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The Role of Generative Artificial Intelligence in Developing Cognitive and Research Talent Among Postgraduate Students

Asem Mohammed Ibrahim, Reem Ebraheem Saleh Alhomayani, Azhar Saleh Abdulhadi Al-Shamrani · Journal of Intelligence · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

2/4

Quality (LMQS)

Evidence

Citations

6.32

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.3390/jintelligence14040053

Methodology & findings

Study design

Cross-sectional self-administered survey using a ten-point rating scale.

Sample

N = 214, 10 groups

Primary method

Descriptive statistics (means, standard deviations, percentages); Pearson correlation analysis; Confirmatory Factor Analysis (CFA) using AMOS 26 with Maximum Likelihood Estimation (MLE); Cronbach's alpha and Guttman split-half coefficients for reliability; Independent samples t-tests for two-category variables; One-way ANOVA for variables with more than two categories; LSD post hoc tests; Assessment of assumptions including normality and homogeneity of variances

Main result

The study found that "postgraduate students show a moderate overall level of engagement with Generative AI in developing their cognitive and research abilities, with a mean score of 6.18 (61.80%)". Additionally, "Ethical and responsible use records the highest mean (7.80; 78.03%), reflecting strong awareness of transparency, accuracy, and responsible application when using AI in academic work" and "the lower score for AI-assisted data analysis (5.43; 54.31%) may reflect limited familiarity with advanced analytical tools or uncertainty about AI-generated interpretations".

Reports effect sizes and confidence intervals.

Research paradigm

Positivist empiricism with quantitative survey methodology

Author conclusions

"This study fosters a comprehensive understanding of how postgraduate students engage with Generative Artificial Intelligence (GAI) as part of their cognitive and research development. The findings reveal a research environment in transition, where GAI is increasingly recognized as a valuable academic partner, yet its adoption remains selective and shaped by individual readiness, institutional support, and the nature of the research task." The authors conclude that "while GAI holds significant potential for enhancing cognitive and research talent, its impact depends on thoughtful, responsible, and well-supported integration. To fully realize the benefits of GAI, higher education institutions must invest in training, establish clear policies, and cultivate environments that empower students to use AI confidently, ethically, and creatively."

Risk of bias

Self-report bias (participants' perceptions may not reflect actual behavior); Selection bias (voluntary participation may exclude less engaged students); Social desirability bias (students may overstate ethical awareness); Context-specific bias (single institution, may not generalize); Temporal limitation (cross-sectional design prevents causal inference); Selection bias: Voluntary participation may result in self-selection of students more interested in or familiar with AI tools; Measurement bias: Self-reported data subject to social desirability bias and retrospective recall bias; Generalizability: Single institution sample (King Khalid University) limits external validity to other educational contexts and cultures; Cross-sectional design prevents causal inference and temporal ordering assessment; No direct measurement of actual quality or originality of AI-assisted outputs; relies on perceived use; Self-report bias - responses may reflect perceptions rather than actual behavior; Selection bias - voluntary participation may exclude reluctant or unmotivated students; Social desirability bias - students may report higher ethical awareness than actual practice; Single institution sampling - results from King Khalid University may not generalize; Single culture sampling - Saudi Arabian context may limit cross-cultural applicability; Lack of objective validation of actual AI-assisted outputs

Limitations

"This study relies on self-reported data, which may be influenced by students' perceptions, confidence levels, or familiarity with AI tools
The sample is limited to postgraduate students within a specific educational context, which may restrict the generalizability of the findings to other institutions or cultural settings
Additionally, while the multidimensional scale captures a broad range of GAI-related practices, it does not directly assess the quality, accuracy, or originality of AI-supported academic outputs
Finally, the cross-sectional design limits the ability to draw causal conclusions about how GAI use influences the development of cognitive and research talent over time."

Open questions raised

"Most existing studies focus on undergraduate populations, general educational contexts, or isolated skills such as creativity or critical thinking. Few studies have examined the comprehensive role of GAI in supporting the intertwined domains of cognitive and research talent among postgraduate learners, who face unique academic demands and expectations." Future research should include "Longitudinal studies would provide deeper insight into how sustained exposure to GAI shapes students' research competencies, academic maturity, and ethical awareness. Experimental or mixed-methods designs could help evaluate the effectiveness of specific training interventions or pedagogical strategies aimed at improving AI literacy. Expanding the sample to include diverse institutions, disciplines, and cultural contexts would enhance the generalizability of the findings."
Much existing research focuses on general educational contexts or undergraduate populations, resulting in incomplete understanding of postgraduate students' use of GAI
Lack of comprehensive research examining the intertwined domains of cognitive and research talent among postgraduate learners
Need for longitudinal studies to understand how sustained exposure to GAI shapes students' research competencies over time
Gap between perception of AI usefulness and actual utilization in meaningful research development
Limited understanding of how GAI contributes to genuine talent development versus simply expediting task completion

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