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2025
Volume: 5, No: 5, pp. 1867–1884
ISSN: 2634-3576 (Print) | ISSN 2634-3584 (Online)
posthumanism.co.uk
DOI: https://doi.org/10.63332/joph.v5i5.1572
Artificial Intelligence, Job Displacement, and Gender-Specific
Training Pathways: A Multi-Group Analysis
Boumedyen Shannaq
1
, Ahmed AlAbri
2
Abstract
This study investigates gender-based differences in how AI-Based Performance Analytics (AIPAN) and Women’s Engagement in
Technology (WET) influence Skill Gap Identification using AI (SGIAI) and its subsequent effect on Training Program Alignment
(TPA). The researchers studied female and male participants independently through SEM multi-group analysis of their data. AIPAN
served as a strong predictor of SGIAI for women alongside WET, which contributed β = 0.712 and β = 0.261, respectively (p <
0.001). Additionally, SGIAI demonstrated a strong β relationship of β = 0.810 to TPA (p < 0.001). The analysis indicates that AIPAN
(β = 0.577, p < 0.001), together with WET (β = 0.211, p < 0.001), transmitted significant indirect effects to TPA through SGI AI.
SGIAI received considerable direct impact from AIPAN (β = 0.943, p < 0.001) when studying males, yet WET demonstrated no
significant relationship (β = -0.036, p = 0.361). AIPAN demonstrated a major indirect relationship with TPA through its β = 0.790,
considerable effect (p < 0.001). However, WET did not produce a meaningful impact on TPA measurement. The combination of
WET and AIPAN variables generated no meaningful interaction effects within both the male and female populations. Numerous
factors point to predictive analytics as a vital element in aligning AI skills, but reveal that women demonstrate increased WET
influence, which requires gender-responsive digital transformations.
Keywords:
Gender-Based Differences , Job Training , AI-Based Performance Analytics (AIPAN) , Women’s Engagement in
Technology (WET) , Skill Gap Identification using AI (SGIAI) , Training Program Alignment (TPA).
Introduction
The fast-moving workforce relies on Artificial Intelligence (AI) to reinvent worker training
methods and skill development practices(Tenakwah & Watson, 2025) (Sainger & Irfan, 2024)
(Alazzawi et al., 2025). Artificial Intelligence has become increasingly important for skill gap
detection in individual employees, which leads to specific training
recommendations(Karthikeyan & Singh, 2024) (Subrahmanyam, 2025). AI-driven solutions
receive limited adoption from female workers, along with underutilized benefits during
implementation, particularly within male-dominated occupational sectors(Wilkens et al., 2025).
The accuracy of AI systems detecting training needs and their effectiveness depend strongly on
female workers’ technology involvement and personal belief in their capabilities as well as
administrative support structures(AlDhaen, 2025) (Almusfar, 2025). The research investigates
AI-assisted training systems by studying AI analytical performance when detecting female skill
deficiencies as well as training recommendation alignment with identified deficiencies. The
research uses Multi-Group Analysis (MGA) to study the way gender impacts these associations,
1
Management of Information Systems Department, University of Buraimi, Sultanate of Oman, https://orcid.org/0000-0001-5867-
3986, Email: boumedyen@uob.edu.om (Corresponding Author).
2
Finance and Administrative Affairs & Supporting Services, University of Buraimi, Sultanate of Oman https://orcid.org/0009-
0009-9743-211X.
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together with institutional support systems.
Research Problem
Research about AI-driven workforce development has failed to deliver systematic evidence
regarding how gender influences the effectiveness of AI-based skill gap analysis and training
recommendation processes (Kyriakidou et al., 2025) (Salman Shifa et al., 2025). Insufficient
gender-based analysis of AI predictive power along with decision systems presents a threat to
perpetuate inefficient training practices while maintaining unaddressed bias reduction for female
technology industry workers(Ghanem et al., 2025) (Thirunagalingam et al., 2024) (B. Shannaq,
Adebiaye, et al., 2024) (Dhar et al., 2025).
Research Gap
AI-based performance analytics research has gained traction but not enough investigations exist
about the targeted training approaches AI delivers to women specifically(Al-Rantisi et al., 2025)
(Sanni, 2025) (Sergeeva et al., 2025). This research field currently lacks investigations about
both employee self-assessment and digital readiness effects on AI accuracy and usefulness in
these environments with gender as a vital moderating variable. A thorough understanding of
how organizational support programs affect female participation in AI-based training programs
remains unidentified in current research(B. Shannaq, 2024).
Research Questions
• Dose the utilization of AI-based performance analytics influences how organizations
detect training requirements of female employees (SGIAI).
• Dose gender causes any modification in the connection between AI-based performance
analytics and skill gap identification methods
Research Objectives
• To investigates how AI performance analytics identify skill gaps during an assessment
of women’s training requirements.
• To research the connection between training recommendation quality and its impact on
work performance improvement by focusing on gender-based analysis.
• To investigates how gender influences the mechanism through which AI detects
employee skills followed by training recommendations.
Hypotheses:
Female Group
Male Group
AIPAN positively influences SGIAI
SGIAI positively influences TPA
WET positively influences SGIAI
WET moderate positively the relationship
between AIPAN and SGIAI
Mediation
SGIAI mediates the relationship between
AIPAN and TPA
AIPAN -> SGIAI -> TPA
AIPAN positively influences SGIAI
SGIAI positively influences TPA
WET positively influences SGIAI
WET moderate positively the relationship
between AIPAN and SGIAI
Mediation
SGIAI mediate the relationship between
AIPAN and TPA
AIPAN -> SGIAI -> TPA
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SGIAI mediates the relationship between
AIPAN and TPA positively when WET
moderates the relationship between AIPAN
and SGIAI
WET x AIPAN -> SGIAI -> TPA
SGIAI mediate the relationship between
AIPAN and TPA positively when WET
moderates the relationship between AIPAN
and SGIAI
WET x AIPAN -> SGIAI -> TPA
Figure 1. The proposed Conceptual Framework
Literature Review
AI, as it is well considered nowadays as an important part of the modern concepts of workforce,
has been successfully implemented through the use of systems of AI-based performance
analytics(Dua, 2024) (Noel & Sharma, 2024). Performance analysis uses Big data quantitative
tools, AI and behavior monitoring to analyze talents’ potential, their deficiencies, and
development over time(Noel & Sharma, 2024). Scholars have pointed out that the application of
Artificial Intelligence analysis enhances decision-making by showing where to intervene in
terms of skill deficits (Machucho & Ortiz, 2025). This, we conceptualized and termed as Skill
Gap Identification using AI (SGIAI) where AIPAN can identify discrepancies between the
expected and actual performance in a given task (Molla et al., 2024) (Brauner et al., 2025) (B.
Shannaq et al., 2025). Previous works have therefore noted that where these skill gaps are found,
organizations will be well-equipped to design appropriate TPA that will suit the area of weakness
for whichever individual/activity group in consideration (Weerasombat & Pumipatyothin, 2025)
(Jaskari, 2024). This also leads not only to lack of match between content of training and job
requirements for enhancing effectiveness of learning as well as the level of satisfaction and
performance among the employees(Sharma et al., 2025) (B. Shannaq, 2025) (.Boumedyen
Shannaq & Alabri, 2025) (Shakir et al., 2024). For instance, learning solutions powered by
artificial intelligence are now employed in organizations for recommending courses that cater to
the learner’s needs with research showing that there is an enhanced training performance (Yadav
& Shrawankar, 2024). Based on the above findings, the issue of applying transformational
learning at organizational level through the medium of SGIAI has led to the development of the
most route towards TPA. Though, this kind of relationship would not be the same for both male
and female students. Some researchers suggest that it may not reliably spot skill gaps when it
come to hire new employees because of data bias or a lack of contextual awareness (Muralidhar
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et al., 2025) (Chinenye Gbemisola Okatta et al., 2024) (B. Shannaq, Saleem, et al., 2024). This
is most important in the case of Women’s Engagement in Technology (WET) whereby it acts as
a moderating variable. It was noted that lower digital confidence, as well as inequality in the use
of technical resources, and limited representations of women in the tech industry and
contributing to ICT product development, can influence how AI processes women’s
performance data(Fraile-Rojas et al., 2025) (April & Daya, 2025). Concerning the practical use
of the AI-based assessment, women who are more involved in the technology industry can
experience a more accurate evaluation of their skills and their real performance on the job, which
can help in determining the actual gap in the office environment (Majrashi, 2025). While
demonstrating increased recognition of the gender aspects in cases of AI utilization, the fact
stays that there is a lack of empirical research that measures the role that women’s participation
in technology can help to enhance the outcomes of the AI-based skill gap identification(Sanni,
2025) (Toledo-Navarro et al., 2025). Many prior models do not isolate gender-related issues or
Gender-Sensitive Considerations in performing the process. This negates the social technical
interactions that affect data analysis and decision making in artificial intelligence systems.
Compared to Existing Literature, the current study can be novel in contributing to the AIPAN
➝ SGIAI relationship in consideration of gender, particularly women’s technology use as a
moderator. By doing so it places the AI system within a context of social and other related factors
that may influence the results. Furthermore, this study includes not only the links between skill
gaps identification and training alignment but also correlates them with other factors of the
organization’s preparedness, which are significant in AI-related workforce learning.
Methodology
A quantitative research design was used to study how Artificial Intelligence performance
analytics systems detect employee skill gaps while examining separate male and female worker
experiences. The methodology implements Structural Equation Modeling (SEM) which runs on
SmartPLS software platform to evaluate complex relationships among observed variables and
latent constructs. Multi-Group Analysis (MGA) serves as an integral component of this study to
assess how technical involvement of women affects these relationships between fundamental
constructs throughout masculine and feminine workgroups.
Staff members from different sectors in Oman received a structured survey that served as the
primary data collection method. The measurement items for constructs comprised AI-Based
Performance Analytics (AIPAN) and Skill Gap Identification using AI (SGIAI) and Training
Program Alignment (TPA) and Women’s Engagement in Technology (WET) which used
validated Likert scale assessments. The method enables comprehensive model testing of
robustness while assessing construct reliability through validity checks and enables comparisons
between male and female respondents to determine AI performance in skill gap recognition.
Methodological Steps and Explanations
The first step focuses on creating an instrument alongside the research design.
Researchers selected a descriptive and cross-sectional design for the study because this method
captured single-time perceptions. Researchers used validated scales and adapted them according
to AI technology and skill gap evaluation and general technology gender participation
requirements when developing the questionnaire. The 5-point Likert scale with values ranging
from 1 to 5 served as the measurement approach for multiple items representing each construct.
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Key Constructs:
AIPAN (AI-Based Performance Analytics)
The AI-based method for skill gap assessment is named SGIAI (Skill Gap Identification Using
AI).
TPA (Training Program Alignment)
WET (Women’s Engagement in Technology)
Step 2
Sampling and Data Collection
The approach used purposive sampling techniques to achieve appropriate representation of
employees from man and woman demographics who worked in AI technology sectors. The
research included 500 original responses which resulted in 432 usable responses during the
cleaning process. The research population established two analytical groups consisting of male
respondents (265) and female respondents (167) prior to Multi-Group Analysis (MGA).
Step 3
Data Screening and Preparation
The data screening process checked for missing data as well as detected outliers and verified
normal distribution. The SmartPLS estimation method proves suitable for complex models along
with smaller sample sizes because it does not need multivariate normality.
Step 4
Measurement Model Assessment
Research investigations validated the measurement constructs through these tests:
All measured constructs displayed Cronbach’s Alpha values in combination with Composite
Reliability values that surpassed 0.7.
The Average Variance Extracted values exceeded 0.5 which demonstrates satisfactory enough
convergent validity.
The analysis using Fornell-Larcker Criterion validated discriminant validity between all
measured constructs.
Step 5
Structural Model Evaluation
The analysis through bootstrapping (5,000 subsamples) in SmartPLS determined significant
relationships between AIPAN ➝ SGIAI ➝ TPA and the moderation impact of WET. The
validity of this study relied on analysis of both t-values alongside p-values.
Step 6
Multi-Group Analysis (MGA)
The research employed MGA as part of its analysis to evaluate structural paths between male
and female respondents. The WET strength as a moderator between AIPAN and SGIAI
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manifested differently for males and females according to this analysis.
Step 7
Interpretation and Reporting of Results
Path coefficient interpretation and moderation effect analysis along with model fit comparison
between gender groups were performed in the last stage. The obtained results became the
foundation for analyzing research inquiries as well as developing recommendations which focus
on enhancing AI equity during workforce development.
Data Analysis and Results
Measurement Model
Outer Loadings presented in Figure 2:
The reliability of measurement items is verified through this table that provides evidence. The
outer loadings exceed 0.8 in all cases which proves that the observed variables show reliable
connections with their constructs while measuring AIPAN, SGIAI, TPA and WET properly.
The analyzed VIF values remain below 5 which demonstrates that severe multicollinearity does
not exist in the study. The model indicators demonstrate independent contribution to the
structural analysis as they do not experience inflation from related variables which indicates
robustness in the model structure.
Figure 2. Factor Loading
Reliability and Validity Table 1:
Cronbach's
alpha
Composite
reliability
(rho_a)
Composite
reliability
(rho_c)
Average variance
extracted (AVE)
AIPAN
0.916
0.917
0.937
0.749
SGIAI
0.935
0.936
0.951
0.794
TPA
0.933
0.933
0.949
0.789
WET
0.919
0.921
0.939
0.756
Table 1. Reliability and Validity
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The measurement model exhibits excellent convergence validity through Cronbach's Alpha
values above 0.9 and composite reliability values higher than 0.93 and AVE above 0.75. The
observed measurement model demonstrates strong reliability and validity because of the
reported results in this analysis.
Discriminant Validity (Fornell-Lacker Table):
Square root values of AVE appear higher than all pairs of construct correlations which
demonstrates discriminant validity. All constructs exist independently from one another despite
the close relationship between AIPAN and SGIAI.
AIPAN
SGIAI
TPA
WET
AIPAN
0.865
SGIAI
0.895
0.891
TPA
0.821
0.824
0.888
WET
0.741
0.728
0.761
0.869
Table 2. Fornell-Lacker
Structural Model
Figure 2 present the Bootstrap for Female group while Figure 3 presents the Bootstrap for the
Male Group .
Figure 3. Bootstrap Female Group
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Figure 4. Bootstrap Male Group
Table 3: Path Coefficients – Female Group
The results demonstrate that AIPAN together with WET leads to higher SGIAI levels which
subsequently enhances TPA. An interaction between WET and AIPAN fails to show
significance as a moderator of SGIAI among ladies.
The training alignment of female program participants depends on AIPAN and SGIAI as well
as WET. The statistical analysis shows that WET and AIPAN fail to create a significant effect
on SGIAI although both variables independently affect this outcome.
Original
sample (O)
Sample
mean (M)
Standard
deviation
(STDEV)
T statistics
(|O/STDEV|)
P
values
AIPAN ->
SGIAI
0.712
0.712
0.074
9.641
0.000
SGIAI ->
TPA
0.810
0.811
0.039
20.660
0.000
WET ->
SGIAI
0.261
0.263
0.078
3.361
0.000
WET x
AIPAN ->
SGIAI
-0.048
-0.049
0.034
1.380
0.084
Table 3. Path Coefficients – Female Group
Table 4: Path Coefficients – Male Group
All tested paths including WET → SGIAI exist except for the WET → SGIAI connection and
the interaction effect measure. The impact of AIPAN flows directly into SGIAI that produces
substantial effects on TPA. The male group experiences no direct or moderating effects from
WET on SGIAI levels.
Among males stronger impacts arise from both AIPAN and SGIAI but WET and its interaction
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with AIPAN produce no meaningful effects. The findings show that WET has less importance
than other constructs in defining male involvement in skills identification through AI and
training alignment process.
Original
sample (O)
Sample
mean (M)
Standard
deviation
(STDEV)
T statistics
(|O/STDEV|)
P
values
AIPAN ->
SGIAI
0.943
0.942
0.079
11.976
0.000
SGIAI ->
TPA
0.837
0.840
0.036
22.991
0.000
WET ->
SGIAI
-0.036
-0.031
0.100
0.356
0.361
WET x
AIPAN ->
SGIAI
0.036
0.033
0.050
0.721
0.235
Table 4 Path Coefficients – Male Group
Table 5: Specific Indirect Effects – Female
The data in this table demonstrates how SGIAI acts as a meaningful mediator to link AIPAN
and WET with TPA for female beneficiaries. The statistical analysis demonstrates that the
combination of WET and AIPAN does not affect the relationship between SGIAI and TPA.
For female employees SGIAI serves as an important mediator which enables WET as well as
AIPAN to influence training alignment goals. The lack of significance in WET-AIPAN
interactive moderated mediation demonstrates that SGIAI functions as a primary mediator of the
training alignment process particularly for female respondents.
Original
sample (O)
Sample
mean (M)
Standard
deviation
(STDEV)
T statistics
(|O/STDEV|)
P
values
AIPAN ->
SGIAI -> TPA
0.577
0.578
0.070
8.188
0.000
WET ->
SGIAI -> TPA
0.211
0.213
0.063
3.357
0.000
WET x
AIPAN ->
SGIAI -> TPA
-0.039
-0.040
0.028
1.384
0.083
Table 5 Specific Indirect Effects – Female
Table 6: Specific Indirect Effects – Male Group
The research finding suggests that only AIPAN demonstrates an important relationship by
linking to TPA through SGIAI. Data reveals that SGIAI mediates only the AIPAN impact for
men because the direct effects from WET and its AIPAN interaction remain insignificant.
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AIPAN affects the relationship between training alignment and SGIAI for male personnel. WET
along with its AI analytics interaction does not create indirect influences therefore indicating
that AI analytics serve a more important role than employee engagement for training purposes
particularly among female candidates.
Original
sample (O)
Sample
mean (M)
Standard
deviation
(STDEV)
T statistics
(|O/STDEV|)
P
values
AIPAN ->
SGIAI -> TPA
0.790
0.791
0.071
11.195
0.000
WET ->
SGIAI -> TPA
-0.030
-0.025
0.084
0.354
0.362
WET x
AIPAN ->
SGIAI -> TPA
0.030
0.027
0.042
0.718
0.236
Table 6. Specific Indirect Effects – Male Group
Comparison Between Female and Male Groups:
• AIPAN → SGIAI → TPA: Significant for both genders, but stronger for males (0.790
vs. 0.577).
• The results indicate WET has an effect on females only under this experimental setup.
• WET direct effect: Positive and significant in females, but non-significant and negative
in males.
• The moderated mediation results for WET × AIPAN were not significant for both
genders but females showed slightly higher effects.
• Across both genders the predictive power of AIPAN remains stable. WET aligns skills
and training more efficiently among females than males so specific strategies should target
women to increase their tech-based upskilling.
Figure 5 presents the interaction plot, which shows the moderating effect of Women’s Engagement in
Technology (WET) on the relationship between AI-Based Performance Analytics (AIPAN) and Skill
Gap Identification using AI (SGIAI).
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Chart Title: WET × AIPAN
• X-Axis: AIPAN (AI-Based Performance Analytics)
• Y-Axis: SGIAI (Skill Gap Identification using AI)
• Lines Representing Moderation Levels:
o Red Line: Low WET (−1 SD)
o Blue Line: Mean WET
o Green Line: High WET (+1 SD)
What the Chart Shows:
1. Positive Slope:
o The upward direction of all three lines (Red, Blue and Green) shows a direct correlation where
AIPAN values lead to similar rises in SGIAI values. The use of AI to examine performance
produces better skill gap identifications—an affirmative relationship between AIPAN and
SGIAI.
2. Moderation Effect of WET:
The positional relationship between lines indicates how increased WET levels affect the
relationship between AIPAN and SGIAI.At higher levels of WET (+1 SD, Green Line), the
positive effect of AIPAN on SGIAI is stronger.
At lower levels of WET (−1 SD, Red Line), the effect is weaker.
The mean level (Blue Line) lies in between.
Interpretation of the Results:
The effectiveness of AI-based performance analytics systems to detect skill gaps increases when
women show elevated involvement in technology.
AI-based systems detect fewer skill gaps for women who show weak engagement because these
systems either lack trained data for women or receive limited use from female users.
Theoretical Implication
Current literature supports concerns about AI methods that discriminate against specific groups
while also presenting inclusivity problems in AI systems. AI systems inadvertently introduce
bias against and demonstrate inferior performance towards people who comprise marginalized
groups. IT engagement by women produces better customized and exact AI analytics results for
personnel training environments.
Managerial & Policy Implications
WET enhancement demands organizations to design AI systems with inclusive gender design
and offer digital training for women to improve digital engagement.
Fully engaged women participants within the Human-AI collaboration process enable AI
systems to deliver recommendations which represent actual student learning requirements.
The necessity of creating unbiased AI systems becomes vital after this result because women
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must have proper representation in training data.
Figure 5. Slope analysis for Male Group
Figure 6 presents the second chart, which illustrates the moderation effect of Women’s
Engagement in Technology (WET) on the relationship between AI-Based Performance
Analytics (AIPAN) and Skill Gap Identification using AI (SGIAI) specifically for the male
group, and then compare it to the female group from the first chart.
Chart Interpretation: Male Group (Second Chart)
Axes and Lines:
X-Axis: AIPAN (AI-Based Performance Analytics)
Y-Axis: SGIAI (Skill Gap Identification using AI)
Three Lines Represent WET Levels:
Red Line: Low WET (−1 SD)
Blue Line: Mean WET
Green Line: High WET (+1 SD)
What We See in the Male Group Chart:
Strong Positive Linear Relationship:
AIPAN shows similar effects on SGIAI within the male participant group as it does in the female
participants because increasing AI analytics performance produces corresponding rises in skill
gap identification.
Negligible Moderation Effect by WET:
The red blue and green lines in this image stay very close to each other which demonstrates that
WET levels produce no meaningful differences in slope for the male participants.
All three lines (Low WET) red, High WET blue, marginally connect at approximately same
vertical level.
AI effectiveness in spotting skill gaps remains unaffected by WET throughout the entire range
of WET for the male demographic.
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Feature
Female Group
Male Group
Slope of Lines
All positive, but spread apart
All positive, closely aligned
Moderation by
WET
Strong moderation effect
Weak or no moderation effect
Impact of High
WET
Boosts the AIPAN ➝ SGIAI
relationship
Has minimal influence
Interpretation
WET amplifies AI’s skill detection
power
WET is less critical for
effectiveness
Table 7. Comparison with the Female Group (First Chart)
Implications & Recommendations
Interpretation of Gender Differences
Women’s Engagement in Technology (WET) proves more significant for female groups than
for males. The effectiveness of AI in detecting skill gaps deteriorates when employee
engagement falls to low levels.
AI systems demonstrate the same level of efficacy regardless of WET measurements for male
users. A system leveraging AI analytics provides benefits to male users at any level of
technological engagement.
Recommendations:
Targeted Interventions for Women:
The organization should create specific digital literacy development along with engagement
programs for female audience members.
Gender bias in AI training data will decrease if organizations make AI datasets more inclusive.
The organization should organize training sessions together with encouraging offers to help
female experts use digital technologies.
AI System Design:
The training data used to develop AI systems should include extensive user patterns from female
contributors and diverse sets of user behavior.
Integrate gender-aware fairness metrics in AI performance evaluation.
Policy-Level Strategy:
The national digital transformation plans such as Oman Vision 2040 should develop dedicated
strategies to promote digital empowerment among different genders.
Encourage female participation in AI governance and decision-making processes.
Table 8: R-square (Female Group)
This table shows that AIPAN and WET explain 79.1% of the variance in SGIAI and 65.7% in
TPA for females, indicating strong model fit and predictive relevance.
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R-square
R-square adjusted
SGIAI
0.791
0.784
TPA
0.657
0.653
Table 8. R-Square Female Group
Table 9: f-square (Female Group)
AIPAN has a large effect on SGIAI (f² = 1.335), and SGIAI strongly influences TPA (f² = 1.911).
WET shows a small effect (f² = 0.184), while the interaction term has a negligible impact (f² =
0.015).
f-square
AIPAN -> SGIAI
1.335
SGIAI -> TPA
1.911
WET -> SGIAI
0.184
WET x AIPAN -> SGIAI
0.015
Table 9. F-Square Female Group
Table 10: R-square (Male Group)
In the male group, 83.4% of the variance in SGIAI and 70.1% in TPA are explained by the
predictors, slightly outperforming the female group in terms of model fit.
R-square
R-square adjusted
SGIAI
0.834
0.825
TPA
0.701
0.695
Table 10. R-Square Male Group
Table 11: f-square (Male Group)
AIPAN again has a large effect on SGIAI (f² = 1.674), and SGIAI's effect on TPA is also very
strong (f² = 2.339). However, WET’s influence is negligible (f² = 0.002), as is the interaction
term (f² = 0.006).
f-square
AIPAN -> SGIAI
1.674
SGIAI -> TPA
2.339
WET -> SGIAI
0.002
WET x AIPAN -> SGIAI
0.006
Table 11. F-Square Male Group
Comparison
Both groups show high R-square values, with males having slightly higher predictive power.
However, females show more sensitivity to WET, whereas males rely more heavily on AIPAN.
The interaction effect is minimal in both cases.
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Conclusion
This research examined the usage of AI-based performance analytics systems for training
requirements assessment for women workers along with assessing gender effects on
performance metrics alignment. These findings from the investigations receive supporting data
through path coefficients as well as R-square measurements along with f-square and indirect
effects analysis.
Women who are digitally ready with self-assessment abilities display a moderate impact on skill
gap identification through AI (SGIAI) (β = 0.261, p < 0.001). The combination of Women
Engagement in Technology (WET) and Artificial Intelligence Predicted Accuracy (AIPAN)
yields an insignificant effect (p = 0.084) on their interaction.
Summary evaluation data (AIPAN) exerts a substantial influence (β = 0.943) on the
effectiveness of AI when analyzing male data while self-reported measurement (WET) exhibits
no meaningful impact (β = -0.036 with p > 0.05). This shows that male systems partly depend
on computer-based data more than user-reported data or readiness domains.
The quality indicators from the applied models (R-square and f-square) demonstrate higher
explanatory values among individuals who are male. The evaluation data from AI metrics meets
comparable levels of influence with user input data for female participants.
Organizations should create programs to improve women's digital readiness since self-
assessment combined with digital literacy in AI systems proves beneficial to this specific group.
Organizations need to provide institutional backing and training programs which support
women's digital involvement.
AI systems require tailoring their operation based on specific gender-based behaviors and
readiness profiles to enhance accuracy and matching between AI systems and training needs.
Modern AI systems should combine staff-assessed input with automated inputs to create better
skill assessment capabilities. —especially effective for female-targeted workforce development.
These findings support more inclusive, accurate, and gender-responsive AI applications in
organizational training strategies.
Acknowledgement
The authors would like to express their sincere gratitude to the University of Buraimi (UoB)
for its generous support and funding of this research. This work was conducted as part of the
internal project titled "Analyzing the Impact of AI on Academic Job Requirements and
Forecasting Future Job Replacements," which has been officially accepted for the UoB
Internal Research Grant for the Academic Year 2024–2025. The university's commitment to
advancing academic research and innovation played a pivotal role in enabling the successful
completion of this study.
Contribution
Boumedyen Shannaq: Conceptualization, Methodology, Investigation, Writing- original draft;
Ahmed AlAbri: Project Administration, Validation, Visualization;
Funding
Internal
research
grant
of
University of Buraimi.
1882
Artificial Intelligence, Job Displacement, and Gender-Specific
Journal of Posthumanism
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