Enterprise AI Analysis
Modelling STEM students' intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach
This study investigates factors influencing Ghanaian STEM students' intentions to learn AI, providing insights for educational policy. Using partial least squares structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA), the analysis revealed multiple combinations of individual and contextual factors that drive students' intentions to learn AI. Fostering AI literacy and career relevance boosts motivation. We recommend integrating AI literacy into STEM curriculum and increasing AI resource access as a national policy priority.
Executive Impact & Key Metrics
This research provides crucial insights for the Ministry of Education (MoE) and Ghana Education Service (GES) to build a technologically competent STEM workforce. By understanding the multifaceted drivers and barriers to AI learning among pre-tertiary students, policymakers can design targeted interventions. The findings highlight the importance of cultivating AI-supportive learning cultures, providing adequate resources, and addressing psychological barriers like AI anxiety to prepare future generations for an AI-driven labor market and contribute to Ghana's digital transformation.
66%
Variance in AI Learning Intention (R²)
0.954
Solution Consistency (fsQCA)
0.352
Subjective Norm Impact (β)
-.334
AI Anxiety Impact (β)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Study Design & Sample Size Adequacy
The study employed a descriptive cross-sectional survey with purposive sampling of 233 AI-familiar STEM students. G*Power analysis confirmed the sample size was robust for 8 predictors, a medium effect size, and 95% power, requiring a minimum of 160 respondents.
233 Total RespondentsDual-Method Analytical Approach
Data Collection (Structured Questionnaires)
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PLS-SEM (Direct & Mediating Effects)
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fsQCA (Configurational Pathways)
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Comprehensive Insights
| Feature | PLS-SEM | fsQCA |
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| Causal Pathways |
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| Sample Size |
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| Output Nuance |
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Influence of AI Anxiety
AI anxiety emerged as the strongest negative predictor of AI learning intention (β = -0.334, p < 0.001), significantly inhibiting engagement.
-0.334 AI Anxiety (Beta Weight)| Theoretical Lens | Key Constructs Supported | Unique Contributions |
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| UTAUT2 |
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| Self-Determination Theory (SDT) |
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Perceived Usefulness Paradox
Despite recognizing AI's potential, perceived usefulness unexpectedly showed a negative influence (β = –0.135, p = 0.004) on learning intention. This suggests that while students acknowledge the value of AI, structural barriers like limited access to tools or unclear career prospects may diminish the perceived feasibility of benefiting from AI, thus weakening the link to actual learning intention.
Policy Recommendations Pathway
Foster AI Literacy
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Address AI Anxiety (Workshops)
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Integrate AI into STEM Curriculum
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Provide Resources & Mentorship
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Strengthen Social Norms & Relevance
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Build Technologically Competent Workforce
Variance Explained by Model
The PLS-SEM model accounted for 66% of the variance in AI learning intention (R² = 0.660), demonstrating strong explanatory power.
66% Variance Explained (R²)| Area | Specific Actions | Expected Outcome |
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| Curriculum Integration |
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| Resource Provision |
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| Support & Culture |
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