Ding Ning holds a PhD from Kangwon National
University, South Korea. Her research focuses on modeling learning and behavioral
mechanisms in human–AI collaborative contexts. From an interdisciplinary
perspective integrating educational science and design research, she examines
cognitive appraisal, motivational formation, and behavioral regulation
processes in generative AI environments, aiming to explain how technology
influences learning and work adaptation through multi-path models.

Methodologically, she employs mixed methods
combining Structural Equation Modeling, fuzzy-set Qualitative Comparative
Analysis, and grounded theory, with the goal of transforming complex learning
and behavioral processes into interpretable mechanism-based models. In recent
years, her research topics have included AI-assisted learning, technology
adoption and resistance, cognitive load, and meaning construction. Related work
has been published in journals such as Scientific Reports, Acta Psychologica,
Frontiers in Psychology, and Sustainability, and she also serves as a reviewer
for multiple international academic journals.

Her research seeks to promote interdisciplinary
integration among educational science, behavioral modeling, and intelligent
technology studies, providing theoretical and methodological support for
digital learning and human–AI collaborative educational contexts.