Exploring the impact of AIGC on digital reading behavior: insights from nudge theory and cognitive neuroscience

Purpose Artificial intelligence (AI)-assisted tools have become widely used to improve reading efficiency. This study aims to systematically investigate how AIGC-generated summaries modulate cognitive load and reading performance across varying levels of text difficulty, conceptualizing such summaries as digital nudges grounded in cognitive load theory and nudge theory. Design/methodology/approach This study used a 2 × 2 within-subjects design in which participants completed reading comprehension tasks under four conditions: easy versus difficult texts and the presence versus absence of AIGC-generated summaries. During the tasks, electroencephalography (EEG) signals, behavioral measures and questionnaire data were simultaneously collected. Findings Findings revealed that AIGC-generated summaries reduced theta/alpha ratio (TAR), shortened reading time, improved comprehension accuracy and alleviated subjective cognitive load across both levels of text difficulty. Interaction analyses further demonstrated that these load-reducing effects were more pronounced under high-difficulty conditions. Originality/value To the best of the authors’ knowledge, this study provides the first neurophysiological evidence for the cognitive benefits of AIGC-generated summaries in alleviating cognitive load during reading, thereby addressing limitations in prior behavior-based research. The findings not only offer practical guidance for the design of AI-assisted intelligent reading systems but also provide empirical evidence to support personalized summary delivery in libraries and digital reading platforms.