Verlumun Journal of AI, Gender and Cultural Studies
Visual Stereotypes in AI: A Content Analysis of AI-Generated Imagery Representing Traditional Nigerian Gender Roles
Background: The rapid integration of Generative Artificial Intelligence (GenAI) into visual media has revolutionised digital content creation but has also raised critical concerns about the replication of societal biases. While GenAI tools offer unprecedented opportunities for cultural storytelling, they often function as algorithmic mirrors that reflect and amplify historical prejudices. In the context of the global South, there is a growing risk that these technologies facilitate a form of digital colonisation by reducing complex identities to Western-centric tropes. Objectives: This study investigated the manifestation of visual stereotypes in AI-generated imagery, with a focus on traditional Nigerian gender roles. It aimed to identify predominant gender tropes, assess the extent of vocational and domestic stereotyping, and evaluate the degree of cultural authenticity and ethnic flattening in the outputs of leading generative models. Methodology: A quantitative content analysis was conducted on a purposive sample of 200 images generated by Midjourney (v6.0) and DALL-E 3. Standardised, neutral prompts were used to generate depictions of traditional Nigerian men and women. The images were coded across dimensions of setting, labour type, power dynamics, and cultural attire. A Chi-square test of independence was performed to determine the statistical significance of gender-based role assignments. Results: The findings revealed a persistent reliance on traditional binaries. Female subjects were predominantly in domestic settings (72%) and assigned to reproductive labour, while male subjects were in public or vocational spheres (68%). Statistically significant disparities were also found in visual gaze and social positioning, with men depicted as authoritative figures and women as passive participants. Furthermore, a qualitative assessment highlighted a high frequency of ethnic flattening and the persistence of the primitive trope. Conclusion: The study concludes that current GenAI models reinforce restrictive, patriarchal, and colonial-era representations of Nigerian identity. These findings underscore the urgent need to decolonise AI datasets and integrate indigenous epistemologies into model development to ensure more inclusive and culturally accurate digital representations.
Artificial Intelligence, Algorithmic Bias, Digital Colonialism, Gender Stereotypes, Nigerian Culture, Content Analysis.
Copyright (c) 2025 Murjanatu Mohammed Abba (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Leveraging Artificial Intelligence to Quantify the Digital Infodemic: A Longitudinal Analysis of Pathogen-Specific Misinformation in Nigeria
Background: The digital infodemic poses a systemic threat to the eradication of viral pathogens, particularly in regions such as Nigeria, with complex digital ecosystems. While traditional content analysis is limited by scale, Artificial Intelligence (AI) offers new possibilities for real-time infodemiology. This study utilises AI to quantify the longitudinal prevalence and social engagement of misinformation regarding HIV, Hepatitis B (HBV), Polio, and COVID-19. Methods: We deployed an AI-driven analytical framework to process 7,234 data points from Nigerian digital news and social media (2021–2026). The study utilised Natural Language Processing (NLP) for automated thematic classification and VADER sentiment analysis to calculate Average Trust Scores for clinical interventions. Regression models were used to measure the correlation between algorithmic amplification and the social transmission rate of misinformation. Results: AI-based sentiment scoring revealed a significant trust deficit regarding chronic viral infections (HBV/HIV) that persisted longer than misinformation about acute pathogens. Automated keyword analysis identified a high volume of traditional/unverified cure mentions, achieving a social reach 40% higher than that of official public health communiqués. Conclusion: By leveraging AI, this study establishes the first data-driven baseline for the Nigerian digital infodemic. The findings demonstrate that AI is not just a tool for analysis but a necessary component of modern public health surveillance to combat vaccine hesitancy and promote evidence-based virological treatment.
Artificial Intelligence; Infodemiology; Digital Health Surveillance; Natural Language Processing; Viral Pathogens; Sub-Saharan Africa; Public Health Communication
Copyright (c) 2025 Sunday Francis Leman, Luka Zakka Toholde (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Gender Disparities in Research Return Rates: The Moderating Influence of AI Self-Efficacy and Methodological Design
Background: The leaky pipeline of data collection remains a significant challenge as research infrastructures pivot toward Artificial Intelligence (AI) and automated tracking. While women traditionally exhibit higher prosocial participation in research, the introduction of AI-driven methodology may act as a demographic filter, creating a methodological gender gap that threatens data inclusivity. Objective: This study investigates the moderating influence of AI Self-Efficacy and Gender on research return rates. The goal is to identify if technological invasiveness reverses traditional participation advantages, thereby skewing the data pools used to train and evaluate AI systems. Methodology: A factorial survey experiment was conducted with a stratified sample of N=450 participants (n=150 Male, n=150 Female, n=150 Non-binary). Participants were randomly assigned to one of three research scenarios: a traditional digital survey (Control), an AI-led laboratory experiment utilizing biometric facial analysis (Treatment A), and a longitudinal quasi-experiment using passive AI tracking (Treatment B). The dependent variable, Probability of Return (Pr), was analyzed using a 3x3 Factorial ANOVA and moderated mediation modeling (Hayes Process Model 1) against a 5-item validated AI Self-Efficacy Scale (α = 0.88). Results: The analysis revealed a significant interaction effect between gender and research design (F = 18.54, p < .001, n2 = .14). In the control group, women reported a higher return rate (72.4%) than men (69.8%). However, a crossover effect occurred in Treatment A, where male return rates (62.1%) significantly outpaced female rates (38.5%). AI self-efficacy was found to be a significant gatekeeper for female participants (beta = .58, p < .001) but had no significant impact on male participation (beta = .12, p = .14). This confirms that while men remain resilient to technological complexity, women’s participation is heavily contingent on their technical confidence. Conclusion: The transition to AI-mediated research introduces a tech-tax that disproportionately discourages female participation, leading to male-dominated datasets. To ensure the growth of inclusive research, scholars must prioritize AI trust-building and transparent design. This paper provides a vital roadmap for auditing research methodologies to prevent the systemic exclusion of women from the future of knowledge production.
AI Self-Efficacy; Research Return Rates; Crossover Effect; Gender Disparities; Methodological Design
Copyright (c) 2025 Igwebuike Innocent Olijo (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Gender and Cultural Biases as Determinants of Research Participation Proclivity: Evidence of the Moderating Effect of Study Setting (Digital vs. Face-to-Face)
Background: The evolution of research methodology—from predominantly face-to-face interaction to reliance on digital media platforms—necessitates a comprehensive understanding of factors influencing research participation. While the advantages of both face-to-face and digital data collection are recognised, a critical gap exists in understanding how entrenched gender and cultural biases within a population act as determinants of an individual's willingness to participate in a study, a relationship which the specific research environment (digital vs. face-to-face) may uniquely moderate. Objective: This study aimed to determine the extent to which gender and cultural biases serve as determinants of research participation proclivity and to test the hypothesis that the study setting significantly moderates this relationship. Methodology: A descriptive survey research design was employed. Data were collected using a structured questionnaire administered to a sample of research participants in Nigeria. The results were analysed using descriptive statistics and multiple regression analysis to test the specific hypotheses concerning determination and moderation. Results: The multiple regression analysis utilised a robust model (<span data-math="R^2 = 0.485, p < 0.001">R2 = 0.485, p < 0.001) to evaluate participation determinants. Both gender bias (GB) (<span data-math="\beta = -0.35, p < 0.001">β = -0.35, p < 0.001) and cultural bias (CB) (<span data-math="\beta = -0.28, p < 0.001">β = -0.28, p < 0.001) were confirmed as highly significant negative determinants of participation proclivity, validating the inhibitory nature of socio-cultural factors. The interaction terms revealed that the digital study setting (SS) significantly mitigated these negative effects. Specifically, the positive moderation of GB x SS (<span data-math="\beta = 0.41, p < 0.001">β = 0.41, p < 0.001) and CB x SS (β = 0.32, p = 0.004) demonstrates that the structural features of the online environment effectively mitigate the inhibitory impact of both biases. Conversely, the substantial magnitude of the negative main effects underscores the critical necessity of researcher training and intentional protocol design in face-to-face data collection to achieve equitable participation. Conclusion: The choice of data collection setting (digital versus face-to-face) must be guided by the nature and presence of gender and cultural biases in the target population. Researchers should strategically deploy the digital setting to mitigate the exclusionary effects of these biases on participation, ensuring more representative and higher-quality data collection.
Copyright (c) 2025 Celestine Verlumun Gever (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Algorithmic Mirror: Youth Perception of Gender Bias in AI-Generated and Digital Media Portrayal of Professions
Background: AI-driven digital media platforms and content increasingly mediate how professional and gender roles are portrayed, often inheriting and amplifying pre-existing cultural bias. This phenomenon, which extends the effects of traditional media theories into the algorithmic age, warrants critical investigation, particularly in relation to professional aspirations among young people. Objective: This study critically investigated youth perception of gender bias in the portrayal of professions within AI-generated and digital media content in Nigeria. Methodology: A descriptive survey research design was utilised. Data was collected using a questionnaire administered to a multi-stage sample of 385 respondents drawn from Nigeria. Results were analysed and presented using descriptive statistics. Results: The findings reveal a widespread perception of severe gender segregation in digital media portrayals. Females were overwhelmingly assigned domestic roles as workers (92%) and sex objects (95%), while males were consistently portrayed in high-status, dignifying roles, such as technocrats and entrepreneurs (ranging from 83% to 98% perception). Also, 88% of respondents perceived that science-related professions are predominantly attributed to men, demonstrating that gender bias has been inherited and codified by AI and digital systems. Conclusion: Digital and AI systems function as an algorithmic mirror, accurately reflecting historical cultural bias and, through their engagement-based logic, actively amplifying this bias. This process perpetuates and accelerates gender inequality in professional aspiration and representation among youth by influencing their self-efficacy (SCT) and cultivating a skewed social reality (CT).
Copyright (c) 2025 Chris Ogwu Attah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Applying the Uses and Gratification Theory to AI: A Gender-Based Study of Nigerian University Students' Motivations for Utilising AI in Learning
Background: Artificial Intelligence (AI) tools, particularly generative AI, have rapidly integrated into higher education, significantly affecting learning practices and student behaviour. However, the specific motivations and contexts that drive the adoption of these powerful tools remain unclear, especially when considering demographic and cultural differences in developing countries like Nigeria. Objective: This study aimed to determine the specific gratifications (motivations) that drive the utilisation of AI tools for academic purposes among university students in Nigeria, with a focus on analysing differences based on gender. Methodology: The researcher utilised a quantitative, cross-sectional descriptive survey design grounded in the Uses and Gratification Theory (UGT). The target population was university students in Nigeria who actively use AI tools for learning. The sample consisted of 300 undergraduates who were also users of AI tools. A structured questionnaire served as the instrument for data collection, and the data were analysed using descriptive statistics and independent samples t-tests to test four hypotheses regarding gender influence. Results: The study found that most respondents reported a high extent of AI tool utilisation (76.6%), with ChatGPT being the most utilised platform. Statistical testing revealed a significant gender difference in utilisation and motivation (p < 0.01). Male undergraduates reported significantly higher gratification for Task Efficiency and Problem-Solving (p < 0.001), while female undergraduates reported significantly higher gratification for Academic Support and Stress Reduction (p < 0.001). Furthermore, gratifications sought were strongly and positively correlated with perceived utility (r = 0.76). Conclusion: It was concluded that the utilisation of AI tools is a goal-directed behaviour driven by a hierarchy of specific gratifications, and that a student’s gender is a significant factor in shaping both their frequency of use and the dominant benefits they seek. The findings validate UGT's application to AI and reveal a gendered motivation profile.
Copyright (c) 2025 Chukwuebuka Nicholas Okpala (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Nigerian Media Framing of AI Ethics and Public Trust: A Content Analysis of News Coverage on Artificial Intelligence Development
Background: Artificial Intelligence (AI) is rapidly developing globally, offering significant opportunities but also presenting complex ethical challenges concerning data privacy, bias, and job security. The public's adoption and trust in this technology are heavily mediated by news coverage. Objective: This study examined the newspaper framing of Artificial Intelligence ethics and development in Nigeria and its influence on public trust. Methodology: A mixed-methods approach utilising both content analysis of newspaper articles and a survey of the public was employed. The code sheet and questionnaire served as the instruments for data collection. Descriptive and inferential statistics (including Chi-square analysis) were used in the data analysis, with results presented in tables. Results: The results showed that dominant frames like opportunity, risk, and regulation manifest in Nigerian media reports concerning AI development. It was also found that the type of AI issue involved (e.g., data privacy vs. economic potential) significantly determined the frames used. The result showed that newspaper framing significantly influences public perception and trust in Artificial Intelligence. Conclusion: Frames such as opportunity, risk, and regulation are central to Nigerian media reports on AI ethics. The framing strategy is significantly dependent on the specific AI issue being reported. Furthermore, media framing has a profound impact on the Nigerian public's trust in AI.
Copyright (c) 2025 Chukwuebuka Stephen Nworie (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Efficacy of AI-Driven Adaptive Learning Platforms in Promoting Critical Thinking Skills among Out-of-School Nomadic Children in Northern Nigeria
Background: Despite the growing potential of Artificial Intelligence (AI)-driven adaptive learning platforms in providing highly personalised educational services, the viability of these scalable tools as an effective alternative to human-led instruction for developing critical thinking skills among disadvantaged and hard-to-reach populations, such as out-of-school nomadic children in Nigeria, remains largely unexplored. Objective: This study examined whether an AI-Driven Adaptive Learning (AIL) intervention could achieve non-inferior learning outcomes compared to traditional Face-to-Face (FTF) instruction in improving critical thinking skills among out-of-school nomadic children. Methodology: The researchers conducted a quasi-experiment with 470 participants, utilising a validated critical thinking skills scale as the primary data collection instrument. The study compared two intervention groups: the AIL group and the FTF control group. Results: The results demonstrated that the AIL group achieved learning gains in critical thinking skills that were statistically equivalent to (or non-inferior to) those recorded by the Face-to-Face group. Specifically, the mean post-intervention essential scores of thinking of the AIL group did not fall below a predetermined non-inferiority margin established for the FTF group. Conclusion: AI-driven adaptive learning platforms represent a highly scalable and equally effective alternative to traditional instruction for delivering complex, higher-order cognitive skills, such as critical thinking, to hard-to-reach populations. Establishing this equivalence supports the adoption of AI platforms based on their superior logistical and cost-effectiveness advantages.
Copyright (c) 2025 Matthew Udaya Onuh (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Politics of Parody: A Cultural and Gendered Analysis of Social Media Skits on Irregular Migration in South Africa
Background: The rise of digital platforms has transformed public discourse, establishing social media skits as viral forms of government-aligned advocacy in South Africa. Despite their algorithmic reach and cultural influence, critical investigation into the gendered and cultural politics embedded within these digital narratives concerning irregular migration remains underdeveloped. Objective: This study utilised an integrated framework combining the Theory of Planned Behaviour (TPB) with Narrative Persuasion Theory to critically analyse the cultural narratives, persuasive mechanisms, and gendered portrayals within anti-migration skits, and to examine their impact on the migration intentions of a South African sample. Methodology: A quantitative survey (N=455) to examine participants who were social media users. The study measured impact using the AIUCA (awareness, interest, understanding, conviction and action) persuasion model and TPB constructs (Attitude, Subjective Norms, PBC). Statistical analysis, including moderated regression, was used to test how gender and TPB variables filter the persuasive effect. Result: While skits successfully generated awareness across the sample, deep conviction failed for decided migrants. The study found that the influence of the skits was powerfully moderated by all three TPB constructs (H4-H6) and was significantly dependent on gender (H3). For decided migrants, the skits did not deter migration; instead, they drove adaptive action—specifically, making efforts to migrate legally and avoiding dangerous routes. Conclusion: Social media skits serve as a powerful, culturally saturated medium for digital policy advocacy; however, their efficacy is contingent upon pre-existing psychological and cultural factors. The findings suggest that this digital advocacy does not stop migration; rather, it promotes safer, professionalised migration by filtering the message through gendered social norms.
Theory of Planned Behaviour, South Africa
Copyright (c) 2025 Daniel Gbadebo Adedeji (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Moderating Role of Gender and Culture in the Relationship Between Parenting Styles and Children’s Self-Esteem in Lafia, Nigeria
Background: Parenting practices profoundly influence children’s self-esteem globally. In Nigeria, the prevalence of authoritarian styles is often associated with diminished self-esteem. This study addresses a gap in local literature by investigating the influence of specific parenting styles—authoritative, democratic, and laissez-faire—on children’s self-esteem within the distinctive cultural context of Lafia, Nigeria. Purpose: This research examined the relationship between authoritative, democratic, and laissez-faire parenting styles and children’s self-esteem, specifically analysing the moderating role of gender and culture among residents in Lafia Metropolis, Nasarawa State, Nigeria. Methodology: A quantitative correlational design was employed. Data were collected via a self-report Likert-scale questionnaire from a purposive sample of 192 children and adolescents (aged 10–18 years) residing in Lafia Metropolis. Ethical compliance included securing parental informed permission and child assent. Quantitative data were analysed using descriptive statistics, Pearson r correlation, and Hierarchical Multiple Regression to test for the moderating effects. Results: All three styles significantly influence children's self-esteem. Democratic parenting showed the strongest positive correlation (r = 0.515; p < 0.05) by promoting independence and autonomy. Gender and cultural context were confirmed to significantly moderate these relationships, particularly showing that the positive effect of authoritative parenting was stronger for male children. Strong communal cultural norms buffered the negative effect of laissez-faire parenting. Conclusion: Parenting styles significantly influence children’s self-esteem in Lafia Metropolis, but their effectiveness is critically contextualised by the child's gender and the family's cultural adherence. These findings underscore the necessity of considering gender and culture when examining parenting outcomes in non-Western settings.
Copyright (c) 2025 Kwagtser Aondofa Peter (Author) https://creativecommons.org/licenses/by-nc-sa/4.0