Norwegian version of this page

Andreas Haraldsrud

Associate professor

Email a.d.haraldsrud@kjemi.uio.no

Mobile phone +47 90 62 07 25

Username

Download business card

Academic interests

I work with research, teaching and disciplinary development in chemistry, with a particular emphasis on modelling, representation and conceptual understanding in physical, biochemical and biophysical chemistry. A central focus is how computational methods and simulations can be used to explore and describe physical and biochemical phenomena, and the conceptual challenges that arise in such systems. In this work, I investigate how complex chemical concepts are analysed, interpreted and operationalised, particularly through computational chemistry and artificial intelligence.

In addition, I am interested in experimental and computational approaches to physical and biochemical processes in soft matter, especially lipids and proteins. This includes an interest in how laboratory techniques such as confocal microscopy, TIRF, surface-specific methods and microfluidics can be combined with molecular dynamics, QM/MM and machine learning to describe processes on and within surfaces, membranes and small biomolecular systems.

My main areas of interest can be summarised as follows:

Chemical representation and conceptual knowledge: Cognitive and epistemic models related to physical and biochemical chemistry, with an emphasis on sensemaking and modelling.
Nanobiochemistry and soft materials: Lipids, liposomes, proteins and membranes.
Combined simulation and experiment: Physical and chemical processes on surfaces and in biomolecules.
Disciplinary teaching and development: Chemically grounded instructional design and methodology.

Teaching

Tags: Chemistry education, Programming, Modelling, Surface chemistry, Lipids

Bolland, Sondre; Haraldsrud, Andreas; Jensen, Siri M. & Strømme, Torstein J. F. (2026). Validation of a Pre-CS1 Concept Inventory. ACM Transactions on Computing Education. doi: 10.1145/3858344. Full text in Research Archive Show summary
As programming has become a prominent component of K–12 education in large parts of the world, incoming university students increasingly enter higher education with prior programming experience. To inform instructors of how far students have already progressed into the introductory programming (CS1) curriculum at the point of entry, we developed the Prior Informatics Knowledge Assessment (PIKA), a diagnostic assessment tool targeting elementary CS1 concepts. This paper presents a validity argument for PIKA as a concept inventory by integrating multiple sources of evidence. Test content validity is supported through curriculum triangulation and iterative expert review. Response process evidence derives from think-aloud interviews ( \(n=10\) ), examining whether items elicit intended conceptual reasoning. Internal structure is evaluated using confirmatory factor analysis and item response theory on large-scale response data ( \(n=1{,}355\) ), assessing dimensionality and item function. Evidence based on relations to other variables is provided by correlating PIKA scores with CS1 final examination performance ( \(n=280\) ). Results indicate that PIKA responses are largely governed by a single latent trait corresponding to introductory programming knowledge. Model fit indices support an essentially unidimensional structure, and think-aloud findings suggest that most tasks require relevant CS1 knowledge to be answered correctly. PIKA scores show a moderate positive correlation with CS1 exam performance, indicating that PIKA measures knowledge that meaningfully relates to CS1 learning outcomes. At the same time, the analyses reveal limitations. Certain constructs—particularly the use of for -loops with lists—appear underrepresented in some K–12 curricula, limiting the diagnostic value of multiple items targeting this content. In addition, item response theory results and response distributions suggest a shortage of difficult items, reducing PIKA's precision for students with advanced prior knowledge. These limitations will be targeted for refinement in upcoming PIKA administrations and changes will be studied in future validation work.
Haraldsrud, Andreas & Wilson, Steven Ray Haakon (2026). From Memorisation to Mechanistic Reasoning in Biochemistry with METAL: An Integrated Framework for Course Design. ChemRxiv. ISSN 2573-2293. doi: 10.26434/chemrxiv.15008073/v1. Full text in Research Archive Show summary
Content-heavy biochemistry courses can reward memorisation without supporting students in constructing chemical explanations of biomolecular phenomena. This Perspective presents METAL (Mechanistic Explanation and Transfer through Aligned Learning), an integrated framework for redesigning content-heavy courses around explanation and transfer. We illustrate the framework through a fourth-semester biochemistry course organised around three recurring explanatory resources: intermolecular interactions and bonding, thermodynamic driving forces, and electron density. Examples from enzyme kinetics, structural modelling, weekly tasks, and an oral examination show how these resources were reused across increasingly demanding contexts. METAL offers instructors a practical route from topic coverage to courses in which students repeatedly build, test, and transfer mechanistic chemical explanations.
Haraldsrud, Andreas & Odden, Tor Ole Bigton (2025). Bridging Artificial Intelligence and Real Intelligence: Self-Scaffolding Computational Modeling with Generative AI in Chemistry. Journal of Chemical Education. ISSN 0021-9584. doi: 10.1021/acs.jchemed.5c00657. Full text in Research Archive Show summary
Chemistry education researchers have, for many years, explored different ways of learning chemistry through modeling and open-ended problem-solving. With the emergence of generative artificial intelligence (GenAI) tools such as ChatGPT, students now have access to dynamic scaffolds that can potentially support them in modeling. However, we still know little about how these tools function within such contexts. This study investigates how students in higher education use GenAI to engage in complex computational modeling in chemistry. Using cognitive clinical interviews with think-aloud protocols, the role of GenAI in students’ modeling activities is analyzed through the lens of distributed cognition. The findings from this study reveal four distinct patterns of GenAI use: (1) Leveraging AI to retrieve and clarify information, (2) Using GenAI to test and critique ideas, (3) Outsourcing thinking to the AI, and (4) GenAI output exceeds student understanding. Productive interactions occurred when students actively orchestrated the AI as part of a distributed cognitive system, using it to retrieve, evaluate, and build upon their own reasoning. In contrast, unproductive use occurred when students outsourced thinking to the AI or were overwhelmed by outputs they could not integrate into their existing knowledge. These findings suggest that effective use of GenAI in chemistry education depends not only on technical proficiency, but also on students’ ability to structure prompts, evaluate AI output, and retain control of the problem-solving process. We argue that the use of GenAI should be explicitly addressed in chemistry instruction and propose that students be taught to engage with GenAI as part of a distributed cognitive system─ – retaining executive control, providing appropriate context, and iteratively refining their inquiries─ – to support meaningful engagement and learning in chemistry.
Haraldsrud, Andreas (2025). Simuleringer og beregninger i kjemi – med datamaskinen som laboratorium. Kjemi. ISSN 0023-1983. p. 26–29. Full text in Research Archive
Haraldsrud, Andreas & Odden, Tor Ole Bigton (2024). Using feedback loops from computational simulations as resources for sensemaking: a case study from physical chemistry. Chemistry Education Research and Practice (CERP). doi: 10.1039/d4rp00017j. Full text in Research Archive Show summary
Sensemaking is an important way of learning and engaging in science. Research has shown that sensemaking activities, such as questioning, hypothesizing, and model building, are pivotal in developing critical thinking and problem-solving skills in science education. This paper investigates the role of computational simulations in facilitating sensemaking in chemistry education, specifically examining how these simulations can sustain the sensemaking process. Through a detailed case study in a physical chemistry course, we explore the interplay between students' real-world experiences, theoretical knowledge, and computational simulations. This analysis reveals that computational simulations, by providing interactive and visual representations of chemical phenomena, can create a conducive environment for sensemaking, where students actively engage in exploring and resolving conceptual uncertainties. Based on these results, we argue that computational tools, when effectively integrated into educational settings, can facilitate sensemaking in science education.
Bolland, Sondre Sæther; Haraldsrud, Andreas; Jensen, Siri Moe; Strömbäck, Filip; Styve, Arne Gerhard & Tøssebro, Erlend [Show all 8 contributors for this article] (2024). The Nordic Prior Knowledge Test in Programming: Motivation, Development and Preliminary Results. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. 4(4). doi: 10.5324/nikt.6216. Full text in Research Archive Show summary
With recent updates to Norway's national curriculum, computational thinking and programming has become a core part of the K-12 education, leading to an influx of students entering higher education with prior programming experience. This shift has the potential to impact the teaching of ICT at universities, as foundational knowledge could allow for the introduction of more advanced topics earlier. However, the quality of programming education varies, making it essential to assess students' prior knowledge. To address this need, the Nordic Prior Knowledge Test in Programming was designed to assess incoming students' programming proficiency in the basic elements of the introductory programming course (CS1). This study details the rationale for assessing incoming students, the development and content of the test, and the preliminary results from its 2024 administration. The test was completed by 3,038 students (2,661 after data pruning) across eight higher education institutions in Norway. Results indicate a mean score of 39.9%, with a significantly higher performance among students exposed to the new curricular model (50%). Despite these gains, a substantial proportion of students scored at the lower end of the scale, highlighting the ongoing need for foundational programming instruction. Although most students will benefit from completing the standard CS1 course, a notable subset of students achieved high scores (14.7% scoring above 90%), suggesting the potential value of accelerated or alternative learning pathways, such as an advanced CS1 course or direct progression to CS2.
Haraldsrud, Andreas & Odden, Tor Ole Bigton (2023). From Integrated Rate Laws to Integrating Rate Laws: Computation as a Conceptual Catalyst. Journal of Chemical Education. ISSN 0021-9584. 100(5), p. 1739–1750. doi: 10.1021/acs.jchemed.2c00881. Full text in Research Archive Show summary
When learning chemistry, students must learn to extract chemical information from mathematical expressions. However, chemistry students’ exposure to mathematics often comes primarily from pure mathematics courses, which can lead to knowledge fragmentation and potentially hinder their ability to use mathematics in chemistry. This study examines how computation can affect students’ blending of cognitive resources and their framing of mathematics in a chemical context. How students analytically and numerically interpret mathematical expressions in chemistry are examined through 14 individual clinical interviews with undergraduate chemistry students. The analysis was done by performing a thematic analysis of the interviews through a theoretical lens provided by the blended processing framework. This analysis reveals that computation and iterative thinking might serve as a catalyst for conceptual understanding and blending of cognitive resources. These findings suggest that we should evaluate how computational approaches could be leveraged to give students a better understanding of both mathematical models and the chemical concepts illustrated through these models.
Haraldsrud, Andreas Drolsum & Tellefsen, Cathrine Wahlstrøm (2018). Programmering for fysikkens skyld. Fra Fysikkens Verden. ISSN 0015-9247. 80(3), p. 70–75. Full text in Research Archive

View all works in NVA

Skogvoll, Vidar & Haraldsrud, Andreas (2026). Lære med KI. Universitetsforlaget. ISBN 9788215076928. 208 p. Full text in Research Archive
Nersveen, Lars; Conradi, Stig; Haraldsrud, Andreas; Helvig, Camilla & Skylstad, Viljar Alain (2025). Modus Naturfag. Aschehoug & Co. ISBN 9788203412622. Full text in Research Archive
Haraldsrud, Andreas & Sundnes, Joakim (2025). Programmering for naturvitenskap og matematikk. Universitetsforlaget. ISBN 9788215066646. 539 p. Full text in Research Archive
Haraldsrud, Andreas; Sandtorv, Alexander Harald & T., Hushovd Odd (2022). Kjemi 2. Aschehoug & Co. ISBN 9788203319495. 480 p. Full text in Research Archive
Haraldsrud, Andreas Drolsum; Sandtorv, Alexander Harald; Hushovd, Odd T. & Brandt, Harald (2021). Kjemi 1. Aschehoug & Co. ISBN 9788203317637. 440 p. Full text in Research Archive
Haraldsrud, Andreas Drolsum; Sveinsson, Henrik Andersen & Løvold, Henrik Hillestad (2020). Programmering i skolen. Universitetsforlaget. ISBN 9788215034287. 320 p. Full text in Research Archive
Haraldsrud, Andreas Drolsum (2017). Programmering og modellering: Naturvitenskapelig programmering for videregående skole. Realfagsforlaget SA. ISBN 9788269091304. 202 p. Full text in Research Archive

View all works in NVA

Haraldsrud, Andreas (2026). Thinking Through a Computer: Programming and Simulation in Chemistry Education. doi: https:/www.ucd.ie/chem/newsevents/schoolofchemistryseminarseries/. Full text in Research Archive
Haraldsrud, Andreas (2026). Hjernen er fortsatt stjernen: Læring i KI-ens tidsalder. Full text in Research Archive
Skogvoll, Vidar; Sand, Odd Petter & Haraldsrud, Andreas (2024). S3E2: KURT fikser formene - Forelesningen. [Internet]. https://open.spotify.com/episode/6VhcqrLwz8OIYmFrzJRO1P. Full text in Research Archive
Skogvoll, Vidar; Lindstrøm, Tom Louis; Benth, Fred Espen & Haraldsrud, Andreas (2024). Real læring - Realistfabrikken: Hvordan lage matematiker. [Radio]. Podcast. Full text in Research Archive
Haraldsrud, Andreas (2024). Chemistry Nobel Price Lecture 2024: The Mysteries of Proteins – Where Real Life Meets Artificial Intelligence. Full text in Research Archive
Siddiq, Fazilat; Berg, Tonje Karoliussen; Haraldsrud, Andreas; Scherer, Ronny; åsgård, ellen & Erstad, Ola [Show all 7 contributors for this article] (2024). Algoritmisk tenkning og samarbeidslæring - hvordan jobbe med programmering i skolen. In Berg, Tonje Karoliussen & Siddiq, Fazilat (Ed.), Algoritmisk tenkning og samarbeidslæring. Hvordan jobbe med programmering i skolen. Gyldendal Akademisk. ISSN 9788205598928. Full text in Research Archive
Haraldsrud, Andreas (2024). Modellering med programmering i naturfag. Naturfag. ISSN 1504-4564. Full text in Research Archive
Haraldsrud, Andreas; Maasø, Arnt; Lerheim, Birgitte; Gregers, Tone Fredsvik; Mirmotahari, Omid & Aronsen, Jan Magnus [Show all 8 contributors for this article] (2024). Rart eller innovativt? Å finne kreativitet i undervisningen. Full text in Research Archive
Skogvoll, Vidar; Aasen, Anna Hjertvik; Huang, Sylvia Jing Yi & Haraldsrud, Andreas (2023). Real læring - ChatGPT: Slik bruker de beste studentene roboten. [Radio]. Podcast. Full text in Research Archive
Haraldsrud, Andreas (2022). Simuleringer og modeller i naturfag. Naturfag. ISSN 1504-4564. Full text in Research Archive
Haraldsrud, Andreas Drolsum & Tellefsen, Cathrine Wahlstrøm (2021). Ressurser for lærere og kompetansepakker: Innhold og ideer bak. Full text in Research Archive
Haraldsrud, Andreas Drolsum (2021). Erfaringer fra lærerkurs og bruk av kompetansepakker. Full text in Research Archive
Haraldsrud, Andreas (2021). Teaching Programming in Science (keynote). Full text in Research Archive
Haraldsrud, Andreas & Hansen, Audun Skau (2021). Exploring chemistry with programming and numerical experiments. Full text in Research Archive
Haraldsrud, Andreas (2021). How does computation and numerical methods shape how students think and make sense of chemistry? Full text in Research Archive
Haraldsrud, Andreas (2024). Thinking Through a Computer – Simulations, Models and Representations in Chemistry. Universitetet i Oslo. Full text in Research Archive Show summary
This dissertation examines the transformative role of computational tools in chemistry education, focusing on how these tools can deepen students' understanding of chemical phenomena and engage students in sensemaking. The research highlights the importance of integrating mathematical and chemical knowledge through simulations and models, which allow students to explore and visualize the microworld of particles. By engaging with these computational methods, students not only improve their scientific problem-solving skills but can also develop a more profound and lasting understanding of scientific models. The study also investigates the role of generative artificial intelligence (GenAI) in this context, showing how GenAI can act as an external cognitive tool, aiding students in problem-solving and computational thinking. The findings suggest that thoughtful integration of these technologies in education can foster a deeper and more meaningful learning experience in chemistry.

View all works in NVA

Published Aug. 17, 2018 10:34 AM - Last modified Feb. 12, 2026 3:47 PM

Research groups

CCSE - Center for Computing in Science Education