Publications

Ajenaghughrure, I. B., Sousa, S., & Lamas, D. (2025, October). The influence of real-time trust assessment and adaptive feedback on users' trust in Artificial intelligence: Autonomous vehicle case study. In Proceedings of the 36th Annual Conference of the European Association of Cognitive Ergonomics (EACE) (pp. 1-4). 

Mets, J., Hooshyar, D., & Bauters, M. (2025, October). Human-Centered AI for Predicting Critical Thinking: Modeling Learners' Application of Reasoning Standards with Bayesian Networks. In Proceedings of the 36th Annual Conference of the European Association of Cognitive Ergonomics (EACE) (pp. 1-8). 

Heilala, V., Araya, R., & Hämäläinen, R. (2025, March). Beyond text-to-text: An overview of multimodal and generative artificial intelligence for education using topic modeling. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (pp. 54-63). 

Setälä, M., Heilala, V., Sikström, P., & Kärkkäinen, T. (2025, March). The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (pp. 74-82). 

Hämäläinen, R., De Wever, B., Heilala, V., Järvinen, M., Lehesvuori, S., Peltoniemi, A., & Rikala, P. (2023). Towards Emotionally Intelligent Scripting: A Methodological and Multimodal Approach to Capture Emotions. In Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning-CSCL 2023, pp. 394-395. International Society of the Learning Sciences. 

Häkkinen, P., Näykki, P., Pijeira-Díaz, H., & Channa, F. (2024). Human-AI collaboration and the future of education. 

Heilala, V., Lehesvuori, S., Hämäläinen, R., & Kärkkäinen, T. (2024, April). Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (pp. 3-10). 

Bauters, M. (2024, July). Embodied Learning–Are We Losing It?. In International Conference on Innovative Technologies and Learning (pp. 309-316). Cham: Springer Nature Switzerland. 

Bauters, M. L., & Abilakimova, A. (2024, June). Ecosystems for knowledge transfer and creation. In ISPIM Innovation Symposium (pp. 1-6). The International Society for Professional Innovation Management (ISPIM). 

Krivich, E., Hooshyar, D., Šír, G., Yang, Y., Bauters, M., Hämäläinen, R., & Kärkkäinen, T. (2025). A Systematic Review of Deep Knowledge Tracing (2015-2025): Toward Responsible AI for Education. 

Hooshyar, D., Šír, G., Yang, Y., Kikas, E., Hämäläinen, R., Kärkkäinen, T., ... & Azevedo, R. (2025). Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room. arXiv preprint arXiv:2504.16148. 

Hooshyar, D., Kikas, E., Yang, Y., Šír, G., Hämäläinen, R., Kärkkäinen, T., & Azevedo, R. (2025). Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods. arXiv preprint arXiv:2504.00615. 

Zhidkikh, D., Heilala, V., Van Petegem, C., Dawyndt, P., Jarvinen, M., Viitanen, S., ... & Hämäläinen, R. (2024). Reproducing Predictive Learning Analytics in CS1: Toward Generalizable and Explainable Models for Enhancing Student Retention. Journal of Learning Analytics, 11(1), 132-150. 

Jääskelä, P., Heilala, V., Kärkkäinen, T., & Häkkinen, P. (2021). Student agency analytics: Learning analytics as a tool for analysing student agency in higher education. Behaviour & Information Technology, 40(8), 790-808. 

Beltrão, G., Goh, S. T., Sousa, S., & Lamas, D. (2025). Community, Identity & Stability? Building trust in facial recognition systems for mass surveillance. Journal of Responsible Technology, 100139. 

Abilakimova, A., Bauters, M., & Afolayan Ogunyemi, A. (2025). Systematic literature review of digital and green transformation of manufacturing SMEs in Europe. Production & Manufacturing Research, 13(1), 2443166. 

Raatikainen, P., Hautala, J., Loberg, O., Kärkkäinen, T., Leppänen, P., & Nieminen, P. (2021). Detection of developmental dyslexia with machine learning using eye movement data. Array, 12, 100087. 

Hooshyar, D., Weng, X., Sillat, P. J., Tammets, K., Wang, M., & Hämäläinen, R. (2024). The effectiveness of personalized technology-enhanced learning in higher education: A meta-analysis with association rule mining. Computers & Education, 223, 105169. 

Hooshyar, D., Azevedo, R., & Yang, Y. (2024). Augmenting deep neural networks with symbolic educational knowledge: Towards trustworthy and interpretable ai for education. Machine Learning and Knowledge Extraction, 6(1), 593-618. 

Hooshyar, D., & Yang, Y. (2024). Problems with SHAP and LIME in interpretable AI for education: A comparative study of post-hoc explanations and neural-symbolic rule extraction. IEEE Access. 

Hooshyar, D., & Druzdzel, M. J. (2024). Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners’ Performance in Computational Thinking. Education Sciences, 14(8), 917. 

Hämäläinen, R., De Wever, B., Sipiläinen, K., Heilala, V., Helovuo, A., Lehesvuori, S., ... & Kärkkäinen, T. (2024). Using eye tracking to support professional learning in vision-intensive professions: a case of aviation pilots. Education and Information Technologies, 29(18), 24803-24833. 

Hooshyar, D., & Yang, Y. (2024). ImageLM: Interpretable image-based learner modelling for classifying learners’ computational thinking. Expert Systems with Applications, 238, 122283. 

Hooshyar, D. (2024). Temporal learner modelling through integration of neural and symbolic architectures. Education and Information Technologies, 29(1), 1119-1146.