The rise of generative artificial intelligence (AI) and large language models (LLMs) have posed significant challenges to humanities, science, and education. To meet these challenges, our team leverages the concepts and tools of LLM to investigate language learning and representation, along with other subjects in science and engineering. We take advantage of the features and affordances offered by AI and the latest technologies to study the interaction between technologies and human users, and to investigate how we can build better AI-empowered platforms to facilitate student learning, critical thinking, and human interaction, and to promote ethical and socially responsible use of AI and technologies. By comparing AI and human speakers, we can gain insights into pedagogical designs that best suit individual needs in evidence-based, personalized educational contexts. Our findings integrate behaviour, cognition and the brain in light of LLMs and human development.
Publications
Xu, Q., Peng, Y., Nastase, S., Chodorow, M., Wu, M., & Li, P. (2025). Large language models without grounding recover non-sensorimotor but not sensorimotor features of human concepts. Nature Human Behavior, 9, 1871–1886 (2025).
Gao, C., Ma, Z., Chen, J., Li, P., Huang, S., & Li, J. (2025). Increasing alignment of large language models with language processing in the human brain. Nature Computational Science.
Gu, C., Nastase, S., Zada, Z., & Li, P. (2025). Reading comprehension in L1 and L2 readers: Neurocomputational mechanisms revealed through large language models. npj Science of Learning, 10, Article 46.
Gu, C., Peng, Y., & Li, P. (2025). Advances in bilingualism as a dynamic process: Thirty years of exploration in bilingual mind and brain. Journal of Neurolinguistics, 77. 101288. (Special issue: Cognitive Processing in Bilinguals: 30 Years Later).
Yu, S., Gu, C., Huang, K., & Li, P. (2024). Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension. Science Advances, 10, eadn7744, 1-12.
Kong, X., Huang, K., Li, P., & Zhang, L. (2025). Toward generalizing visual brain decoding to unseen subjects. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Singapore.