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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]
Copyright of Frontiers in Education is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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PubType: Academic Journal
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  Data: 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 &lt; 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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  Data: &lt;i&gt;Copyright of Frontiers in Education is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=191603845
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3389/feduc.2026.1753873
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        Text: English
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        Type: general
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      – SubjectFull: Educational technology
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      – TitleFull: Application of artificial intelligence to measure attention levels in university students.
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              Text: 2026
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