Norsk IKT-konferanse for forskning og utdanning
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
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.
Copyright (c) 2025 Per Lauvås, Torstein Strømme https://creativecommons.org/licenses/by/4.0
The Norwegian Conference for Organizations’ Use of IT (NOKOBIT) 2025 Preface
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
Copyright (c) 2025 Miroslav Bachinski, Mary Sánchez-Gordón https://creativecommons.org/licenses/by/4.0
Norsk Informatikkonferanse (NIK) 2025 Forord
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.
Copyright (c) 2025 Maben Rabi, Patrick Stünkel https://creativecommons.org/licenses/by/4.0
How and Why Novices Use LLM-Based Tools to Solve CS1 Coding Tasks
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.
Copyright (c) 2025 Christian Garmann Sørli, Trond Aalberg https://creativecommons.org/licenses/by/4.0
Gradert karakterskala eller Bestått/Ikke bestått? Refleksjoner fra studenter i høyere IT utdanning
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.
Copyright (c) 2025 Per Lauvås Jr, Omid Mirmotahari, Tengel Sandtrø, Yngvar Berg, Yngve Lindsjørn https://creativecommons.org/licenses/by/4.0
Learning Agile with Generative AI: Student Experiences in a Project-Based Course
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.
Copyright (c) 2025 Rolando Gonzalez, Per Lauvås Jr, Marculescu Bogdan, Jefferson Seide Molléri, Hanne Sørum https://creativecommons.org/licenses/by/4.0
Veiviser: scoping challenges in the deployment of augmented reality navigation for impaired individuals
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.
Copyright (c) 2025 Georgios Marentakis, Morten Gustavsen , Jan-Erik Vinje, Tom-Robert Bryntesen https://creativecommons.org/licenses/by/4.0
Konsortiemyopi: Hvordan kollektivt eierskap skaper strategisk vranglås i digitale plattformer
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.
Copyright (c) 2025 Hong-Ngan Hua, Thomas Østerlie https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Sunniva Bjerke, Carolina Gunnesdal, John Krogstie, Dimitra Chasanidou https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Aida Ashrafi, Katja Enberg, Bjørnar Tessem https://creativecommons.org/licenses/by/4.0
Bruk av Kunstig Intelligens (KI) i Norsk Offentlig Sektor: Status 2025
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
Copyright (c) 2025 John Krogstie https://creativecommons.org/licenses/by/4.0
Kunstig intelligens som medspiller i kunnskapsarbeid
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.
Copyright (c) 2025 Synnøve Bjørgum, Tara Kenny, Karen Sæbbo Osmundsen, Kjersti Berg Danilova https://creativecommons.org/licenses/by/4.0
An Automated Solution to Exam Scheduling at Østfold University College
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.
Copyright (c) 2025 Ole Marcus Hansen, Radin Morik, Shvan Nasser, Stine Vågnes, Monica Kristiansen Holone https://creativecommons.org/licenses/by/4.0
Generative AI for Reducing Language and Administrative Barriers for Immigrants in Public Services
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.
Copyright (c) 2025 Emma Ju Blix, Ilaria Crivellari https://creativecommons.org/licenses/by/4.0
Emerging Knowledge Networks in the Architectural Transformation of Digital Infrastructures
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.
Copyright (c) 2025 Egil Øvrelid, Andrea Helmersen, Madin Cej https://creativecommons.org/licenses/by/4.0
A Representative Sampling Approach to Few-Shot Transfer Learning
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.
Copyright (c) 2025 Rusith Chamara Hathurusinghe Dewage, Habib Ullah, Muhammad Salman Siddiqui, Rakibul Islam, Fadi-Al Machot https://creativecommons.org/licenses/by/4.0
Data Replication for Distributed Emergency Management Systems
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.
Copyright (c) 2025 Øyvind Hanssen https://creativecommons.org/licenses/by/4.0
Passive bandwidth estimation for fog computing
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.
Copyright (c) 2025 Kjeti Omang, Carsten Griwodz, Frank Johnsen https://creativecommons.org/licenses/by/4.0
Flexible Visualization of Eye Tracking Data to Aid Assessment of Visuospatial Neglect
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.
Copyright (c) 2025 Alexander Holt, Joel Mattias Tømmerbakk, Tor Ivar Hansen, Xiaomeng Su https://creativecommons.org/licenses/by/4.0
Revisiting Structured Representation Use in LLM
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.
Copyright (c) 2025 Michal Chudoba, Tomasz Wiktorski, Sergey Alyaev https://creativecommons.org/licenses/by/4.0
Explaining Monocular Depth Estimation - Diving into Regional Differences in the Prediction
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.
Copyright (c) 2025 Sabine Fischer, Øyvind Ødegård, Frank Lindseth, Steven Yves Le Moan, Gabriel Hanssen Kiss https://creativecommons.org/licenses/by/4.0
Selective Temporal Fusion using Recurrent Attention for End-to-End Autonomous Driving
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.
Copyright (c) 2025 Andreas Bentzen Winje, Florian Wintel, Gabriel Hanssen Kiss, Frank Lindseth https://creativecommons.org/licenses/by/4.0
FragFM: Fragmented Fourier Matrix for multi-seasonality extraction in Univariate Long-Term Time Series Forecasting
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
Low Rank Fourier Transform Seasonal Decomposition
Copyright (c) 2025 Aditya Dey, Jonas Kusch, Fadi Al Machot https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Robin Isfold Munkvold, Niels Pálmi Skovsgård Jónsson https://creativecommons.org/licenses/by/4.0
Generative AI tools to propose summative assessment tasks
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.
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
Advancing Cybersecurity Education in the Era of Generative AI
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.
Copyright (c) 2025 Selina Demi, Bjørnar Guttormsen, Kevin Forberg Reinaas, Mats Hansen, Sofie Sand https://creativecommons.org/licenses/by/4.0
Introducing Coding Co-Pilots in an Introductory Programming Course - the Beginning of the End?
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.
Copyright (c) 2025 Lars Michael Kristensen, Sven-Olai Høyland https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2025 Jonas Julius Harang, Hans Georg Schaathun, Kenny Solevåg-Hoti https://creativecommons.org/licenses/by/4.0
Fra gjetting til refleksjon på eksamen med flervalgsoppgaver
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.
Copyright (c) 2025 Omid Mirmotahari, Iuliia Perminova, Yngvar Berg https://creativecommons.org/licenses/by/4.0
Kva seier generativ KI om kvalitet i IT-utdanning?
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.
Copyright (c) 2025 Guttorm Sindre, Line Kolås, Robin Isfold Munkvold https://creativecommons.org/licenses/by/4.0