Metropolia AI at Pedaforum 2025: Demos, Dialogue and a Lot of Excitement

Metropolia and the University of Eastern Finland jointly organized the IWCLUL workshop (International Workshop on Computational Linguistics for Uralic Languages), which brought together researchers of Finno-Ugric languages from across Europe. The workshop was held as part of the international ACL community and provided an up-to-date overview of language technology research on Uralic languages, especially in the era of artificial intelligence and large language models. A broad range of Metropolia’s research Metropolia’s AI research was exceptionally well represented at the workshop. Four full papers produced at Metropolia were accepted for the workshop, addressing both pedagogical and language technology topics from multiple perspectives. The paper From NLG Evaluation to Modern Student Assessment in the Era of ChatGPT: The Great Misalignment Problem and Pedagogical Multi-Factor Assessment (P-MFA) examined the impact of artificial intelligence on assessment practices in higher education. The study highlighted the so-called Great Misalignment Problem, where assessment no longer measures what it is intended to measure when students can produce high-quality outputs using generative language models. The paper introduced a new Pedagogical Multi-Factor Assessment (P-MFA) model, which emphasizes the learning process, diverse forms of evidence, and pedagogical transparency rather than single final products. In a paper co-authored with Waseda University, Benchmarking Finnish Lemmatizers across Historical and Contemporary Texts evaluated Finnish lemmatization tools on both contemporary and historical data. The study made use of the Project Gutenberg corpus and, for the first time, included the Trankit tool in a comparison of Finnish lemmatization. A key finding was that Murre preprocessing significantly improves lemmatization results for dialectal and historical texts, while its impact on modern Finnish is minimal. In the image, Aki Morooka is talking about normalization experiments. A timely application of artificial intelligence to foresight was presented in the paper ORACLE: Time-Dependent Recursive Summary Graphs for Foresight on News Data Using LLMs. The study developed a new method in which temporally evolving recursive summary graphs are constructed from news data using large language models. The ORACLE approach enables the analysis of developments and emerging trends in news content by combining temporal structure with language model–based summarization. The fourth paper, co-authored with the University of Helsinki, Evaluating OpenAI GPT Models for Translation of Endangered Uralic Languages: A Comparison of Reasoning and Non-Reasoning Architectures, focused on machine translation for endangered Uralic languages. The study compared reasoning-based and non-reasoning architectures of OpenAI’s GPT models and analyzed their performance on low-resource languages. The results provide valuable insights into which types of language model solutions are best suited for supporting small and endangered languages. Metropolia’s lightning talks: agile openings on topical themes Metropolia’s visibility at the IWCLUL workshop was not limited to full research papers but extended strongly to the lightning talks as well. The lightning talks provided a concise yet substantively rich overview of rapidly developing research directions that are central to language technology for Uralic and other small languages. The lightning talk UralicMCP: Turning LLMs into Experts in Endangered Languages with MCP presented a new Model Context Protocol (MCP)–based extension to the UralicNLP library. The core idea of UralicMCP is to connect large language models with rule-based language technology tools such as a morphological analyzer, inflector, lemmatizer, and dictionaries. This makes it possible for language models to perform NLP tasks even in endangered Uralic languages for which they have little to no training data. Experiments presented in the lightning talk showed that, with MCP, language models can succeed in tasks that would otherwise be impossible for them. Lev Kharlashkin addressed the current state of the Karelian language. The second lightning talk, From Toki Pona to Uralic: A Grammar-Constrained Pipeline for Low-Resource Language Generation, addressed a methodological approach to training language models for low-resource languages. The work used an extremely controlled language such as Toki Pona as a testbed for grammatically guided synthetic data generation. The goal was not Toki Pona itself, but a scalable method that can be transferred to morphologically rich Uralic languages. The lightning talk highlighted how explicit grammatical constraints and validated synthetic data can compensate for the lack of large datasets. The lightning talk Did Karelian Survive the Year? A Small Data Update provided an up-to-date snapshot of the digital vitality of the Karelian language. The talk presented a lightweight yet repeatable data collection process used to analyze Karelian-language online content, particularly in news and article texts. The results showed that Karelian is actively produced online, especially in short news formats, and that even a small but regularly updated dataset can provide meaningful insights into the current state of an endangered language. The fourth Metropolia lightning talk, Evaluating Finnish Dialect Normalization in GPT Models with and without Reasoning, focused on dialect normalization of Finnish using language models. The study compared traditionally fine-tuned GPT-style models with models explicitly equipped with reasoning (chain-of-thought). The results showed that strong pretraining in the Finnish language is more crucial than explicit reasoning, and that reasoning-based fine-tuning can even degrade normalization performance in this task. The lightning talk highlighted important insights into when and how reasoning capabilities should be leveraged in language technology applications. Artur Roos explained what Uralic languages can learn from synthetic languages. From research to practice: AI in support of small languages The IWCLUL workshop highlighted how Metropolia’s AI research brings together theoretical linguistics, practical language technology, and societal impact. Both the full research papers and the lightning contributions demonstrated that large language models are not viewed at Metropolia as standalone, general-purpose solutions, but rather as tools that can be guided, constrained, and complemented with linguistic expertise. The common denominator across Metropolia’s presentations was the reality of endangered languages: limited datasets, rich morphology, and the need for transparent and maintainable solutions. Whether the focus was on rethinking assessment in education, translation of Uralic languages, the digital vitality of Karelian, or normalization of dialectal Finnish, the research emphasized approaches that work even when ready-made data or perfect models are not available. The workshop reinforced Metropolia’s role in the international language technology community as an actor that brings together artificial intelligence, open-source development, and the needs of language communities. At the same time, it demonstrated that research on small languages is not a side track of AI development, but one of its most important testbeds: it is precisely there that the assumptions, limitations, and design choices underlying language models are forced into the open.

Under the leadership of Development Manager Mika Hämäläinen, Metropolia’s AI team is developing various solutions based on large language models to address the organization’s challenges. The core idea is to solve real problems in a user-centered and agile manner. Since large language models are constantly evolving, there is no longer a need to develop the AI itself — instead, our task is to adopt AI and integrate it into everyday life in an easy-to-use form. Our team currently includes software developers Lev Kharlashkin, Melany Macías Morán and Leo Huovinen, as well as student interns Yehor Tereshchenko, Sheng Tai and Aki Morooka. The tools we have developed are named OpintoHain, Oracle, Grant Writing Assistant, Curriculum Tool, and Moodle AI plugin. OpintoHain OpintoHain was developed as part of a project led by Sonja Saarikivi, with the goal of creating a tool for lifelong learners. The target audience consists of individuals external to Metropolia who wish to update their skills and study at Metropolia — whether by taking a single course or potentially pursuing a suitable Master’s degree. We responded to the challenge by developing a chatbot that understands the course offerings of Metropolia’s Open University. The tool is powered by a RAG (Retrieval-Augmented Generation) model that is familiar with Metropolia’s courses and degree programs. It also includes a multi-agent system with dedicated agents for course and degree recommendations, as well as for study guidance. The OpintoHain tool is available for testing on Metropolia’s website. Oracle Foresight has taken on an increasingly important role at Metropolia — everyone is expected to anticipate future developments, but how? We set out to address this challenge with the Oracle tool, which ingests online content such as news articles and job postings. Based on this input, we can analyze the data using vectorization and clustering techniques. We have already developed methods for identifying weak signals and megatrends, detecting drivers of change, conducting data-driven scenario work and implementing an automated multi-agent version of the Delphi method. The guiding idea is that AI processes foresight data into a ready-to-use format, so that the end user can gain maximum benefit from the insights, even if they have little to no prior knowledge of foresight practices themselves. In putting Oracle into practical use to support real-world applications, we are supported by the foresight working group led by Maani Nyqvist, along with foresight expert Marita Huhtaniemi. Grant Writing Assistant The importance of external funding is growing in the higher education sector. Competition for funding is fierce, and often even strong applications go unfunded. We are developing an AI tool in collaboration with Maarit Haataja, Director of RDI and Project Services, and her team, to enhance Metropolia’s chances of securing external funding. In EU Horizon funding calls in particular, it is crucial that every section of the call for proposals is addressed within the application. Even a strong application can be rejected if it fails to mention even a single sub-point. Grant Writing Assistant automatically analyzes the call for proposals and compares it with the content of the application. Any missing elements are clearly reported to the user, who can then choose to correct them manually or have the AI automatically insert the missing content. The tool is also capable of identifying risks and breaking the project down into work packages. Curriculum Tool Writing curricula is a time-consuming process. Each course-level curriculum should reflect both the goals of sustainable development and Arene competencies. To support this, we developed the Curriculum Tool, which analyzes curricula and visualizes the content of degree programs from the perspectives of Arene competencies and sustainable development. In the development of this tool, Metsälintu Pahkin played a valuable role as a liaison with the degree coordinators. You can read the scientific publication describing the tool for more details. Moodle AI Plugin The Moodle AI Plugin was developed for teachers, enabling them to automatically generate assignments directly in Moodle based on their own course materials. The core idea has been to integrate AI directly into a familiar tool, rather than creating a separate system. Senior Lecturer Tricia Cleland-Silva served as a valuable liaison with the teaching staff during the development process. You can read the scientific publication describing this tool for further insights.

Non-player characters (NPCs) in games have typically followed one rule: players act, and the character reacts - within strict, predefined limits. They often guide a user through a certain story, predefined by a creator. That can be the main storyline, side quest, etc. Even though it can be various, frequently there is not much diversity in the game line through which players go. While it can be fun for the first time, after replaying a game, it becomes boring and even at the start players have limited abilities to go outside the "borders" defined by the developer. That is where AI can help because it can react to almost everything, and this reaction is unique even in the same circumstances. So why not use it in game development to make the final product and user experience even more inspiring!? What happens when that reactivity becomes context-aware, unscripted, and emotionally intelligent? That's exactly what a group of Metropolia students set out to explore during the Supercell AI Hackathon, hosted by Junction in Helsinki this May. Their entry, developed in just over 24 hours, landed an impressive 3rd place among nearly 50 international teams and offered a glimpse into the future of interactive AI. The team after receiving the "Supercell award". The teammates worked on the Myllypuro campus for two days: on Friday, they brainstormed about ideas and attempted to find inspiration and on Saturday, very active prototype development. After several hours of efficient discussion, they came up with an idea of making the following reactive-NPC prototype: “Purr-suit of Attention” illustration showing the cat and its witch AI-companion A game where the player isn't in control - the AI is The team's prototype, Purr-suit of Attention, flips the classic power dynamic between player and NPC. Instead of commanding the world, the player steps into the paws of a curious cat living with an AI-powered fantasy witch - a non-playable character that responds dynamically to everything the cat does. The task of the game is to explain to the AI what the cat wants to use different activities and make the NPC do a certain action. So, how cat-witch (player to NPC) interaction goes? Meow? She speaks. Knock over a bottle? She sighs, laughs, or reacts with surprise. Jump on a table? She might ignore it, become indignant, or think that the cat is hungry. Meows, scratching the front door, staring at it for a long time? The NPC will definitely think that the cat wants to go inside, but will she open the door? It depends on the mood and a bunch of other possible factors that cannot be described through "if-else statements" The cat meows at a locked door as the witch offers to open it. The twist? None of these reactions are scripted. They're generated in real time using a large language model, meaning each play through is unique and emotionally rich. And the most impressive thing is that the player has no limits or boundaries! "Cat" can do whatever, and the AI will interpret the player's actions itself. How it works? The system relies on a full-stack integration of modern tools: 1. Unity 6 and C# for gameplay mechanics, API calls and animation control, etc. Unity Editor scene view with the cat, witch, and setup. 2. Python (Flask) for backend logic, handling game events and states, LLM calls; 3. Google Gemini for generating context-aware intent, choosing animation to play and speech. Split-screen of the Python backend code alongside the Unity Play window and debug logs. 4. Sesame Voice for natural, spoken responses Actions taken by the cat are tagged and flagged as events (meow, jump, scrap, etc) and states (looking at and near objects). These are processed by the backend, where the AI evaluates them in context using techniques like high-low temperature, prompt enhancement (system, static and dynamic prompts; moving "IMPORTANT" flag), reattempts, and random selection according to heightening parameters from the AI response. The result? A witch NPC that doesn't just respond - she feels alive, true (but a little bit "slow-witted person" because the response from free LLM's API takes some time😊). Unity editor with cat script visualizing and backend answer in logs The Metropolia team worked closely, handling different roles from backend AI logic to design and animation scripting. The final prototype - developed from scratch in just one day - impressed judges and participants for its creativity, interactivity, and potential for further development. More Than a Game The cat sleeps under the task list while the AI-witch teases, “Already sleeping kitty?” The significance of this project goes beyond the podium. It reflects a broader shift happening in AI and game design: toward responsive, emotional, and emergent behavior in digital characters. As large language models become more accessible, fast and controllable, the line between code and personality continues to blur. It isn't just a whimsical experiment, it's a prototype for a world where games don't just entertain - they converse, react, and surprise. 🎮 Watch the gameplay demo 📄 Junction submission 🏆 Award ceremony moment Final cozy art: the witch sips a warm drink by the fire as the cat naps. The contents of this blog reflect the collective effort of Metropolia students (Yehor Tereshchenko, Artur Roos, Unai San Segundo, Kartik Patel) participating in the hackathon. With gratitude to Metropolia for giving an opportunity to join from Myllypuro's co-working places (especially on Saturday), for previous knowledge, and bringing team members together as first-year students; also, thanks to Supercell, Junction, and all participants.