-based learning
  topic: keywords
subject: Agile
  topic: keywords
subject: Scrum Master
  topic: keywords
creator: Rolando Gonzalez
creator: Per Lauvås Jr
creator: Marculescu Bogdan
creator: Jefferson Seide Molléri
creator: Hanne Sørum
rights: Copyright (c) 2025 Rolando Gonzalez, Per Lauvås Jr, Marculescu Bogdan, Jefferson Seide Molléri, Hanne Sørum https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/681a9k25

Veiviser: scoping challenges in the deployment of augmented reality navigation for impaired individuals
type: item
description: This study reports on the development and evaluation of a multimodal augmented reality (AR) indoor navigation prototype using Visual Positioning technology designed to improve mobility for individuals with visual impairments or other disabilities and deployed in the public sector. Particu-lar focus was on scalability, transparency, and privacy. Based on consultation with different user groups in a user-centered design (UCD) process, different technological possibilities were identified, and an application was developed and evaluated. The evaluation incorporated a System Usability Scale (SUS) test and post-test interviews. Results were promising, demonstrating that the prototype is user-friendly and effective. Subsequently, we reflected on the implications of a deployment at a larger scale and potential risks to privacy and transparency. Several issues related to privacy and scal-ing the application to support more users and offer wide navigation opportunities were identified and discussed in the article.
subject: Articles
  topic: section
subject: Augmented and Virtual Reality
  topic: keywords
subject: Visual Positioning
  topic: keywords
subject: Mobile and Weareable Technologies
  topic: keywords
subject: Human-computer interaction
  topic: keywords
subject: Interaction design
  topic: keywords
creator: Georgios Marentakis
creator: Morten Gustavsen
creator: Jan-Erik Vinje
creator: Tom-Robert Bryntesen
rights: Copyright (c) 2025 Georgios Marentakis, Morten Gustavsen , Jan-Erik Vinje, Tom-Robert Bryntesen https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/j9cc6597

Konsortiemyopi: Hvordan kollektivt eierskap skaper strategisk vranglås i digitale plattformer
type: item
description: Digitale plattformer eid av enkeltsselskaper møter kritikk for monopol og verdiekstrahering. Som svar har konsortiumstyrte plattformer vokst fram for å fremme åpne, generative økosystemer gjennom kollektivt eierskap. Likevel sliter mange slike plattformer med å skalere økosystemene sine med komplementører. Dette utgjør et kritisk paradoks: hvordan kan en styringsform som er ment å sikre et sunt økosystem, systematisk mislykkes med å realisere det? Med utgangspunkt i en case-studie av en konsortiestyrt plattform og teorier om interorganisatoriske systemer, gir denne artikkelen et mulig svar. Vi introduserer begrepet konsortiemyopi, en strukturell dynamikk der det rasjonelle, kortsiktige fokuset på å tilfredsstille konsortiemedlemmenes særinteresser systematisk overskygger og undergraver det langsiktige, kollektive målet om å kultivere et plattformøkosystem. Vi viser hvordan dette skaper stiavhengigheter som fører til en «strategisk vranglås» som hindrer skalering av plattformøkosystemet. Artikkelen bidrar til faglitteraturen ved å identifisere en grunnleggende utfordring i konsortiumstyrte plattformer og forklare hvorfor ambisjonen om et økosystem med komplementører ofte forblir uforløst.
subject: Articles
  topic: section
subject: Digitale plattformer
  topic: keywords
subject: Konsortiumstyrte plattformer
  topic: keywords
subject: Plattformstyring
  topic: keywords
subject: Konsortiemyopi
  topic: keywords
subject: Interorganisatoriske systemer
  topic: keywords
subject: Stiavhengighet
  topic: keywords
creator: Hong-Ngan Hua
creator: Thomas Østerlie
rights: Copyright (c) 2025 Hong-Ngan Hua, Thomas Østerlie https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/6t3cdb95

Designing sustainable ICT platforms
type: item
description: The sustainability of ICT platforms is becoming increasingly important with a potential explosion of negative social and environmental impacts of new technologies. To analyse such impact, several frameworks exist; however, SuSAF (Sustainability Awareness Framework) is the most widely applied. This paper reports on a multiple case study on the use of the SuSAF to evaluate existing ICT platforms as a basis for design suggestions for future versions of the platforms in a more sustainable direction. The main results of the paper include design suggestions for the further development of ICT platforms, which illustrate the applicability of SUSAF for such analysis. Future work includes a variety of ICT platforms and applies other evaluation tools to other sustainability issues.
subject: Articles
  topic: section
subject: Sustainability design
  topic: keywords
subject: ICT platforms
  topic: keywords
subject: SusAF
  topic: keywords
subject: Design suggestions
  topic: keywords
creator: Sunniva Bjerke
creator: Carolina Gunnesdal
creator: John Krogstie
creator: Dimitra Chasanidou
rights: Copyright (c) 2025 Sunniva Bjerke, Carolina Gunnesdal, John Krogstie, Dimitra Chasanidou https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/phzw0263

Interpretable AI for Fisheries Data
type: item
description: Sustainable use of fish resources is essential, and decision makers such as the Norwegian Directorate of Fisheries (NDF) must take proactive measures to prevent Illegal, Unreported, and Unregulated (IUU) fishing activities. With access to large volumes of open datasets, machine learning (ML) models can play a key role in automating the detection of hidden patterns indicative of such activities. One valuable dataset is the collection of catch reports, where fishermen record the details of their fishing operations. Previous research has explored the use of ML models to predict expected catch quantities. By comparing these predictions with the actual reported values, potential violations of regulations can be identified. However, to ensure trust in the model’s outputs and to gain deeper insight into the data, this paper applies interpretable Artificial Intelligence (AI) methods and visualization techniques to analyze prediction errors. We investigate feature importance and examine how the most influential features affect the model's output patterns. The results are promising, demonstrating that it is possible to provide transparency in the use of ML models for fisheries data. This approach enables domain experts to better understand, trust, and make informed use of the model's findings in the future.
subject: Articles
  topic: section
subject: Sustainability
  topic: keywords
subject: Fisheries
  topic: keywords
subject: Catch reports
  topic: keywords
subject: Prediction error
  topic: keywords
subject: Interpretable AI
  topic: keywords
creator: Aida Ashrafi
creator: Katja Enberg
creator: Bjørnar Tessem
rights: Copyright (c) 2025 Aida Ashrafi, Katja Enberg, Bjørnar Tessem https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/ycd5sj22

Bruk av Kunstig Intelligens (KI) i Norsk Offentlig Sektor: Status 2025
type: item
description: Det er store forventninger til bruk av KI i Norge. På den annen side rapporteres det at adopsjonen av KI i Norge går tregere enn forventet både i privat og offentlig sektor. Ved hjelp av svar fra NOKIOS teknologiradar 2017-2021, IT i Praksis – undersøkelser utført av Rambøll i 2021-2025, samt en annen nasjonal undersøkelse som en del av en femårig syklus, ser vi i denne artikkelen på rapportert og planlagt bruk av KI med fokus på lokale (kommuner) og statlige virksomheter. IT i praksis distribueres til en lang rekke norske offentlige virksomheter, med en svarprosent på over 50%. De nyeste dataene (2025) presentert i denne artikkelen er basert på svar fra 273 offentlige organisasjoner, med 188 kommuner, og 85 offentlige organisasjoner på nasjonalt eller regionalt (fylkes) nivå. Undersøkelsen viser stor økning av enkel bruk av KI, mens integrert bruk KI med virksomhetens andre IT-systemer fortsatt er på et tidlig stadium, selv om forventningene er høye til fremtidig bruk. Vi presentere også en nærmere undersøkelse av i hvilken grad rapportert KI bruk sam-varierer med etablerte metrikker for god ressursbruk for IT-aktivitet i virksomhetene
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: KI
  topic: keywords
subject: Adoption
  topic: keywords
subject: Implementasjon
  topic: keywords
subject: Survey investigation
  topic: keywords
subject: Spørreundersøkelse
  topic: keywords
creator: John Krogstie
rights: Copyright (c) 2025 John Krogstie https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/fwkjqp79

Kunstig intelligens som medspiller i kunnskapsarbeid
type: item
description: Store språkmodeller med generativ kunstig intelligens (KI) har fått mye oppmerk-somhet de siste årene og har gjort sitt inntog på norske arbeidsplasser. I denne artikkelen presenterer vi funn fra en kvalitativ studie som undersøker hvordan kunnskapsintensive organisasjoner kan støtte og styrke kunnskapsarbeideres oppgaveutførelse gjennom implementering av KI. Studien er basert på intervjuer med 11 informanter i mediebransjen i Norge. Med utgangspunkt i et sosioteknisk perspektiv finner vi fire elementer som er særlig viktige for at KI skal styrke kunnskapsarbeideren i sin oppgaveutførelse. Vi diskuterer disse med særlig vekt på organisasjonskultur og ansvarlig digitalisering. Studien bidrar til å øke kunnskapen rundt hva som kreves for å få til samspillet mellom menneske og maskin i kunnskapsintensive organisasjoner.
subject: Articles
  topic: section
subject: Kunstig intelligens
  topic: keywords
subject: KI
  topic: keywords
subject: Kunnskapsarbeid
  topic: keywords
subject: Menneske-maskin
  topic: keywords
subject: Sosio-teknisk perspektiv
  topic: keywords
subject: Ansvarlig KI
  topic: keywords
creator: Synnøve Bjørgum
creator: Tara Kenny
creator: Karen Sæbbo Osmundsen
creator: Kjersti Berg Danilova
rights: Copyright (c) 2025 Synnøve Bjørgum, Tara Kenny, Karen Sæbbo Osmundsen, Kjersti Berg Danilova https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/azwb6985

An Automated Solution to Exam Scheduling at Østfold University College
type: item
description: Exam scheduling in universities is often a complex and time-consuming task. At Østfold University College (HIOF), this task is currently performed manually. An exam coordinator must handle constraints such as ensuring at least three days between mandatory course exams and respecting pre-assigned dates from the Exam Office. This manual approach is prone to human error and can result in scheduling conflicts. To address this exam scheduling problem, we developed a user-friendly application paired with a greedy incremental algorithm. The proposed algorithm takes the schedule of last year as input and iteratively moves exams that violate constraints by one day per step. The algorithm is easy to implement and ensures that key constraints are met. On a computer with average specifications, our application produces a conflict-free schedule in seven milliseconds. Any remaining conflicts can be manually fixed through the spreadsheet. Furthermore, the algorithm is paired with a Java Swing framework to develop a user-friendly graphical user interface (GUI) for the application. In its current state, the application significantly reduces manual work, lowers the risk of human error, and brings HIOF to a near-fully automated exam scheduling process.
subject: Articles
  topic: section
subject: Exam scheduling
  topic: keywords
subject: Timetabling
  topic: keywords
subject: Local search algorithm
  topic: keywords
subject: User-centered design
  topic: keywords
creator: Ole Marcus Hansen
creator: Radin Morik
creator: Shvan Nasser
creator: Stine Vågnes
creator: Monica Kristiansen Holone
rights: Copyright (c) 2025 Ole Marcus Hansen, Radin Morik, Shvan Nasser, Stine Vågnes, Monica Kristiansen Holone https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/fegtmp34

Generative AI for Reducing Language and Administrative Barriers for Immigrants in Public Services
type: item
description: Generative artificial intelligence (AI), including large language models, is increasingly explored to support communication and navigation in public services. This paper presents a case study of how immigrants in Norway use and perceive generative AI when interacting with digital public systems. We adopted a mixed-methods design, combining twelve individual interviews with immigrants, two focus groups with six NAV employees, one digital professional meeting with about twenty NAV employees, four interviews with staff from the municipal Refugee Unit, and a survey of 55 immigrants in Trondheim. The study highlights language and administrative literacy as key barriers, including challenges with complex procedures and secure authentication (e.g., BankID). Generative AI was valued for translating, simplifying, and explaining bureaucratic content, but its use was mainly concentrated among participants with moderate to high digital skills. Concerns about accuracy, trust, and privacy shaped adoption. We find that AI should complement rather than replace human support; equitable deployment requires inclusive design, verified official sources, and training for both users and staff. The study contributes to discussions on digital inclusion and the responsible use of AI in the public sector.
subject: Articles
  topic: section
subject: Digital inclusion
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Immigrants
  topic: keywords
subject: Public services
  topic: keywords
subject: Mixed-methods research
  topic: keywords
creator: Emma Ju Blix
creator: Ilaria Crivellari
rights: Copyright (c) 2025 Emma Ju Blix, Ilaria Crivellari https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/c6kryq71

Emerging Knowledge Networks in the Architectural Transformation of Digital Infrastructures
type: item
description: For large incumbent organizations undergoing architectural transformation, knowledge is paramount. In the literature, knowledge is often fragmented, focusing either on individual systems or large corporate systems. Additionally, existing research tends to concentrate on how legacy systems can be made competitive or on the technical implications of transformation. In this paper, we adopt an infrastructure perspective to examine the knowledge networks that emerge during such transformations. We pose the question: How do knowledge networks emerge, and how are they organized to facilitate architectural transformation within digital infrastructures? Our study focuses on a prominent Norwegian bank that is currently undergoing an architectural transformation. We contribute to the discourse by developing a framework for "domain technical knowledge," encompassing three types of knowledge that collectively form a cohesive knowledge network. Furthermore, we demonstrate the horizontal and vertical alignment between these different knowledge types, illustrating how knowledge networks emerge.
subject: Articles
  topic: section
subject: Architectural Transformation
  topic: keywords
subject: Knowledge Networks
  topic: keywords
subject: Digital Infrastructures
  topic: keywords
creator: Egil Øvrelid
creator: Andrea Helmersen
creator: Madin Cej
rights: Copyright (c) 2025 Egil Øvrelid, Andrea Helmersen, Madin Cej https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/af03j778

A Representative Sampling Approach to Few-Shot Transfer Learning
type: item
description: Accurate plant growth estimation is essential in controlled environment agriculture (CEA), where crop monitoring, yield prediction, and environmental optimization all rely on reliable assessments. However, this task remains challenging due to the limited availability of labeled data. In this study, we propose a few-shot transfer learning framework for estimating cucumber plant growth, using a multi-stage approach. Our method combines deep feature extraction, representative image se- lection, and regression to accurately predict cucumber plant height using limited samples. By identifying key representative images, our approach reduces data requirements while maintaining comparable predictive per- formance. Our best model achieved a mean absolute error (MAE) of 20 mm with an R2 value of 0.68 using six representative samples. Ex- perimental results demonstrate that systematic representative sampling combined with regression significantly improves predictive performance and stability in few-shot transfer learning scenarios, thereby minimizing data collection efforts and reducing computational costs. Moreover, the integration of clustering enhances explainability, making the approach more transparent and suitable for practical deployment.
subject: Abstracts for Presentation
  topic: section
subject: controlled environment agriculture
  topic: keywords
subject: plant growth estimation
  topic: keywords
subject: few-shot transfer learning
  topic: keywords
subject: representative image selection
  topic: keywords
creator: Rusith Chamara Hathurusinghe Dewage
creator: Habib Ullah
creator: Muhammad Salman Siddiqui
creator: Rakibul Islam
creator: Fadi-Al Machot
rights: Copyright (c) 2025 Rusith Chamara Hathurusinghe Dewage, Habib Ullah, Muhammad Salman Siddiqui, Rakibul Islam, Fadi-Al Machot https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/kkyvdd97

Data Replication for Distributed Emergency Management Systems
type: item
description: This paper explores how existing, potentially autonomous applications for crisis management and emergency response can be extended with replication and where nodes are mobile or working in remote areas where they can be offline or weakly connected for extended periods. Tradeoffs between consistency, availability and partitioning tolerance must be made. The approach investigated provides replication with strong eventual consistency, using operation-based CRDTs, specifically the Last-Writer-Wins (LWW-Element-Set) CRDT. Nodes subscribing to datasets form hierarchical overlay networks. This approach is implemented and tested as an extension to an existing application used in voluntary search and rescue operations. The implementation is backed by an RDBMS and REST APIs. Results are discussed in terms of the LWW strategy, which relies on timestamps. The discussion includes challenges related to delete semantics as well as ways to leverage causality information to enhance the basic LWW strategy.
subject: Abstracts for Presentation
  topic: section
subject: Crisis management
  topic: keywords
subject: offline replication
  topic: keywords
subject: operation based CRDT
  topic: keywords
creator: Øyvind Hanssen
rights: Copyright (c) 2025 Øyvind Hanssen https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/b816jf45

Passive bandwidth estimation for fog computing
type: item
description: This paper investigates passive bandwidth estimation as a means to improve scheduling in Kubernetes-based fog computing environments. In contrast to active probing techniques, passive methods rely on observing existing traffic patterns to infer available bandwidth (ABW), offering a non-intrusive alternative suitable for dynamic and distributed systems. The study focuses on wired infrastructure networks within the cloud–fog–edge continuum. We develop and present an approach based on a modified version of the probe gap model, evaluated in an emulated network testbed. Ten experiments were conducted under varying traffic conditions, packet loss rates, and link capacities. Timestamping artifacts and data filtering were addressed to ensure measurement integrity. The approach demonstrates reliable ABW estimation across all scenarios, confirming the viability of passive methods for bandwidth-aware scheduling in Kubernetes.
subject: Regular papers
  topic: section
subject: bandwidth estimation
  topic: keywords
subject: fog computing
  topic: keywords
subject: experimental evaluation
  topic: keywords
creator: Kjeti Omang
creator: Carsten Griwodz
creator: Frank Johnsen
rights: Copyright (c) 2025 Kjeti Omang, Carsten Griwodz, Frank Johnsen https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/de4y1h80

Flexible Visualization of Eye Tracking Data to Aid Assessment of Visuospatial Neglect
type: item
description: Traditional pen-and-paper tests used for assessing visuospatial neglect, a condition which may occur in stroke patients, provide limited amounts of data, often leaving it up to the clinician to fill in the gaps. There is growing interest in applying VR to these situations by adapting traditional pen‑and‑paper tests for virtual environments. Combined with eye tracking, such systems generate far richer datasets, but also demand new tools to structure and visualize the increased data. This article explores various visualization techniques of eye tracking data through a custom built system, and evaluates its utility with respect to neglect assessment by consulting experts within the field. Overall, the system was deemed highly useful for clinicians. Static visualizations were found useful for quickly identifying patterns and areas of reduced attention by providing an overview of the attention allocation, while dynamic visualizations provide real-time insight into visual behavior, making them useful for investigating the temporal aspects of visuospatial neglect. However, due to neglect assessment being a highly specialized field, the number of experts giving statements are limited, and further research is needed to ensure reliable and more broadly generalizable results regarding the evaluation of the usefulness of the visualization system.
subject: Regular papers
  topic: section
subject: visuospatial neglect
  topic: keywords
subject: hemispatial neglect
  topic: keywords
subject: unilateral spatial neglect
  topic: keywords
subject: VR
  topic: keywords
subject: virtual reality
  topic: keywords
subject: eye tracking
  topic: keywords
subject: visualization
  topic: keywords
creator: Alexander Holt
creator: Joel Mattias Tømmerbakk
creator: Tor Ivar Hansen
creator: Xiaomeng Su
rights: Copyright (c) 2025 Alexander Holt, Joel Mattias Tømmerbakk, Tor Ivar Hansen, Xiaomeng Su https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/v8nvr683

Revisiting Structured Representation Use in LLM
type: item
description: Structured representations (SRs), pivotal in pre-LLM NLP, play a contentious role today, with studies showing that they can degrade task performance. The prevailing hypothesis suggests that Large Language Models (LLMs) are unfamiliar with traditional formalisms like Abstract Meaning Representation (AMR). We argue that this view is incomplete, as LLMs are extensively trained on structured data, particularly programming code. To test this, we introduce two new prompt frameworks and evaluate three representation formats (AMR vs. RDF vs. Python code) across multiple LLMs and tasks. Our findings indicate that the choice of representation is of high importance. Across multiple models and tasks, we show that Python code and RDF outperform AMR up to 20% in classification tasks. The effectiveness of any SR is also conditioned on the LLM's baseline capability, the prompting method, and the quality of the representation itself. Although SRs can substantially boost performance for models with weaker baselines, they offer diminishing returns and can harm performance for models that are already highly capable, confirming a "sweet spot" for their application. Our work demonstrates that the utility of SRs in the LLM era depends on their alignment with the models' training data.
subject: Regular papers
  topic: section
subject: Natural Language Processing
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Structured Representation
  topic: keywords
creator: Michal Chudoba
creator: Tomasz Wiktorski
creator: Sergey Alyaev
rights: Copyright (c) 2025 Michal Chudoba, Tomasz Wiktorski, Sergey Alyaev https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/2ethqv02

Explaining Monocular Depth Estimation - Diving into Regional Differences in the Prediction
type: item
description: Recent foundational depth estimation models achieve impressive accuracy on various scenes. However, due to their black box nature, we lack knowledge about how they utilize the input for their predictions, and hence about their applicability to domains sparsely covered by labeled datasets. This paper, therefore, applies occlusion to investigate the influence of the input on different parts of the predicted depth maps for underwater scenes. Our results show that foundational depth estimation models combine global and local features to estimate relative distance, and that their influence differs significantly between the background and the rest of the scene. This suggests that extending post-hoc explanations to consider relationships between multiple input and output features can enrich our understanding of monocular depth estimation models and potentially help to gauge their applicability to new domains.
subject: Regular papers
  topic: section
subject: Computer Vision
  topic: keywords
subject: Monocular Depth Estimation
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Sabine Fischer
creator: Øyvind Ødegård
creator: Frank Lindseth
creator: Steven Yves Le Moan
creator: Gabriel Hanssen Kiss
rights: Copyright (c) 2025 Sabine Fischer, Øyvind Ødegård, Frank Lindseth, Steven Yves Le Moan, Gabriel Hanssen Kiss https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/3kmsk121

Selective Temporal Fusion using Recurrent Attention for End-to-End Autonomous Driving
type: item
description: In end-to-end autonomous driving (E2E-AD), understanding the complex and dynamic environment of the driving scene is crucial. Temporal information supports this by extending perception beyond what is observable in a single frame. While some E2E-AD architectures, like TransFuser++, operate without temporal modeling, various methods for temporal fusion have been explored, from frame-stacking to memory-based methods and most recently, attention-based recurrent methods. However, existing recurrent attention methods lack a mechanism for forgetting information, distributing attention across all past features even when they are no longer relevant. In this paper, we present a recurrent attention-based temporal fusion module (TFM) with selective forgetting, designed as a drop-in extension for E2E-AD architectures. The TFM fuses current and past information using cross-attention, enabling temporal modeling with minimal impact on inference time, and allows for interpretable retention through attention weight visualization. We integrate a selection mechanism using a void token to allow selective forgetting of irrelevant past information. Applied to the TransFuser++ architecture, our method achieves a driving score of 83.69% on the closed-loop Bench2Drive benchmark and provides qualitative insights into how models retain past information. These results demonstrate its potential as a temporal extension to otherwise temporally unaware architectures.
subject: Regular papers
  topic: section
subject: End-to-End Autonomous Driving
  topic: keywords
subject: Recurrent Neural Networks
  topic: keywords
subject: Imitation Learning
  topic: keywords
subject: Attention
  topic: keywords
subject: Temporal Processing
  topic: keywords
subject: CARLA
  topic: keywords
creator: Andreas Bentzen Winje
creator: Florian Wintel
creator: Gabriel Hanssen Kiss
creator: Frank Lindseth
rights: Copyright (c) 2025 Andreas Bentzen Winje, Florian Wintel, Gabriel Hanssen Kiss, Frank Lindseth https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/zpjv0r03

FragFM: Fragmented Fourier Matrix for multi-seasonality extraction in Univariate Long-Term Time Series Forecasting
type: item
description: Long-term time series forecasting often relies on frequency- or time-domain decomposition to capture trends and seasonalities. Frequency based methods, such as the Discrete Fourier Transform, isolate dominant periodic components and suppress noise, but they operate in the complex domain. Time-domain models apply trend–seasonality decomposition but often capture only a single seasonality entangled with residual noise. To address these limitations, we propose the \ac{FragFM}-Fragmented Fourier Matrix--a time domain framework that incorporates spectral insights through fixed Fourier fragments. FragFM removes trends via moving averages, extracts multiple seasonal components with fragmented Fourier bases, and corrects residual noise. Operating fully in the real domain, FragFM achieves forecasting accuracy comparable to leading frequency-domain models while consistently outperforming traditional time-domain approaches. Its lightweight, interpretable design offers an efficient solution for long-term forecasting, bridging time- and frequency-domain methods. The code repository is: https://github.com/K0DX-DV1NUX/FragFM
subject: Regular papers
  topic: section
subject: Long Term Time Series Forecasting
  topic: keywords
subject: Univariate Forecasting
  topic: keywords
subject: Low Rank Fourier Transform Seasonal Decomposition
  topic: keywords
creator: Aditya Dey
creator: Jonas Kusch
creator: Fadi Al Machot
rights: Copyright (c) 2025 Aditya Dey, Jonas Kusch, Fadi Al Machot https://creativecommons.org/licenses/by/4.0
date: 2025-11-24
publication date: 2025-11-24
doi: 10.5324/140y3898

A rubric on Core Game Mechanics
type: item
description: Game development education in Norwegian higher education has expanded significantly in recent decades yet lacks a unified framework for assessing the quality of games and their development processes. Current assessment practices vary across institutions and often rely on subjective interpretations or inconsistent theoretical foundations. This paper addresses the need for a shared evaluation framework by focusing on the concept of Core Game Mechanics (CGM) and the development of a rubric to assess their quality. The study identifies five key dimensions of CGM: Primary Force, Mastery, Fun Factor, Feedback, and Purpose, through an iterative, design-based research approach. These dimensions were derived from both qualitative analysis of survey responses from industry professionals and educators, and students’ evaluations. The resulting rubric aims to support both formative and summative assessment, offering a more objective and pedagogically aligned tool for evaluating student game projects. The study contributes to the ongoing discourse on standardizing assessment in game development education and highlights the potential of heuristics-informed rubrics to enhance learning, teaching, and curriculum development.
subject: Articles
  topic: section
subject: Core Game Mechanics
  topic: keywords
subject: Rubric
  topic: keywords
subject: Heuristics
  topic: keywords
subject: Assessment
  topic: keywords
subject: Game Development
  topic: keywords
creator: Robin Isfold Munkvold
creator: Niels Pálmi Skovsgård Jónsson
rights: Copyright (c) 2025 Robin Isfold Munkvold, Niels Pálmi Skovsgård Jónsson https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/h0f9e633

Generative AI tools to propose summative assessment tasks
type: item
description: Generative artificial intelligence (GenAI) is increasingly considered for educational purposes, including the design of programming assessments. While prior work has focused on introductory-level courses, little is known about the suitability of GenAI for generating examination tasks at the master’s level. This study investigates the potential of ChatGPT 4.0, Bing Copilot, and Google Gemini, to generate programming exam questions and model solutions. Using a fixed prompt, four researchers collected responses monthly from July 2024 to March 2025. The resulting questions and solutions were analyzed through a mixed-methods approach, combining semantic similarity analysis with Sentence-BERT embeddings, automated code quality metrics, and manual evaluation of task appropriateness. Findings indicate substantial variation across systems: ChatGPT and Gemini generated a diverse set of tasks, including some suitable for advanced assessment, whereas Bing Copilot displayed strong topical conver-gence, with limited variation and several tasks more appropriate for undergradu-ate levels. While the vague prompt limited contextual alignment with intended learning outcomes, results show that GenAI can assist educators by providing a pool of candidate tasks and solutions, albeit requiring expert curation and refine-ment. Future work should examine richer prompting strategies, inclusion of course-specific learning objectives, and student-centered evaluations of gener-ated tasks.
subject: Articles
  topic: section
subject: Programming
  topic: keywords
subject: Exam Questions
  topic: keywords
subject: Generative AI
  topic: keywords
subject: LLMs
  topic: keywords
subject: Summative Assessments
  topic: keywords
creator: Guttorm Sindre
creator: Line Kolås
creator: Robin Isfold Munkvold
creator: Mariusz Nowostawski
creator: Håkon Einar Stokke Ringen
creator: Joakim Pettersen Vassbakk
rights: Copyright (c) 2025 Guttorm Sindre, Line Kolås, Robin Isfold Munkvold, Mariusz Nowostawski, Håkon Einar Stokke Ringen, Joakim Pettersen Vassbakk https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/zy7yn618

Advancing Cybersecurity Education in the Era of Generative AI
type: item
description: Artificial intelligence advancements have recently received atten-tion in both academic and industrial settings, demonstrating disruptive potential in many domains, including education. Previous studies have shown that AI-driven tools can transform conventional learning methods. This paper focuses on identifying how generative AI (GAI) tools are leveraged by cybersecurity stu-dents. A survey has been designed based on cognitive process dimension of Bloom’s taxonomy and distributed to students studying cybersecurity in Norway. The results of the survey were illustrated with a set of use cases that demonstrated how Microsoft Copilot can enable students with skills ranging from lower-order knowledge-based skills to higher-order competence-based skills. The applied use cases presented in this paper indicate that GAI tools can en-hance lower- and higher-order thinking skills of cybersecurity students and con-tribute to bridging the gap between theoretical knowledge and competence-based skills needed to face real-world cybersecurity challenges. However, the results of the survey indicated that this potential has not yet been fully utilized by students, as they use such tools mainly for tasks that require lower-order thinking skills, such as remembering and understanding cybersecurity concepts, and less for more advanced cognitive tasks, such as creating role-playing scenarios. These preliminary findings can be useful to trigger discussions on how to improve fu-ture cybersecurity curricula to include applied GAI use cases and align them with learning outcomes. Ultimately, future efforts can be dedicated to developing a roadmap as a collaborative effort among policy-makers, cybersecurity academics and practitioners on the trustworthy incorporation of applied GAI in cybersecu-rity education.
subject: Articles
  topic: section
subject: generative AI
  topic: keywords
subject: cybersecurity education
  topic: keywords
subject: Bloom’s taxonomy
  topic: keywords
subject: Microsoft Copilot
  topic: keywords
subject: survey
  topic: keywords
creator: Selina Demi
creator: Bjørnar Guttormsen
creator: Kevin Forberg Reinaas
creator: Mats Hansen
creator: Sofie Sand
rights: Copyright (c) 2025 Selina Demi, Bjørnar Guttormsen, Kevin Forberg Reinaas, Mats Hansen, Sofie Sand https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/42gawg86

Introducing Coding Co-Pilots in an Introductory Programming Course - the Beginning of the End?
type: item
description: Recent advances in generative AI and coding co-pilots in particular have fundamentally changed the tools available to students. For introductory programming courses this represents a challenging setting as assignments typically posed are well within reach of being automatically solved by current state-of-the-art generative AI technology. This in turn raises concerns about student learning outcomes in terms of fundamental programming knowledge and skills. At the same time, recent surveys indicate a considerable industry uptake of generative AI to boost productivity in software development. The latter suggests that in the course of their education, computer science students must learn to master these new technologies. This paper presents our initial investigations into introducing coding co-pilots in a first-year introductory programming course with a view towards embracing co-pilots and encouraging reflective use by the students.
subject: Articles
  topic: section
subject: Teaching Programming
  topic: keywords
subject: Coding co-pilots
  topic: keywords
subject: Generative AI
  topic: keywords
creator: Lars Michael Kristensen
creator: Sven-Olai Høyland
rights: Copyright (c) 2025 Lars Michael Kristensen, Sven-Olai Høyland https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/699gyw43

Tilbakemelding frå ChatGPT i Moodle
type: item
description: Kunstig intelligens tok verda med storm i desember 2022. Høgare utdanning har eit stort behov for kostnadseffektiv vurdering, både summativ og formativ, og der er stor etterspurnad etter KI som kan retta og gje tilbakemelding på oppgåver som elles krev fagleg skjønn. Praktiske løysingar lèt derimot venta på seg. Her skal me presentera ei enkel løysing for å gje umiddelbar tilbakemelding på oppgåver i optikk, vha. Moodle og ChatGPT. Me vurderer potensialet basert på faktiske studentsvar og tilbakemeldingar, samt dei etiske innvendingane og behov for vidare utvikling.
subject: Articles
  topic: section
subject: vurdering
  topic: keywords
subject: kunstig intelligens
  topic: keywords
subject: fysikkdidaktikk
  topic: keywords
subject: automatisk tilbakemelding
  topic: keywords
creator: Jonas Julius Harang
creator: Hans Georg Schaathun
creator: Kenny Solevåg-Hoti
rights: Copyright (c) 2025 Jonas Julius Harang, Hans Georg Schaathun, Kenny Solevåg-Hoti https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/fey8zs22

Fra gjetting til refleksjon på eksamen med flervalgsoppgaver
type: item
description: Denne studien undersøker implementering av confidence-based assessment (CBA) i flervalgsoppgaver. Gjennom et mixed-methods-design design analyseres data fra over 1100 informatikkstudenter over to år (2023-2024). Resultatene viser at CBA gir et mer nyansert bilde av studentenes kunnskapsnivå ved å redusere effekten av gjetting og fremme metakognitiv refleksjon. Studentene bruker betydelig mer tid på CBA-oppgaver sammenlignet med tradisjonelle flervalgsoppgaver, noe som indikerer dypere kognitiv prosessering. Kvalitative intervjuer avdekket at studenter opplever CBA som utfordrende men verdifull for læring, særlig ved komplekse oppgaver. Ulike poengmodeller blir sammenlignet, og full CBA-poenggivning viser seg å gi mest rettferdige vurderingsresultater. Studien konkluderer med at CBA kan transformere flervalgsoppgaver fra rene kunnskapstester til verktøy som også måler metakognitiv bevissthet, spesielt på høyere nivåer i Blooms taksonomi.
subject: Articles
  topic: section
subject: confidence-based assessment
  topic: keywords
subject: Flervalgsoppgaver (MCQ)
  topic: keywords
subject: Informatikkutdanning
  topic: keywords
subject: Metakognitiv refleksjon
  topic: keywords
subject: Blooms taksonomi
  topic: keywords
creator: Omid Mirmotahari
creator: Iuliia Perminova
creator: Yngvar Berg
rights: Copyright (c) 2025 Omid Mirmotahari, Iuliia Perminova, Yngvar Berg https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/a5zvhg53

Kva seier generativ KI om kvalitet i IT-utdanning?
type: item
description: Dei siste åra har generativ KI basert på store språkmodellar fått stor popularitet til ulike formål, både innan akademia og elles i samfunnet. Éin mogeleg bruk kan vere å få kjapt innblikk i eit nytt tema gjennom eit konsist samandrag. I denne artikkelen prøver vi å finne ut kva generativ KI seier om kvalitet i høgare IT-utdanning. Forskingsspørsmålet er ikkje primært å finne ut kva utdanningskvalitet er (som fagfolk allereie burde ha eigne idear om), men vel så mykje å finne ut noko om desse systema og – indirekte – kva dei har lært om utdanningskvalitet frå treningsmaterialet sitt. To ulike prompt blei brukte, der det første spør om kvalitet i høgare IT-utdanning på ein open måte, medan det andre spesifikt vinklar spørsmålet mot samstemd undervisning (constructive alignment). Tre ulike KI-system vart brukte første måndag i kvar månad gjennom eit halvt år, for også å kunne sjå eventuelle skilnader mellom systema og utvikling over tid. Dei primære funna er at det var ganske stor skilnad på svar mellom dei to ulike prompta, men mindre skilnad i svar på same prompt mellom ulike KI-system, og ingen klar tendens til utvikling i svara over den tidsperioden studien føregjekk.
subject: Articles
  topic: section
subject: IT-utdanning
  topic: keywords
subject: kvalitet
  topic: keywords
subject: samstemd undervisning
  topic: keywords
subject: KI
  topic: keywords
subject: språkmodellar
  topic: keywords
creator: Guttorm Sindre
creator: Line Kolås
creator: Robin Isfold Munkvold
rights: Copyright (c) 2025 Guttorm Sindre, Line Kolås, Robin Isfold Munkvold https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
doi: 10.5324/739ssc03