Prof. Kaska Porayska-Pomsta is a Professor in Adaptive Technologies for Learning and an RCUK Academic Fellow at UCL Institute of Education.
Research Interests
Prof. Porayska-Pomsta’s research focuses on developing fully interactive, adaptive technologies through real-time learner modelling, especially in relation to learners’ affect and motivation, and AI-driven interaction, including natural language and non-verbal communication. She conducts her research in the context of enhanced reality environments and within the serious games paradigm. Her research has two main aims, to provide new and effective modes of learning and teaching and to create research tools that allow researchers, teachers and practitioners to learn about learning, teaching and communication processes between humans and between humans and machines.
As a linguist Prof. Porayska-Pomsta aims to understand socio-cultural as well as cognitive and affective determinants of successful communication and what constitutes success in communication. Prof. Porayska-Pomsta has experience in working with children and adults, with and without special needs, in a variety of domains including social communication, affective self-regulation as well as well-defined subject domains such as physics and mathematics.
Publications
See here for a full list of Prof. Porayska-Pomsta’s publications. Here are some recent ones:
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Porayska-Pomsta, K., & Holmes, W. (2022). Conclusions: Toward ethical AIED. The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates (pp. 271-281). doi:10.4324/9780429329067-14
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Thomas, M., & Porayska-Pomsta, K. (2020). Computational methods in education: Neurocomputational models of cognition versus technology as a tool for supporting learning and teaching. In O. Houdé, G. Bosrt (Eds.), The Cambridge Handbook of Cognitive Development, Volume 3: Education and school-learning domains.. Cambridge University Press.
- Porayska-Pomsta, K. K., & Rajendran, G. (2019). Accountability in human and artificial decision-making as the basis for diversity and educational inclusion. In J. Knox, Y. Wang, M. Gallagher (Eds.), Artificial Intelligence and Inclusive Education (pp. 39-59). Springer Nature. doi:10.1007/978-981-13-8161-4