Supervisor: Gabrielle Hansen
 

Project Description: This project investigates a new introductory IT course, specifically developed for LUR students. The course differs radically from traditional IT intro courses by focusing on mastery-based learning without exams, exercises, or lectures. The goal is to offer a new approach that stimulates independent learning and fosters a sense of accomplishment among students.
 

Teaching Assistants from Excited: Oda Bang-Olsen, Joakim Pettersen Vassbakk, Elias Borge Svinø

Supervisor: Line Kolas
 

Project Description: This project focuses on analyzing programming games for adults, aiming to evaluate the suitability of these games for learning programming. The work includes data collection and cleaning to exclude games that do not meet the inclusion criteria. Additionally, researchers and teaching assistants will play and analyze the games using an analytical framework, which is under development. The goal is to understand how these games can serve as learning tools for beginner programmers.
 

Teaching Assistants from Excited: Anna Holden Jacobsen, Tiril Sjøberg, Håkon Ringen

Supervisors: Aamna Rais Ahmed, Line Kolas
 

Project Description: This project is a systematic literature review of "Blended Project-Based Learning" (BPBL) in higher computer science education. The goal is to analyze how BPBL is defined, implemented, and evaluated in research. Teaching assistants will be assigned research articles and use an analytical framework to map definitions, implementation methods, and research outcomes related to BPBL. The findings will contribute to understanding BPBL’s impact in computer science education and guide future teaching practices.
 

Teaching Assistants from Excited: Karan Singh Sandhu, Anton Tveito

Supervisor: Trond Aalberg
 

Project Description: This project explores the use of ChatGPT as a tool in introductory programming education. Through observations and interviews with students, the researchers will examine how ChatGPT can support learning processes and understanding in programming. The work includes an Excited workday, where researchers and students collaborate to develop an understanding of ChatGPT's potential and limitations as a support tool in teaching.
 

Teaching Assistants from Excited: Hanna Jacobsen, Anton Tveito, Tiril Sjøberg

Supervisor: Rune Hjelsvold
 

Project Description: This project, led by Cluster 1 in Excited, analyzes learning outcome descriptions for Norwegian bachelor's programs in IT. The aim is to assess the quality of the descriptions, classify them according to Bloom's taxonomy, and examine the coverage of "soft skills." The project involves downloading and analyzing the latest descriptions, comparing them to previous versions, and performing a consistency check to group similar descriptions across study programs and institutions.
 

Teaching Assistants from Excited: Birk Strand Bjørnaa, Karan Singh Sandhu, Thomas Nitsche, Cathrine Libæk

Supervisor: Birgit R. Krogstie
 

Project Description: This project is the first step in a larger initiative aimed at integrating sustainability meaningfully into bachelor’s and master’s theses at IDI. Currently, sustainability is mentioned in learning outcomes to varying degrees, but students often struggle to engage with it in a meaningful way. The goal of this phase is to collect and structure relevant resources that will be made available on a website by the end of March. These resources should help students, supervisors, course coordinators, and examiners incorporate sustainability into their work beyond a superficial requirement. Key tasks include gathering existing frameworks, consulting stakeholders, and compiling examples of sustainability discussions in theses.
 

Teaching Assistants from Excited: Anton Tveito, Camilla Szwarc Jensen and Hanne Heggdal

Supervisors: Guttorm Sindre (NTNU), Mariusz Nowostawski (NTNU), Line Kolås (Nord), Robin Munkvold (Nord)
 

Project description: This project investigates the differences and similarities in responses from various AI tools over time. Every month, the same five questions—two requiring pure text responses and three involving text and Python—are submitted to ChatGPT, Bing Copilot, and Gemini. The research focuses on how responses vary between tools, users, and over time, as well as whether there is a progression in answer quality. So far, semantic similarity analysis has been conducted using Word documents and a Python script with Sentence-BERT. The next step is a deeper analysis, both manual and automated, starting with the fifth question. Key aspects include examining the topics, required competencies, and suitability of the proposed tasks, as well as analyzing the quality, complexity, and variation in the suggested solutions.

Supervisor: Guttorm Sindre

Project Description: “Learning to learn”, students are often expected to figure out most things on their own and receive little guidance on study techniques. The idea behind this project is to create resources, such as videos, that students can use. These resources may include tips for effective study techniques, how Blackboard/Canva can support these, and be presented in a format that is engaging, widely used, and therefore useful for many students.

To avoid creating a large number of resources that end up barely being used, the plan is to start by developing an MVP (Minimum Viable Product). This would be a single resource with content we believe could benefit many students, used to test the concept with the target group. Each iteration will provide insights that can be used to further develop the concept and expand it into more resources that actually work.

Learning Assistants from Excited: Petra Flores Halvorsen

Supervisor: Dag Olav Kjellmo

Project Description:
Support learning by leveraging AI technology without reducing the cognitive effort required for effective learning. We have developed a framework that provides feedback of high pedagogical quality. For Spring 2026, the focus is more on testing the framework and further developing it.

A new AI tool is being introduced into ITGK. Recently, some of our learning assistants have worked on a project for the course TDT4110 Introduction to Information Technology. They have contributed to developing a system integrated into the ITGK exercise setup, which uses AI to generate questions and provide feedback on students’ code through a chatbot.

With the increased use of generative AI, there has been a decline in attendance at learning assistant sessions and less use of traditional guidance. The goal of the solution is therefore to use AI in a way that supports learning by providing questions and hints rather than direct answers, thus mimicking the classical learning process.

Going forward, the idea is to use feedback from user testing to further develop the tool and improve the prompting of the LLM/chatbot. We are excited to bring this solution to students.

Learning Assistants from Excited: Håkon Ringen, Jessica Liu

Supervisor: Trond Aalberg

Project Description:
Created a question bank to be tested in DigiWind and then further developed. This will be done by refining the wording and selection of questions, ensuring quality through testing, and gathering feedback from course instructors. The work also includes translating it into Norwegian and exploring how such a questionnaire can be made into an open resource.

Learning Assistants from Excited: Thea Slemdal Bergersen, Aurora Nordseth Fredriksen

Supervisors: Monica Divitini and Swetlana Fast

Project Description:
The main task is to analyze project applications, create an overview of proposed initiatives, and identify the applicants. It also involves interviewing instructors from completed mini projects and conducting surveys. The goal is to determine whether the methods or tools developed or tested in the mini projects are still in use, have been further developed, or have been shared with colleagues. The learning assistants’ tasks have therefore largely consisted of developing interview guides, conducting interviews, and transcribing them.

Learning Assistants from Excited: Anton Tveito, August Middelkoop, Jørgen Holt, Jørgen Holt

Supervisor: Somayeh Bayat Esfandani

Project Description:
Assessing peers’ code and projects can be particularly challenging for computer science students, especially when they lack sufficient subject knowledge, an issue consistently confirmed in our previous research. To address this, we aim to integrate artificial intelligence into the peer assessment process. The proposed system will provide subject-specific conceptual support, concrete examples, and step-by-step guidance to help students evaluate their peers’ projects more effectively. In this way, the assessment process becomes not only easier but also a valuable learning opportunity, enabling students to develop a deeper understanding and provide higher-quality, more constructive feedback. To implement this approach, we plan to develop a Visual Studio extension to be used in a programming course in the upcoming semester.

Learning Assistants from Excited: August Middelkoop, Johan Knudsen