The Longitudinal BCI-Based Evaluation of Thinking Patterns: Neuro-Cognitive Generative AI Implications in Higher Education

Gaining acceptance of Generative AI (GenAI) in higher education has revolutionized how students access information, solve problems, gain knowledge, and engage in learning activities. Although previous research has extensively studied the pedagogical advantages and challenges of using GenAI, there is a lack of focus on the long-term implications of GenAI for learners' thinking processes. With the advent of Brain-Computer Interface (BCI) technologies and low cost electroencephalography (EEG) systems, there is an unprecedented opportunity to explore the effects of prolonged interactions with GenAI on attention, cognitive engagement, creativity, decision-making, and metacognitive behavior. In this paper, a framework for the longitudinal evaluation of changes in students' thinking processes due to regular use of GenAI in higher education settings is proposed. The study aims to bridge the gap between educational technology research and neurocognitive assessment. The paper provides a theoretical context for the importance of GenAI in learning, summarizes recent research on using EEG in education, and proposes a longitudinal study design that can capture neural markers linked to the development of different cognitive behaviors. The proposed framework will address SDG 4 & SDG 9 and will provide evidence based learning about the impact of technology on human cognition and learning in higher education.