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Gender Bias in Self-Perception of AI Knowledge, Impact, and Support among Higher Education Students: An Observational Study

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Title: Gender Bias in Self-Perception of AI Knowledge, Impact, and Support among Higher Education Students: An Observational Study
Language: English
Authors: Cristina Cachero (ORCID 0000-0001-6281-8287), David Tomás (ORCID 0000-0003-3287-9366), Francisco A. Pujol (ORCID 0000-0002-6503-2076)
Source: ACM Transactions on Computing Education. 2025 25(2).
Availability: Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/
Peer Reviewed: Y
Page Count: 26
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Gender Bias, Self Concept, Knowledge Level, Undergraduate Students, Student Attitudes, Gender Differences, Foreign Countries
Geographic Terms: Spain
DOI: 10.1145/3721295
ISSN: 1946-6226
Abstract: Objectives: This study investigates gender biases in AI perceptions among university students. It focuses on assessing self-perceptions regarding knowledge, impact, and support, with a specific emphasis on identifying any significant gender differences. The main hypotheses are focused on the existence of gender disparities in AI awareness, perceptions, and attitudes among higher education students. Participants: The study involves 380 participants, enrolled in undergraduate courses across various academic disciplines. Participants are university students with diverse backgrounds in terms of age, academic majors, and prior exposure to AI technologies. Study Methods: This research employs an observational study design. The sample size includes 380 participants. The study utilizes a structured questionnaire as the primary instrument for data collection. Outcome measures focus on variables such as perceived knowledge of AI, perceived impact of AI, and levels of support or apprehension towards AI technologies. Findings: The findings reveal significant gender differences, with females exhibiting lower levels than their male counterparts in the level of perceived knowledge about AI (p<0.005), exposure awareness (p = 0.001), perceived ability to apply AI (p = 0.004), sensitivity towards AI use of private data (p = 0.004), positive impact on society (p = 0.002), support for AI development (p<0.005), and positive expectations towards AI (p<0.005). Statistical analysis, including nonparametric tests, was used to validate these observations. Conclusions: There are notable gender biases in the knowledge and perception of AI among university students. These biases have implications for the future development and adoption of AI technologies, suggesting a need for more gender-inclusive educational strategies in AI. The findings underscore the importance of addressing gender disparities in AI education to ensure equitable access and understanding of these technologies. It is important to integrate gender perspectives in AI curriculum and policy-making to mitigate potential biases and enhance inclusivity in the field of AI.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1476238
Database: ERIC
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  Data: Gender Bias in Self-Perception of AI Knowledge, Impact, and Support among Higher Education Students: An Observational Study
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Cristina+Cachero%22&quot;&gt;Cristina Cachero&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0001-6281-8287&quot;&gt;0000-0001-6281-8287&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22David+Tom&#225;s%22&quot;&gt;David Tom&#225;s&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0003-3287-9366&quot;&gt;0000-0003-3287-9366&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Francisco+A%2E+Pujol%22&quot;&gt;Francisco A. Pujol&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0002-6503-2076&quot;&gt;0000-0002-6503-2076&lt;/externalLink&gt;)
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  Data: Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/
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  Data: 10.1145/3721295
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  Data: Objectives: This study investigates gender biases in AI perceptions among university students. It focuses on assessing self-perceptions regarding knowledge, impact, and support, with a specific emphasis on identifying any significant gender differences. The main hypotheses are focused on the existence of gender disparities in AI awareness, perceptions, and attitudes among higher education students. Participants: The study involves 380 participants, enrolled in undergraduate courses across various academic disciplines. Participants are university students with diverse backgrounds in terms of age, academic majors, and prior exposure to AI technologies. Study Methods: This research employs an observational study design. The sample size includes 380 participants. The study utilizes a structured questionnaire as the primary instrument for data collection. Outcome measures focus on variables such as perceived knowledge of AI, perceived impact of AI, and levels of support or apprehension towards AI technologies. Findings: The findings reveal significant gender differences, with females exhibiting lower levels than their male counterparts in the level of perceived knowledge about AI (p&lt;0.005), exposure awareness (p = 0.001), perceived ability to apply AI (p = 0.004), sensitivity towards AI use of private data (p = 0.004), positive impact on society (p = 0.002), support for AI development (p&lt;0.005), and positive expectations towards AI (p&lt;0.005). Statistical analysis, including nonparametric tests, was used to validate these observations. Conclusions: There are notable gender biases in the knowledge and perception of AI among university students. These biases have implications for the future development and adoption of AI technologies, suggesting a need for more gender-inclusive educational strategies in AI. The findings underscore the importance of addressing gender disparities in AI education to ensure equitable access and understanding of these technologies. It is important to integrate gender perspectives in AI curriculum and policy-making to mitigate potential biases and enhance inclusivity in the field of AI.
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      – SubjectFull: Self Concept
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