The Algorithmic Loop in Intensifying Brainrot and Reducing Language Learning Focus - Ismail, Nyak Mutia; Lismalinda; Jamilah, Sitti; Tahir, Muhammad (Journal of Nusantara Education, 2026)
Brainrot,defined by a tendency toward excessive micro-scrolling, persistent audio reassurance and an increased fondness for overstimulating short-form,has become a major cognitive phenomenon among young adults. However, it is relatively unexplored how algorithmic recommender systems reinforce this state and what roles this plays in academic processes (e.g. English learning). The current study is based on a quantitative explanatory research design and involves 321 daily TikTok or Instagram users. Four latent constructs were investigated: Algorithmic Exposure, Brainrot Intensity, Cognitive Load and Language Learning Focus. The confirmatory factor analysis showed high both construct validity and the model fits well (CFI =. 956, RMSEA =. 052). Algorithmic exposure was a powerful predictor of brainrot intensity (β =. 72, p <. 001), and this inhibition subsequently had a significant impact on cognitive load (β =. 66, p <. 001). Cognitive load had a relatively large negative influence on language learning attention (β = –. 58, p <. 001). There was no significant direct path from algorithmic exposure to learning focus verifying full mediation by brainrot and cognitive load. This suggests the influence of algorithmically curated digital environments on learning is not immediate, but rather it works its effect through cognitive breakdowns that limit learners’ ability to sustain focus in vocabulary building, reading comprehension and grammar processing. The findings of this study suggest that ELT education should include training in algorithmic literacy and attention management to help people cope with the increasing cognitive demands put forward by short-form social media.