Application of artificial intelligence to measure attention levels in university students - Wilmer Alexander Edquén Barboza; Roberto Carlos Flores Ramirez; Jhon Marcos Luna Villanueva; Yngue Elizabeth Ramírez Pezo; Danny Lévano; Nancy Esther Casildo-Bedón (My University, 2026)
Objective To evaluate the effect of an AI-assisted attentional monitoring system on the perception of class dynamism and the self-assessment of attention among university students. Methods A quasi-experimental design was employed with 160 students divided into an experimental group, which attended classes with attentional monitoring and real-time feedback provided to the teacher, and a control group, which received traditional instruction. Perception of attention and dynamism was assessed pre- and post-intervention using the AIDA questionnaire. Results The experimental group exhibited significant increases in perceived class dynamism and attentional self-regulation compared to the control group ( p < 0.05), demonstrating improved engagement during the session. Conclusion AI-assisted immediate feedback was associated with enhanced attention and student participation, highlighting the potential of such tools to support and strengthen teaching dynamics in in-person educational settings.
Keywords: Dynamism; Perception; Class (philosophy); Control (management); Cognitive psychology; Psychology; Attentional control; Measure (data warehouse); Computer science; Mathematics education; Artificial intelligence; Selective attention; Applied psychology; Dynamics (music); Significant difference; Social psychology; Dynamism; Perception; Class (philosophy); Control (management); Attentional control; Measure (data warehouse); Social Sciences Psychology Developmental and Educational Psychology Flow Experience in Various Fields; Physical Sciences Computer Science Human-Computer Interaction Virtual Reality Applications and Impacts; Life Sciences Neuroscience Cognitive Neuroscience Mind wandering and attention