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Application of artificial intelligence to measure attention levels in university students.

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Title: Application of artificial intelligence to measure attention levels in university students.
Authors: Edquén Barboza, Wilmer Alexander1 (AUTHOR), Carlos Flores Ramirez, Roberto1 (AUTHOR), Luna Villanueva, Jhon Marcos1 (AUTHOR), Ramirez Pezo, Yngue Elizabeth1 (AUTHOR), Lévano, Danny2 (AUTHOR), Casildo-Bedón, Nancy Esther2 (AUTHOR) nancy.casildo@upeu.edu.pe
Source: Frontiers in Education. 2026, p1-15. 15p.
Subject Terms: *Artificial intelligence, *Self-evaluation, *Educational technology, *College students, *Student engagement, Attention testing, Psychological techniques
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:2504284X
DOI:10.3389/feduc.2026.1753873