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Gender and Functional Differentiation in Generative AI Usage among Malaysian Higher Education Student

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Title: Gender and Functional Differentiation in Generative AI Usage among Malaysian Higher Education Student
Language: English
Authors: Fahd Ali Raza (ORCID 0000-0003-4577-6083), Abtar Darshan Singh (ORCID 0000-0003-4525-5966), Rabia Anwar (ORCID 0009-0004-3672-4825), Jonathan Jeevan Strivinas Kovilpillai (ORCID 0000-0001-7517-2809), Analisa Binti Hamdan (ORCID 0000-0003-4225-129X), Fumiko Konno (ORCID 0009-0009-4510-9142), Vaikunthan Rajaratnam (ORCID 0000-0001-7498-4995), Murali Raman (ORCID 0000-0003-2392-628X), Husna Hafiza Razami (ORCID 0000-0002-2845-2004)
Source: European Journal of STEM Education. 2026 11(1).
Availability: Lectito Journals. Wassenaarseweb 20, 2596 CH, The Hague, The Netherlands. Tel: 31-70-2190600; e-mail: info@lectitojournals.com; Web site: http://www.lectitopublishing.nl
Peer Reviewed: Y
Page Count: 41
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Gender Differences, Artificial Intelligence, Technology Uses in Education, College Students, Intellectual Disciplines, Foreign Countries, STEM Education, Cultural Differences
Geographic Terms: Malaysia
ISSN: 2468-1954
2468-4368
Abstract: Despite achieving gender parity in higher education enrolment (60% female), Malaysia faces an emerging digital divide in how students use artificial intelligence. This study examined whether gender predicts task-specific ChatGPT usage patterns among Malaysian students (n = 443), employing latent profile analysis and multinomial logistic regression on CC BY 4.0 licensed Global ChatGPT Student Survey data (October 2024-February 2025). Four distinct usage profiles emerged: Selective Users (14.9%), Moderate Adopters (31.8%, coding-focused), Academic Enthusiasts (33.0%, text-focused), and Comprehensive Users (20.3%). Gender significantly predicted specialized profile membership (χ² = 19.47, p < 0.001). Males concentrated in coding-focused use (OR = 0.48 for females, p = 0.007), females in text-focused use (OR = 1.89, p = .020), while Comprehensive Users exhibited gender parity. Exploratory analyses indicate a tentative pattern of larger gender gaps in technical AI use in STEM fields (33.4 percentage points in Applied Sciences) versus Social Sciences (12.7 points), though small cell sizes (n=3 for female Natural Sciences students) prevent definitive conclusions. Profiles predicted domain-specific skill development with large effects (η² = 0.18-0.33). Findings reveal that equal access masks unequal functional engagement, with implications for gender-segregated occupational pathways that warrant further investigation with larger samples.
Abstractor: As Provided
Notes: https://doi.org/10.17632/nv2343nwsb.2
Entry Date: 2026
Accession Number: EJ1505190
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Gender and Functional Differentiation in Generative AI Usage among Malaysian Higher Education Student
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  Data: Lectito Journals. Wassenaarseweb 20, 2596 CH, The Hague, The Netherlands. Tel: 31-70-2190600; e-mail: info@lectitojournals.com; Web site: http://www.lectitopublishing.nl
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  Data: 41
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  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Gender+Differences%22&quot;&gt;Gender Differences&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Artificial+Intelligence%22&quot;&gt;Artificial Intelligence&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Technology+Uses+in+Education%22&quot;&gt;Technology Uses in Education&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22College+Students%22&quot;&gt;College Students&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Intellectual+Disciplines%22&quot;&gt;Intellectual Disciplines&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Foreign+Countries%22&quot;&gt;Foreign Countries&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22STEM+Education%22&quot;&gt;STEM Education&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Cultural+Differences%22&quot;&gt;Cultural Differences&lt;/searchLink&gt;
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  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Malaysia%22&quot;&gt;Malaysia&lt;/searchLink&gt;
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  Data: 2468-1954&lt;br /&gt;2468-4368
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Despite achieving gender parity in higher education enrolment (60% female), Malaysia faces an emerging digital divide in how students use artificial intelligence. This study examined whether gender predicts task-specific ChatGPT usage patterns among Malaysian students (n = 443), employing latent profile analysis and multinomial logistic regression on CC BY 4.0 licensed Global ChatGPT Student Survey data (October 2024-February 2025). Four distinct usage profiles emerged: Selective Users (14.9%), Moderate Adopters (31.8%, coding-focused), Academic Enthusiasts (33.0%, text-focused), and Comprehensive Users (20.3%). Gender significantly predicted specialized profile membership (χ&#178; = 19.47, p &lt; 0.001). Males concentrated in coding-focused use (OR = 0.48 for females, p = 0.007), females in text-focused use (OR = 1.89, p = .020), while Comprehensive Users exhibited gender parity. Exploratory analyses indicate a tentative pattern of larger gender gaps in technical AI use in STEM fields (33.4 percentage points in Applied Sciences) versus Social Sciences (12.7 points), though small cell sizes (n=3 for female Natural Sciences students) prevent definitive conclusions. Profiles predicted domain-specific skill development with large effects (η&#178; = 0.18-0.33). Findings reveal that equal access masks unequal functional engagement, with implications for gender-segregated occupational pathways that warrant further investigation with larger samples.
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  Label: Abstractor
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  Data: As Provided
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  Data: https://doi.org/10.17632/nv2343nwsb.2
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  Data: 2026
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      – Text: English
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      Pagination:
        PageCount: 41
    Subjects:
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: College Students
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      – SubjectFull: Cultural Differences
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      – SubjectFull: Malaysia
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