This paper details an LLM-assisted system designed to support curriculum writing within a Finnish higher education institution. Developed over 18 months through iterative prototyping, workshops, and user testing with faculty, the tool functions as a collaborative partner. It provides structured suggestions and analyzes course content for alignment with institutional goals and standards like UN SDGs, aiming to reduce educator cognitive load while keeping humans central to the process. The paper presents the system’s technical architecture, findings from user feedback (including quotes and evaluation metrics), and discusses its potential to aid complex educational planning compared to generic AI tools.

Lemmatization is crucial in natural language processing (NLP) for languages like Finnish, where complex inflectional morphology significantly affects downstream tasks such as parsing, named entity recognition, and sentiment analysis. This study evaluates the accuracy and efficiency of several Finnish lemmatizers, utilizing the Project Gutenberg corpus, which includes diverse Finnish-language texts from different periods. Notably, this is the first study to employ Trankit for Finnish lemmatization, providing novel insights into its performance. Additionally, the integration of Murre preprocessing has been emphasized, demonstrating substantial improvements in lemmatization results. By comparing traditional and neural-network-based approaches, this paper aims to provide insights into tool selection for NLP practitioners working with Finnish based on dataset characteristics and processing constraint.

In this paper, we leverage an exclusive English dataset to train diverse multilingual classifiers, investigating their efficacy in adapting to Finnish data. We employ an exclusively English classification dataset of UN Sustainable Development Goals (SDG) in an education context, to train various multilingual classifiers and examine how well these models can adapt to recognizing the same classes within Finnish university course descriptions. It’s worth noting that Finnish, with a mere 5 million native speakers, presents a significantly less-resourced linguistic context compared to English. The best performing model in our experiments was mBART with an F1-score of 0.843.