In this cutting-edge book, Andy Nguyen, Kshitij Sharma and Ha Nguyen explain how AI can improve our understanding of how people learn. The authors demonstrate how, by analysing multimodal data from different channels, including eye gazes, physiological data and self-reports, AI can provide a clearer picture of what learners think, feel, and do, going far beyond traditional tests or surveys.The authors introduce both classical and deep learning methods for collecting and analyzing data, with a particular focus on multimodal learning process analytics. They explore how large language models and generative AI tools can facilitate human-AI collaboration, while addressing ethical and privacy issues such as fairness, transparency and inclusivity. Integrating theoretical, methodological, and practical perspectives, authors demonstrate how using AI responsibly and thoughtfully can create more inclusive, adaptive and evidence-based learning environments.Artificial Intelligence in Multimodal Learning Process Analytics is a vital reference for scholars and students of learning sciences and pedagogy, particularly those focusing on educational technology, learning analytics and human-computer interaction. It is also highly beneficial for educators and educational policymakers interested in how AI can support learning.

In this cutting-edge book, Andy Nguyen, Kshitij Sharma and Ha Nguyen explain how AI can improve our understanding of how people learn. The authors demonstrate how, by analysing multimodal data from different channels, including eye gazes, physiological data and self-reports, AI can provide a clearer picture of what learners think, feel, and do, going far beyond traditional tests or surveys.The authors introduce both classical and deep learning methods for collecting and analyzing data, with a particular focus on multimodal learning process analytics. They explore how large language models and generative AI tools can facilitate human-AI collaboration, while addressing ethical and privacy issues such as fairness, transparency and inclusivity. Integrating theoretical, methodological, and practical perspectives, authors demonstrate how using AI responsibly and thoughtfully can create more inclusive, adaptive and evidence-based learning environments.Artificial Intelligence in Multimodal Learning Process Analytics is a vital reference for scholars and students of learning sciences and pedagogy, particularly those focusing on educational technology, learning analytics and human-computer interaction. It is also highly beneficial for educators and educational policymakers interested in how AI can support learning.

In this cutting-edge book, Andy Nguyen, Kshitij Sharma and Ha Nguyen explain how AI can improve our understanding of how people learn. The authors demonstrate how, by analysing multimodal data from different channels, including eye gazes, physiological data and self-reports, AI can provide a clearer picture of what learners think, feel, and do, going far beyond traditional tests or surveys.The authors introduce both classical and deep learning methods for collecting and analyzing data, with a particular focus on multimodal learning process analytics. They explore how large language models and generative AI tools can facilitate human-AI collaboration, while addressing ethical and privacy issues such as fairness, transparency and inclusivity. Integrating theoretical, methodological, and practical perspectives, authors demonstrate how using AI responsibly and thoughtfully can create more inclusive, adaptive and evidence-based learning environments.Artificial Intelligence in Multimodal Learning Process Analytics is a vital reference for scholars and students of learning sciences and pedagogy, particularly those focusing on educational technology, learning analytics and human-computer interaction. It is also highly beneficial for educators and educational policymakers interested in how AI can support learning.

In this cutting-edge book, Andy Nguyen, Kshitij Sharma and Ha Nguyen explain how AI can improve our understanding of how people learn. The authors demonstrate how, by analysing multimodal data from different channels, including eye gazes, physiological data and self-reports, AI can provide a clearer picture of what learners think, feel, and do, going far beyond traditional tests or surveys.The authors introduce both classical and deep learning methods for collecting and analyzing data, with a particular focus on multimodal learning process analytics. They explore how large language models and generative AI tools can facilitate human-AI collaboration, while addressing ethical and privacy issues such as fairness, transparency and inclusivity. Integrating theoretical, methodological, and practical perspectives, authors demonstrate how using AI responsibly and thoughtfully can create more inclusive, adaptive and evidence-based learning environments.Artificial Intelligence in Multimodal Learning Process Analytics is a vital reference for scholars and students of learning sciences and pedagogy, particularly those focusing on educational technology, learning analytics and human-computer interaction. It is also highly beneficial for educators and educational policymakers interested in how AI can support learning.