New Research Behind Real Adaptivity in Language Games
Imagine you’re playing a game while learning a language. You have your cute little owl (don’t worry, ours won’t threaten your streak), gathering intel, talking to characters, making choices, trying to stay fully immersed in this adventure when, without realizing it, the game quietly steps in and gives you a hand. Maybe it whispers the first letters you need. Perhaps it speaks slower? Or gives you an easier quest. Almost as if it knew you were about to hit a (virtual) wall. Too good to be true? (As good as sunshine on a Belgian winter day?) That’s the promise of adaptive learning. Not an empty one though, there’s a new empirical study that proves we can deliver on that promise (the adaptivity one, not the sunshine, yet). Researchers at KU Leuven set out to investigate whether Language Hero, our narrative-based game, can automatically assess students’ language performance in real time and adapt tasks to their level accordingly. And can it do that? [Spoiler] Yes it can. Why is this research important? From a theoretical standpoint, research in second language acquisition is clear: we learn to speak a language by using it in meaningful interaction, whether with another person or with spoken-dialogue systems. But for such systems to truly support learning, they need to adapt to each student’s needs and proficiency in real time (check Bibauw et al., 2022 to learn all about dialogue systems). Everyone promises these adaptive and personalized AI tools. But we need empirical evidence to show how such adaptation works and whether it improves language learning. This is precisely what KU Leuven researchers investigated in their recently published study. To examine how the built-in adaptivity in Language Hero could predict successful task completion, they analyzed students’ oral language using theoretically grounded measures (check out Koizumi & In’nami, (2024) for a deep dive into these measures). More specifically, Then they asked: Do these measures predict if a learner will succeed on the next task? They found out that: And why does this matter? Adaptability can support all learnersData-driven learner models like the one in Language Hero can improve micro-adaptability. It can offer better individualized support. For instance, for lower proficient students, the system can provide more detailed hints, adjust linguistic complexity, or present alternative tasks, all based on real-time indicators of students’ performance. Teachers get data they can useThese models expand pedagogical possibilities by providing interpretable linguistic data through dashboards and visualizations of students’ proficiency and progress. Teachers save time, choose what matters the most, and decide where to focus their attention. AI you can trustThis research offers a transparent, theory-informed learner model (as opposed to an opaque “black box” of off-the-shelf chatbots), that we hope can improve trust in AI-powered applications for language learning Spoken dialogue systems can do more than “talk back” …or provide speaking practice. In a way, they can tell when a learner is about to struggle before they do. And that’s when adaptivity kicks in. This study is a milestone for us. It shows that our evidence-based adaptivity is moving in the right direction. And we intend to keep building it, validating it, and sharing it with all our students. Original article Cornillie, F., Gijpen, J., Metwaly, S., Luypaert, S., & Van den Noortgate, W. (2025). Towards adaptive spoken dialogue systems for language learning: Predicting task completion from learning process data. CALICO Journal, 42(3). https://utppublishing.com/doi/10.3138/calico-2025-0035 Other Work Cited Bibauw, S., François, T., & Desmet, P. (2022). Dialogue Systems for language learning: Chatbots and beyond. In N. Ziegler & M. González-Lloret (Eds.), The Routledge handbook of second language acquisition and technology (pp. 121–135). Routledge. https://doi.org/10.4324/9781351117586 Koizumi, R., & In’nami, Y. (2024). Predicting functional adequacy from complexity, accuracy, and fluency of second-language picture-prompted speaking. System, 120, 103208. https://doi.org/10.1016/j.system.2023.103208
5 things we improved about Linguineo Pro that you need to know
Summer is coming to an end. *Cries in Belgian weather* While we’ve enjoyed the occasional sunny moments, we have been working *read: the whole year* behind the scenes to improve Linguineo Pro. We know, summer isn’t done yet, but we sure are with the update of Linguineo Pro. We have done a major update to Linguineo Pro, incorporating all the most important previous user feedback. Yay! We literally can’t wait to share it with you! So, keep on reading to find out what we improved.
OKAN pupils learn Dutch with voicebot POL
If you move here from another country, the best thing you can do is of course learn the language. Easier said than done? Not with POL, our personalized voicebot tailored to OKAN students.
OKAN stands for ‘Onthaalklas voor anderstalige nieuwkomers’, or classes for non-native newcomers between ages 6 and 18 who have not yet mastered Dutch. The goal is to support them and make their learning experience more enjoyable. We partnered with D-Teach and KU Leuven’s Centrum voor Taal en Onderwijs to do that! Currently, we are in the testing phase of POL, and we would love to share you a little bit more about him.