Balancing Efficiency and Dependency: The Impact of AI Tools on

Employee Skills and Emotional Engagement

Ruotong Zhao

Nanjing University of Information Science and Technology, Nanjing, China

Keywords: Artificial Intelligence, Complementary Skills, Human Resource Management.

Abstract: This study focuses on the dual impact of the use of AI tools in the workplace on employees' skills and emotions.

It explores the degradation of skills and emotional connection caused by over-reliance on AI tools while

enhancing employees' work efficiency. Through the literature research method, we constructed a model of

Skill Complementarity - Emotional Connection to explore the dual impact of the use of AI tools on employees

and the dynamic feedback mechanism. It is found that although AI tools can improve the processing efficiency

of complex tasks, excessive use of AI tools can weaken employees' autonomy and lead to negative impacts.

Therefore, companies should provide differentiated training according to different employee groups, establish

a dynamic assessment mechanism to adjust the degree of intervention of AI tools, and improve employees'

acceptance of AI tools through training to avoid the potential risks associated with their over-reliance on AI

tools.

1 INTRODUCTION

Artificial Intelligence has become a necessary tool to

help human beings improve productivity in modern

society, and with the emergence of various types of

AI tools, some of the employees have begun to worry

about whether AI will lead to unemployment of

human beings. As a matter of fact, human resources

are still the most important resource in enterprises in

today's society, but in the context of increased

competition in contemporary society, human beings

must follow the trend of the times and dare to adopt

more flexible and innovative productivity tools, and

combine and apply AI tools with human resources, in

order to promote the iteration of technology and

advance social progress and development.

Although there are already colleges and

universities combining AI with other majors to derive

composite majors, the existing AI applications are

still in the primary stage, and some studies have

concluded that AI is accompanied by a series of

negative psychological problems while bringing

about an increase in the ability of employees (Shi &

Liu, 2025). Therefore, management needs to pay

attention to the impact of timely AI tools on

employees in terms of emotional connection, and HR

should help employees use AI tools reasonably. At

the same time as the use of AI for employees to bring

more convenient and efficient help to reduce

cognitive load to improve the quality of life and work

efficiency at the same time, employees on the AI tools

to produce a sense of dependence on the employee's

future development will also cause a series of impacts

(Zhang & Shen, 2024).

The public generally believes that AI can enhance

human skills, but dependence on AI tools may lead to

skill degradation. To maximize the positive impact of

AI tools in an organization, they should be used

strategically to complement human abilities, with

clear guidelines and training provided to employees

on how to leverage AI effectively while maintaining

and developing their own core competencies. In the

era of AI, exploring these questions cannot only help

corporate managers or human resources practitioners

to improve their human resources management,

training and development capabilities, but also allow

employees to re-examine this special relationship,

adjust unreasonable usage, and avoid emotional ties

that may lead to unhealthy development. This study

is based on an extensive review of domestic and

international literature on AI tools.

130

Zhao, R.

Balancing Efﬁciency and Dependency: The Impact of AI Tools on Employee Skills and Emotional Engagement.

DOI: 10.5220/0013987200004916

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Public Relations and Media Communication (PRMC 2025), pages 130-137

ISBN: 978-989-758-778-8

2 LITERATURE REVIEW

2.1 Current Status

With the rapid development of artificial intelligence

technology, AI tools are increasingly applied in the

workplace. Their use in organizations evolves

through three stages: technology embedding, process

reconstruction, and value creation. AI is transforming

employees' work methods and workflows, from

human resource management to daily office tasks.

For example, the application of AI technology in the

recruitment process has become very common,

through natural language processing and machine

learning, AI can quickly screen resumes, identify

potential candidates, and conduct preliminary

interviews, thus significantly improving recruitment

efficiency (Hmoud & Laszlo, 2019). At the strategic

level it is even able to construct employee

competency growth curves through deep learning-

driven predictive analytics systems, increasing the

accuracy of talent retention decisions by 28.6%.

Meanwhile for other industry sectors it is worth

noting that the application of AI tools shows

significant industry heterogeneity: the financial

industry focuses on risk prediction models, while the

manufacturing industry focuses on IoT-driven device

co-optimization. Meanwhile, AI tools are not limited

to recruitment but also play an important role in

training and employee development. With

personalized training modules and real-time

feedback, AI can help employees improve their skills

and adapt to changing work environments. AI is also

increasingly used in performance management today,

where through data analysis and predictive

modelling, AI can provide more accurate

performance appraisals and help employees improve

their performance.

However, existing research still has some

limitations. Based on research related to various

aspects of business, some studies can tend to be

technologically deterministic, with Tiwari noting that

78% of the literature overestimates the technological

efficacy of AI tools and ignores the rigid constraints

of organizational practices. For example, small and

medium-sized manufacturing enterprises have a

23.7% misjudgment rate of AI quality control

systems due to the lack of standardized processes

(Tiwari et al., 2021). At the same time, existing

evaluations mostly use unidimensional efficiency

indicators (e.g., man-hour compression rate),

ignoring hidden values such as knowledge spillover

effects.

Meanwhile, the update of AI tools also has some

impact on the research results, Zhang, J. found that

the performance leap of ChatGPT from version 3.5 to

4.0 led to the extension of the average adaptation

cycle of employees to 4.2 months, which incurred a

significant skill replacement cost. Nowadays, when

AI updates are very fast and iterative, the impact of

model updates on various aspects cannot be ignored.

2.2 AI Tools

It has been shown that AI tools have significant

effects in enhancing employee skills. For example, by

using AI-driven training platforms, employees can

learn new skills more efficiently, reduce learning

time, and improve learning outcomes (Singh &

Shaurya, 2021). AI tools can also help employees

better master complex tasks by simulating real-life

work scenarios and providing practice opportunities

(Qamar et al., 2021).

However, the use of AI tools also brings some

negative psychological problems. For example,

employees may feel less autonomous and have

concerns about skill degradation due to over-reliance

on AI tools. In addition, the use of AI tools may

trigger anxiety and stress in employees, especially if

the feedback from AI tools is not clear or fair enough.

Empirical studies show that AI-assisted software

engineers have significant improvements in code

quality and development efficiency, but there is an

obvious ability compensation gradient effect: the

benefit rate of junior employees is significantly

higher than that of senior employees, which reflects

the barriers to the absorption of new technologies in

the existing knowledge system.

2.3 Impact of AI Tools

The use of AI tools in the workplace not only affects

employees' skill improvement but also has a profound

impact on their emotional connection. Research has

shown that employees' emotional connection to AI

tools may affect their job satisfaction and work

engagement. For example, when employees feel that

AI tools provide support and assistance, they are more

likely to have a positive affective connection to the

AI tool, which leads to increased job satisfaction

(Tang et al., 2022). Emotional connection research

presents a dialectical relationship of technological

empowerment-psychological depletion. In the

positive dimension, AI tools enhance the stickiness of

using them through emotional design (e.g.,

Balancing Efﬁciency and Dependency: The Impact of AI Tools on Employee Skills and Emotional Engagement

131

anthropomorphic interaction interface), and increase

the job satisfaction of telecommuting employees.

Particularly in high-pressure work scenarios,

intelligent stress management systems have led to a

significant increase in team cohesion index through

real-time emotion recognition and intervention

(Qamar et al., 2021).

However, the implicit expansion of algorithmic

power is triggering a systemic crisis in the

organizational emotional ecosystem: nearly half of

employee’s report that AI performance monitoring

has led them to experience the Panopticon Effect.

Algorithmic bias may even trigger a crisis of

perceived fairness - one multinational company's AI

recruitment system reduced female hiring by 12.4%

due to gender discrimination, leading to a 19.3% drop

in employee commitment (Vrontis et al., 2022). In

addition, employees' dependence on and excessive

trust in AI tools can also have an impact on their

subsequent career development.

All in all, over-reliance on AI tools may lead to

excessive emotional connection of employees,

affecting their autonomy and creativity. When AI

tools play an overly important role in decision-

making, employees may rely less on their own

judgement, leading to reduced decision-making

ability. In addition, incorrect or unfair feedback from

AI tools may trigger negative emotions in employees,

reducing job satisfaction and work engagement.

2.4 Shortcomings of Current Research

Although existing studies have made some progress

in the area of AI tools on employees' skill

enhancement and emotional connection, there are still

some shortcomings. Firstly, most studies have

focused on the short-term impact of AI tools, while

fewer studies have examined the long-term impact.

Whether the long-term use of AI tools will lead to a

sustained improvement in employee skills or trigger

a more serious degradation of skills still requires

further research.

Second, existing studies have less research on the

impact of AI tools in different cultural and

organizational contexts. Different cultural and

organizational contexts may have a significant impact

on the acceptance and usage of AI tools, thus

affecting their skill enhancement and emotional

connection effects on employees. Managers need to

pay attention to the mental health and skill

development of their employees to ensure that the use

of AI tools truly promotes employee growth and

organizational development rather than leading

employees astray.

3 ANALYSIS

3.1 The Implementations

Compared with the systematic literature review that

relies on a predefined framework of questions, this

method chooses a more open research paradigm that

avoids the limitations of quantitative tools and meets

the needs of exploratory theoretical research.

3.1.1 Literature Search and Screening

Taking EBSCO, Web of Science, CNKI and other

databases as the core, using keywords such as AI tool

use, employee skills, emotional connection and other

keyword combinations in Chinese and English, we

screened journal articles, monographs and industry

reports published in the fields of management and

psychology during 2018-2024, focusing on the

inclusion of SSCI/CSSCI source literature.

3.1.2 Theoretical Framework Construction

In terms of theoretical framework construction, this

study is based on the MRD structure, which is

methodology, results and discussion, which

integrates two major theoretical axes. The first is the

theory of skill complementarity, which draws on the

three-quadrant model of human-computer ability

complementarity proposed by Man Tang et al. to

deeply analyze the skill compensation mechanism

achieved by AI tools through task offloading,

decision-making enhancement and cognitive

expansion. The second is the emotional connection

theory, which combines the cognitive dependency of

AI proposed by Zhang and Shen to reveal the

dynamic evolution path of emotional response.

Through the theoretical coupling, we construct the

Skill Complementation and Emotional Connection

analysis matrix, forming a multi-dimensional

explanation framework, which provides a solid

theoretical foundation for the in-depth analysis of the

impact of AI tools on employees' skills and emotions.

3.1.3 Critical Analysis

Contradiction analysis is used to deconstruct

opposing views in literature. For example, in terms of

the paradox of efficiency gain and alienation, Qamar

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et al. confirm the efficiency gain of AI in recruitment,

while Shi and Liu reveal that over-reliance on AI

leads to an increase in the incidence of ethical excuses,

in terms of the gradient effect of skill compensation,

Man Tang et al. find that the rate of skill enhancement

for junior employees is significantly higher than that

for senior employees, which is attributed to the

difference between knowledge inertia and technology

absorption, in terms of the two-way regulation of

affective connection's bidirectional moderation, some

scholars noted that employees in collectivist cultures

are more tolerant of AI monitoring, but did not

quantify the moderating effect of the power distance

index (PDI). These comparisons reveal the core

research blind spot: the absence of an interaction

mechanism between skill compensation and affective

alienation.

3.2 Theoretical Research

Existing studies do not have a combined analysis of

skill complementarity and affective connection

aspects generated using AI tools on employees.

Current research mostly explores the single impact of

AI tools on employees' skills or emotions in isolation,

failing to reveal the dynamic interaction mechanism

between the two. Skill complementarity theory

emphasizes that AI improves efficiency through task

division but ignores the inhibitory effect of the

emotional connection between employees and AI

tools on employee skill transformation. For example,

although AI-assisted programming improves code

output, the black-boxing of algorithms may weaken

developers' understanding of technical principles,

leading to the skill compensation illusion.

Meanwhile, the effective connection theory focuses

on anxiety but fails to explain how affective

fluctuations can be counteracted in skill acquisition.

It is worth considering how to utilize the relationship

between affective connection and complementary

skills to train and assist employees. The potential

cause is a lack of interdisciplinary dialogue due to

disciplinary barriers leading to theoretical

fragmentation, with management favoring efficiency

analysis and psychology focusing on emotional

mechanisms.

The limitations of the current research

methodology are mainly due to insufficient empirical

evidence for longitudinal tracking and cross-cultural

comparisons. Current research relies on cross-

sectional data and lacks tracking of the long-term

impact of AI tools. Qamar et al. validated the

immediate effects of AI training but did not assess

whether employees' ability to iterate their skills

would decline after 3 years because of AI

dependency. Cross-cultural comparative studies are

similarly scarce, with employees in collectivist

cultures more likely to embrace AI tools but failing to

quantify the moderating effect of different cultural

dimensions on technology adoption thresholds.

Initial speculation was that longitudinal studies

were costly and time-consuming due to academics'

preference for short-term results. Cross-country data

access was limited by corporate confidentiality

agreements and cultural sensitivities, leading to

homogenized samples (more than 80% of the market

is between Europe, the US and China) (Vrontis et al.,

2022). Methodologically, existing scales (e.g., the

Technology Anxiety Scale) have not been adapted to

the characteristics of different AI tools and their

validity is questionable.

3.3 Practical Disconnect

There is a synergistic imbalance between the help of

AI tools to organizations and the presence of ethical

constraints on employees. There is a general tendency

to emphasize technology over management in

enterprise practice, and many enterprises do not carry

out human-computer collaboration training after

introducing AI tools, resulting in employees' anxiety

due to the skills gap. This shows that when enterprises

invest in technology, they often neglect supporting

management measures, such as training and skills

upgrading programs, thus affecting the ability of

employees to adapt to new technologies.

In terms of ethical governance, the correction of

algorithmic bias also remains at the technological

level, lacking a synergistic framework of technology-

institution-culture. Shi and Liu's study found that the

incidence of employees' moral excuses was 37%

higher in enterprises that did not have an ethical

review mechanism in place (Shi & Liu, 2025). This

highlights the fact that it is not enough for enterprises

to rely only on technological means to correct

algorithmic bias in the process of technology

application and that they also need to build a

comprehensive ethical governance framework at the

institutional and cultural levels to reduce the ethical

risks in the application of technology.

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133

4 RESULTS

By integrating the existing literature, this study found

that the application of AI tools in the workplace

presents a complex bidirectional dynamic

relationship between employees' skills and emotions.

In terms of skill complementarity, AI tools

significantly improve employees' efficiency in

handling complex tasks through task decomposition

and cognitive support. Junior employees can quickly

master standardized operations assisted by the tools,

but long-term over-reliance may lead to the gradual

degradation of core skills. For example, when the

degree of AI intervention exceeds a certain threshold,

the ability of employees to solve problems on their

own may be implicitly weakened, forming a tool

substitution trap - behind the surface of the efficiency

improvement, it is the loss of innovative thinking and

depth of technical understanding.

At the level of emotional connection, the

interaction design of AI tools directly affects the

psychological state of employees. Transparent and

explainable feedback mechanisms (e.g., clear

progress reminders and decision-making basis) can

enhance employees' trust in the tool, thus establishing

positive emotional dependence. On the contrary,

black-boxing or excessive monitoring of algorithms

can easily trigger anxiety and alienation and even

trigger resistance to technological control. It is worth

noting that employees with different skill levels

respond differently to affective stimuli: high-skilled

employees are more concerned about the limitations

of AI tools on professional autonomy, while low-

skilled employees may be caught in a cycle of passive

adaptation due to increased tool dependency.

Analyzed in conjunction with the model diagram

(Skill Complementarity-Emotional Connection

Model), it was found that the two do not act in

isolation but form dynamic feedback through self-

efficacy. When employees accumulate successful

experiences with the help of AI, their self-efficacy

will further stimulate their willingness to learn

actively, forming a positive cycle of skill

enhancement and confidence reinforcement. On the

other hand, if the use of tools weakens employees'

perceptions of their own abilities, it will lead to a

vicious interaction of increased anxiety and skill

stagnation. As shown in Figure 1, this mechanism

manifests itself differently in different organizational

environments: hierarchical organizations are more

receptive to AI monitoring, but employees become

emotionally drained faster, whereas in flat teams, the

flexibility of tool use may mitigate emotional

conflict, but there is a risk of homogenization of

skills.

5 DISCUSSION

After logically analyzing and extrapolating the

literature in related fields, this paper focuses on two

major academic sections, psychology and

management, and examines the use of AI tools in

today's hot topic of exploration, drawing relevant

conclusions about the ability to assist in human

resource management.

5.1 Main Findings

The core finding of this study reveals that the use of

AI tools in the workplace brings positive as well as

negative impacts to employees, which can be reduced

by managers through training: while enhancing

employees' skills, AI tools may trigger a complex

evolution of emotional connections. The main finding

suggests that AI tools significantly increase

employees’ efficiency in handling complex tasks

through a skill complementation mechanism, but this

efficiency gain comes with a potential risk - when AI

tools are used beyond a reasonable threshold,

employees’ reduced autonomy will lead to a

degradation of their skills and trigger negative

affective associations. This finding validates the

tension between perceived usefulness and perceived

risk in the Technology Acceptance Model (TAM).

This suggests that the realization of the value of AI

tools needs to be based on a dynamic equilibrium.

Secondly, due to inter-individual differences (the

acceptance of AI tools by highly ethically sensitive

employees was significantly lower than that of the

general group, which stemmed from their higher

demand for their own task completion and higher

sense of ethical standards 0. This result challenges the

assumption of established research that technology

acceptance is solely attributed to tool effectiveness,

and the author analyses the need for organizations to

establish a differentiated training system, such as

designing pre-training courses such as psychological

counselling for employees with high ethical senses

before conducting formal AI operation courses

(defining employees' adaptability to the use of AI

tools can be done by designing measurement

questionnaires to categorize the groups.

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Alt Text for Figure: Steps towards achieving complementary skills and emotional connection of employees using

AI tools for employees in organizations.

Figure 1: Skills Complementarity-Emotional Connection Model Diagram.

5.2 Theoretical Breakthroughs

Existing research has long been fragmented by

disciplinary perspectives - management scholars

focus on technology effectiveness and productivity

improvement, while psychologists focus on mood

swings and mental health, resulting in a fragmented

explanation of the skill-emotion interaction

mechanism. The theory of skill complementarity

emphasizes that AI tools optimize efficiency through

task division, yet it does not reveal the inhibitory

effect of emotional depletion on skill transformation;

the study of affective connection portrays the path of

anxiety generation but overlooks the moderating

effect of skill level on affective response (Morandini

et al., 2023). This split essentially stems from

methodological differences: quantitative studies

prefer measurable efficiency indicators, while

qualitative studies are more concerned with in-depth

descriptions of subjective experiences.

By integrating complementarity theory and

affective connection theory, this study constructed the

Skills Complementarity Model, which realizes three

levels of theoretical innovation. Firstly, From the

perspective of dynamic interaction, the study reveals

that skill enhancement and emotional depletion are

not independent processes but form a feedback loop

through self-efficacy. Skill degradation can erode

employees' confidence in their own abilities, which in

turn increases technical anxiety, which in turn inhibits

the active acquisition of new skills. Secondly,

expanding on the ethical dimension to include the

impact of emotional connection on employees’

behaviors using AI tools in employee training and

development considerations, concluding that

classifying different employees before training will

increase employees’ acceptance of AI tool use and

reduce cognitive dependence and negative emotions

caused by inappropriate AI use. Lastly, constructing

an interdisciplinary explanatory framework that

combines the emotional connection between AI and

human beings studied in the field of psychology with

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135

the impact of AI tools on the skill aspects mastered

by employees studied in the field of management

from the perspective of the discipline of human

resources, to point out the direction for organizations

to train their employees related to the use of AI

(Azeem et al., 2024).

5.3 Theoretical and Practical

Extensions

The core value of this study is that it breaks through

the interpretative limitations of a single discipline and

provides an integrative analytical framework for

human-computer collaborative management in the

digital workplace. At the theoretical level, the

proposed model bridges the gap in existing literature

on the skill-emotion interaction mechanism and

provides a testable hypothesis system for subsequent

research. At the practical level, the findings of the

study provide a threefold message to business

managers (Krishnan et al., 2024). Organizations

should consider the boundaries of using AI tools in

work tasks, establish a dynamic evaluation

mechanism, and adjust the degree of AI intervention

according to the complexity of the task and the ability

level of the employee, to avoid the degradation of

skills caused by over-reliance on AI tools. The

enterprise needs to have an emotional support system,

incorporate AI ethics training into the regular

management system, enhance employees' awareness

of the limitations of the technology through

workshops, case studies and other forms, and reduce

the trust crisis caused by algorithmic errors

(Natarajan & Gombolay, 2020). Organizations can

differentiate their capacity building by designing a

technical-psychological dual-track training program

for highly ethically sensitive employees, which

enhances their critical examination of algorithmic

decision-making while improving their tool operating

skills.

5.4 Research Limitations and Future

Directions

Due to the limitations of secondary data and data

acquisition methods, this study was unable to track

the cumulative effects of long-term AI tool use on

employees' career development. For example, skill

degradation may exhibit progressive characteristics,

the tipping point of which is difficult to capture

through cross-sectional data. In addition, the samples

mainly come from technology-intensive industries

that have been well studied in literature, and the

interaction patterns between AI tools and employee

competencies in traditional industries (e.g.,

manufacturing, education) remain to be explored

(Yang & Yan, 2024).

Future research can be deepened in three

directions: first, adopting a mixed research

methodology to reveal the trajectory of skill evolution

through tracking experiments and in-depth interviews.

Second, conducting cross-cultural comparisons to test

the mitigating effect of organizational training on the

emergence of employees' technological anxiety

across different cultures, and third, focusing on the

impact of generative AI (e.g., big language models)

knowledge workers, especially the relationship

between technological dependency and creativity's

paradoxical relationship (Fang et al., 2024).

6 CONCLUSION

The widespread use of AI tools in the workplace

opens new paths for employee skill enhancement but

also brings potential risks of emotional detachment

and capability degradation. This study systematically

reveals the double-edged sword effect of AI tools:

while significantly improving efficiency through task

division and skill complementation, over-reliance

will weaken employee autonomy, triggering skill

degradation and negative emotional connection.

Especially in the highly ethically sensitive group,

concerns about the fairness of algorithms

significantly reduce the acceptance of the technology,

highlighting the critical impact of individual

differences on the effectiveness of AI applications.AI

tools can cause emotional bonding phenomena while

improving the skills of employees, suggesting that the

inappropriate use of AI tools can lead to skill

degradation and re-establishment of negative

affective bonding. Whereas the proper use of AI tools

can greatly improve efficiency, highly ethical

employees are less receptive to AI tools, so

companies that need to use AI as aid in the modern

era should provide different training for different

groups of employees. Research shows that the

efficiency of AI tools stems from their data

processing and automation advantages, but the

rapidity of technology iteration also forces employees

to adapt frequently, resulting in hidden skill reset

costs. In addition, the complexity of the emotional

connection is reflected in the co-existence of tool

dependence and psychological alienation - the lack of

algorithmic transparency exacerbates employee

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mistrust, while the imbalance of technological

enablement may inhibit innovation. In conclusion,

managers should pay attention to employee

psychology, conduct regular sniffing or checking to

ensure that employees are using AI tools correctly,

and continually train employees in line with

organizational goals.

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