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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| Formato: | Artículo |
| Lenguaje: | inglés |
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2026
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| _version_ | 1881508923340488704 |
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| author | Edquén Barboza, Wilmer Alexander Carlos Flores Ramirez, Roberto Luna Villanueva, Jhon Marcos Ramirez Pezo, Yngue Elizabeth Levano Rodriguez, Danny Casildo Bedon, Nancy Esther |
| author_browse | Carlos Flores Ramirez, Roberto Casildo Bedon, Nancy Esther Edquén Barboza, Wilmer Alexander Levano Rodriguez, Danny Luna Villanueva, Jhon Marcos Ramirez Pezo, Yngue Elizabeth |
| author_facet | Edquén Barboza, Wilmer Alexander Carlos Flores Ramirez, Roberto Luna Villanueva, Jhon Marcos Ramirez Pezo, Yngue Elizabeth Levano Rodriguez, Danny Casildo Bedon, Nancy Esther |
| author_sort | Edquén Barboza, Wilmer Alexander |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/864 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/8642026-10-30T17:01:06ZApplication of artificial intelligence to measure attention levels in university studentsEdquén Barboza, Wilmer AlexanderCarlos Flores Ramirez, RobertoLuna Villanueva, Jhon MarcosRamirez Pezo, Yngue ElizabethLevano Rodriguez, DannyCasildo Bedon, Nancy Estherartificial intelligence (AI)behavioral engagementcomputer visiondeep learninghigher educationreal-time feedbackObjective 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.Articlehttps://public-pages-files-2025.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1753873/pdfhttps://doi.org/10.3389/feduc.2026.175387310.3389/feduc.2026.1753873https://hdl.handle.net/20.500.15037/864W7127054600enFrontiers in Education2504-284X2026-02-022026-06-13T09:38:56Z2026-06-13T09:38:56Z |
| spellingShingle | artificial intelligence (AI) behavioral engagement computer vision deep learning higher education real-time feedback Edquén Barboza, Wilmer Alexander Carlos Flores Ramirez, Roberto Luna Villanueva, Jhon Marcos Ramirez Pezo, Yngue Elizabeth Levano Rodriguez, Danny Casildo Bedon, Nancy Esther Application of artificial intelligence to measure attention levels in university students |
| title | Application of artificial intelligence to measure attention levels in university students |
| title_full | Application of artificial intelligence to measure attention levels in university students |
| title_fullStr | Application of artificial intelligence to measure attention levels in university students |
| title_full_unstemmed | Application of artificial intelligence to measure attention levels in university students |
| title_short | Application of artificial intelligence to measure attention levels in university students |
| title_sort | application of artificial intelligence to measure attention levels in university students |
| topic | artificial intelligence (AI) behavioral engagement computer vision deep learning higher education real-time feedback |
| topic_facet | artificial intelligence (AI) behavioral engagement computer vision deep learning higher education real-time feedback |
| url | https://public-pages-files-2025.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1753873/pdf https://doi.org/10.3389/feduc.2026.1753873 https://hdl.handle.net/20.500.15037/864 |
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