Application of artificial intelligence to measure attention levels in university students
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 c...
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| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | Ver en el sistema de origen Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
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| Sumario: | 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. |
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