Education Domain
Research in the education domain is rapidly evolving, driven by the integration of artificial intelligence, particularly large language models (LLMs) and multimodal AI, to enhance teaching, learning, and assessment. Current efforts focus on leveraging these models for personalized learning, automated feedback, and analyzing student data (e.g., through time series analysis and emotion detection) to improve educational outcomes. This work highlights the need for responsible AI development in education, addressing ethical concerns and ensuring equitable access to these powerful technologies, while also exploring the use of other AI models such as those for visual question answering and nonverbal immediacy analysis.
Papers
Big Data and Education: using big data analytics in language learning
Vahid Ashrafimoghari
Data Science and Machine Learning in Education
Gabriele Benelli, Thomas Y. Chen, Javier Duarte, Matthew Feickert, Matthew Graham, Lindsey Gray, Dan Hackett, Phil Harris, Shih-Chieh Hsu, Gregor Kasieczka, Elham E. Khoda, Matthias Komm, Mia Liu, Mark S. Neubauer, Scarlet Norberg, Alexx Perloff, Marcel Rieger, Claire Savard, Kazuhiro Terao, Savannah Thais, Avik Roy, Jean-Roch Vlimant, Grigorios Chachamis