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The Integration of Artificial Intelligence in Policing: A Study of Future
Directions of the Nigeria Police Force
ASP Shide Sunday, PhD
Department of Police Professional Courses,
Nigeria Police Academy Wudil, Kano State, Nigeria
DOI: https://doi.org/10.51244/IJRSI.2025.1210000279
Received: 11 September 2025; Accepted: 12 November 2025; Published: 19 November 2025
ABSTRACT
Policing is an important aspect of the enforcement of law and order as well as public protection while
Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically
require human intelligence. This study investigated “The Integration of Artificial Intelligence (AI) in Policing:
A Study of Future Directions of the Nigeria Police Force”. The study employed a Cross-Sectional Survey
Design, where 384 Police Personnel consisting of 201 (52%) Males and 183 (48%) Females were used for the
study. Their ages ranged from 19 to 59 years with the Mean of 38.22 (SD=8.45227). 223 (58%) of the
respondents were junior cadre while 161 (42%) were from the senior cadre. Also, 163 (42%) of the
respondents were married, 87 (23%) were divorced while 134 (35%) were single. The Police Perceived AI
Use Questionnaire (PPAIUQ) was used for data collection. Statistical analysis involved the use of descriptive
Statistics. Findings from the hypotheses indicated that, AI integration in policing significantly leads to
improved crime prevention, crime investigation and surveillance in Nigeria. It was finally revealed that, AI
integration in policing significantly leads to future Directions of the Nigeria Police Force. Based on the
findings, it was recommended that, Nigeria Police Officers should be trained on AI integration in policing for
improved crime prevention in Nigeria. Also, Government/Nigeria Police Management Team should fully
integrate AI in policing in order to improve crime investigation in Nigeria. Finally, more researches should be
encouraged on The Integration of Artificial Intelligence in Policing.
Keyword’s: Artificial Intelligence (AI), Policing, Future Directions and the Nigeria Police Force
INTRODUCTION
The use of Artificial Intelligence (AI) in policing has gained significant attention in recent years, driven by the
need for more efficient and effective law enforcement strategies (Brayne, 2020). Artificial Intelligence (AI)
refers to the development of computer systems that can perform tasks that typically require human
intelligence such as learning, problem-solving, and decision-making among others. The term Artificial
Intelligence (AI) was coined by John McCarthy in 1955 at a conference (workshop) held at Dartmouth
(McCarthy, 1955). These tasks can simply be explained as decision-making, learning, problem-solving and
perception among others. Research finding has shown that, Artificial Intelligence (AI) technologies have the
potential to revolutionize policing by enhancing crime prevention, investigation, and public safety (Ferguson,
2017).
Artificial Intelligence (AI) encompasses a broad range of concepts and techniques which include: Machine
Learning (Supervised Learning, Unsupervised Learning & Reinforcement Learning) Deep Learning (Neural
Networks, Convolutional Neural Networks & Recurrent Neural Networks), Natural Language Processing
(Text Analysis, Sentiment Analysis, Language Translation, Data & Algorithms), Computer Vision (Image
Recognition, Image Segmentation & Object Detection) and Robotics (Autonomous Systems &Robotics
Process Automation) among others. The advent of Artificial Intelligence (AI) has revolutionized various
sectors, including law enforcement. As crime trends evolve and become increasingly sophisticated, the
Nigeria Police Force must adapt and leverage technology to stay ahead. The integration of AI in policing has
the potential to significantly enhance crime prevention, investigation, and public safety.
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The history/origin of Artificial Intelligence (AI) can be traced back to thousands of years and to ancient
philosophers considering questions of life and death (Copeland, 2025).The idea of creating machines that can
function independently has been around since ancient times. One of the earliest records of an automaton
comes from 400 BCE, referring to a mechanical pigeon created by a friend of the philosopher Plato otherwise
known as Ancient Automatons ((Brayne, 2020). However, more notable history/origin of Artificial
Intelligence (AI) can easily be viewed from three major phases. These include: Rule-Based Systems in the
1960s-1980s, Expert systems in the 1980-1990 and Modern Machine Learning in the -2000s to present. Some
notable historic achievement in the development and origin of Artificial Intelligence (AI) include: 1900s Era:
In 1990s, Scientists Karel Capek coined the word "robot" in 1921 which served as a testing ground with
Artificial Brain. in 1929 Era, a Japanese Professor Makoto Nishimura built the first Japanese robot, named
Gakutensoku. Also, in 1949 Era: a Computer Scientist Edmund Callis Berkley published a book titled' "Giant
Brains”, or “Machines that Think," comparing of newer models of computers to human brains (Weise, Metz,
Grant, & Isaac, 2023).
However, the modern concept of Artificial Intelligence (AI) began taking shape in the 20th century; with
significant milestones from 1950s to 2000s. These include 1950s when the Dartmouth Summer Research
Project on Artificial Intelligence, led by John McCarthy marked the beginning of Artificial Intelligence (AI)
as a field of research in 1955). It started in 1952 when Arthur Samuel developed a program to play checkers.
Alan Turing also proposed the Turing Test to measure a machine's ability (Computer Machinery &
Intelligence) which exhibited intelligent behavior equivalent to, or indistinguishable from, that of a human.
1960s marked the development of the first Artificial Intelligence (AI) programs, such as ELIZA and
SHRDLU, demonstrated the potential of Artificial Intelligence (AI) and increased adoption in many industries
(Roumeliotis, & Tselikas, 2023). In 1495 Leonardo da Vinci's Automaton designed a mechanical knight that
could sit up and wave its arms while 2000s brought about the rise of big data, cloud computing, and deep
learning which led to significant advancements in Artificial Intelligence (AI) research and applications.
There are several types of Artificial Intelligence (AI) and they are categorized based on their capabilities,
applications, and functionality. However, these types are not mutually exclusive (Richardson, 2023). The
development and applications of AI continue to evolve, leading to new types and classifications. These types
include Narrow or Weak Artificial Intelligence (AI), General or Strong Artificial Intelligence (AI), Super-
intelligence Artificial Intelligence (AI)/ Artificial General Intelligence (AGI), Reactive Machines Artificial
Intelligence (AI), Limited Memory Artificial Intelligence (AI) nd Theory of Mind Artificial Intelligence (AI)
among others.
There are AI computer programs designed to simulate human-like conversations with users, such as text or
voice interaction. These include: ChatGPT (Claude, Meta AI & Zapier Agents), AI Writing (Grammarly,
Plagiarism Checker, Quarkle, Jasper & Writer), AI Image Generators (DALL-E 3, Midjourney, Ideogram &
Adobe Firefly), Content Generation (Synthesia & Loom), AI Video Generation (Runway, Descript &
Wondershare Filmora), AI Education and Learning (Education Apps, Khan Academy's Khanmigo, Task and
Project Management, Asana & BeeDone), Scheduling (Reclaim, Clockwise & Motion), AI Development and
Coding (Tabnine, CodeWhisperer & Ghostwriter) as well as other application such as Amazon Alexa,
Customer Service, Empower, Data Analysis and DataRobot.
The integration of Artificial Intelligence (AI) in policing has the potential to significantly enhance law
enforcement capabilities, from predictive policing and advanced crime data analysis to facial recognition and
real-time crime analysis (Frank, 2023). While Artificial Intelligence (AI) offers numerous benefits, including
improved efficiency, accuracy, and decision-making, it also raises important questions about ethics, bias, and
transparency (Roumeliotis, & Tselikas, 2023). Artificial Intelligence (AI) is increasingly being used in
policing to enhance efficiency, effectiveness and decision-making. Therefore, integration of Artificial
Intelligence (AI) in Police work offers numerous benefits and advantages which include: Predictive Policing,
Surveillance/Monitoring and Investigation and Crime Analysis.
Studies have shown that Artificial Intelligence (AI) can significantly contribute to crime prevention by
analyzing crime data, identifying crime patterns, Criminal Modus Oparandi and predicting potential crimes
(John, 2019). Some ways Artificial Intelligence (AI) can help include Predictive Policing, Surveillance and
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Monitoring and Crime Data Analysis among others. By leveraging Artificial Intelligence (AI) in crime
prevention, the Police can enhance public safety, improve efficiency, and make data-driven decisions. It is
against this background that, this study sought to investigate The Integration of Artificial Intelligence in
Policing: A Study of Future Directions of the Nigeria Police Force
Statement of the Problem
Policing and law enforcement are perceivably stressful events across the globe which Nigeria is not an
exception (John, 2024). Observation over the years has shown that, challenges associated with policing are
becoming ramparts due to technological advancement and modern crime trend. This development led to
numerous difficulties face by police personnel in the cause of their Criminal Investigation, Predictive
Policing, Surveillance and Monitoring.
The lack of comprehensive research on the opportunities and challenges of Artificial Intelligence (AI)
integration in policing has hindered the development of effective policies and guidelines for its adoption
(Robert, 2024). Consequences of these problems go beyond boundaries of the police circle. It may affect
families, communities and the country at large. This explains why, not much study of this nature were carried
out in the study area therefore, the understanding of “The Integration of Artificial Intelligence in Policing: A
Study of Future Directions of the Nigeria Police Force run the risk of becoming culturally biased. It is against
this background and problems identified that, this study seeks to address the gap by providing an in-depth
analysis of the benefits and challenges of Artificial Intelligence (AI) in policing.
It was therefore hypothesized that:
1. AI integration in policing will significantly lead to improved crime prevention in Nigeria
2. AI integration in policing will significantly lead to improved crime investigation in Nigeria
3. AI integration in policing will significantly lead to improved surveillance in Nigeria
4. AI integration in policing will significantly lead to Future Directions of the Nigeria Police Force
METHODS
This deals with the method that was adopted in carrying out the study. Specifically, the procedure employed in
investigating The Integration of Artificial Intelligence in Policing: A Study of Future Directions of the Nigeria
Police Force. The section contains the design, setting, sampling, sample size determination, instruments, pilot
study, procedure, participants and data analysis.
Design
This study employed a cross-sectional survey design to elicit information from respondents on the Integration
of Artificial Intelligence in Policing: A Study of Future Directions of the Nigeria Police Force. This research
design enabled the researcher elicited information from respondents (Police Officers) cutting across different
sex, rank, age, marital status, education, and income among Nigeria Police Personnel working under
investigation, intelligence and operational units; which adequately measured the study variables. The
independent variable in this study is artificial intelligence while the dependent variable is the Future
Directions of the Nigeria Police Force.
Setting
The study was conducted among Police Personnel serving under Investigation, Intelligence and Operation
Units within Police Zone 1 (Kano & Jigawa States), Zone 2 (Lagos & Ogun States), Zone 4 (Benue, Nasarawa
& Plateau States) and Zone 7 (Abuja & Niger States). The Zones are located in northwest, southeast, and
north central. The reason for the choice study areas is to enable the researcher to elicit relevant response
needed for the study hence; the zones are made of Police Personnel drawn from almost all parts of the
country.
Sampling
Multi-Stage Sampling Procedures were used for the study. Cluster Sampling Technique was at the first stage
used for selection of study areas (Zone 1, Zone 2, Zone 4 and Zone 7). Purposive Sampling technique was
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used at the second stage. The investigator used Purposive Sampling technique in identifying individuals who
were considered to be typical of the population (only Police Officers) and selected them as sample (Akinsola,
2005). Finally, Proportional Sampling Technique was used at the third stage to ensure that, Police Personnel
at different sex, rank, age, marital status, education, and income who volunteered within the Study Area were
all represented.
Sample Size Determination
The sample size for this study was determined using Krejcie and Morgan’s (1970) Sample Size Estimation
Table for known population. Considering the population of 193,336 Police Personnel working under
Investigation, Intelligence and Operation Units out of the total Police Personnel strength of 371,800; using
Krejcie and Morgan’s Sample Size Estimation Table, the ideal sample size is 384 (Idris August 2023).
To further buttress the sample size figure from Krejcie and Morgan’s (1970) sample size table, their formula
was applied using the population of 193,336 Police Personnel working under Investigation, Intelligence and
Operation Units. The formula is stated thus:
S= X
2
NP (1 - P) ___
d
2
d(N- 1) + X
2
P (1-P)
Where:
S = Required sample size
X = Z value (1.96 for 95% confidence level)
N = The population size
P = Population Proportion (expressed as a decimal; assumed to be 0.5 i.e. 50%)
D= Degree of accuracy (5%) expressed as a proportion (.05); i.e. the margin of error
Therefore substituting the formula stated above
S= 1.96
2
X193,336 X0.5 (1 – 0.5) _
0.05
2
(193,336 - 1) + 1.96
2
X0.5
(1 – 0.5)
S= 1.96
2
X193, 336 X0.5 (0.5) _
0.05
2
(193,335) + 1.96
2
X0.5
(0.5)
S= 3.842X193, 336X0.25
2.5X193, 335 + 3.84X0.25
S= 742,796,912X0.25
483337.5+ 0.96
S= 185,699,228
483, 3338,46
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S= 384
This means that the total number of Police Personnel from the selected Units was ascertained and their
respective proportions in the sample size of 384 Police Personnel also determined.
1. The population of Police Personnel of Intelligence Unit: 18,590
2. The population of Police Personnel of Investigation Unit: 26,026
3. The population of Police Personnel of Operation Unit: 148,720
Questionnaire were administered to 384 Police Personnel in three selected Units (Investigation, Intelligence
and Operation Units) within Police Zone 1 (Kano & Jigawa States), Zone 2 (Lagos & Ogun States), Zone 4
(Benue, Nasarawa & Plateau States) and Zone 7 (Abuja & Niger States). Therefore, Proportional Sampling
method was applied. Halleck’s (2001) formula for proportional distribution was used to determine the sample
for each of the Area Commands as shown below: (
n
N
)Ni
N= Population Size per Stratum
N= Total Population
Ni= Determined Sample Size
i. Intelligence Unit: 18,590 18,590 X384 =37
193,336
ii. Investigation Unit: 26,026 26,026X384 =52
193,336
iii. Operation Unit: 148,720 148,720X384 =295
193,336
Instruments
Data for this study was collected using a designed survey research questionnaire titled: “Police Perceived AI
Use Questionnaire”: The 6 items instrument was developed by the researcher in 2025 using yes or no response
to elicit information from respondents.
Pilot Study
In order to ensure reliability and validity of the instrument used on the study sample, the instruments (Police
Perceived AI Use Questionnaire) were subjected to pilot study using police Personnel in Kogi State Police
Command. The choice of the location is due to the fact that these Police Officers have similar characteristics
with the proposed population for the main study which is the Police Personnel working under Investigation,
Intelligence and Operation Units within Police Zone 1, 2, 4 and Zone 7. Hence, this ensured robust and
objective trial that qualified the instrument for a major study that is efficient and objective.
For this Pilot Study, a total number of 145 copies of instruments were administered to the participants using
convenience sampling in which each officer were contacted while on duty and responded voluntarily. Out of
the number 145 copies of questionnaires distributed, 139 were returned representing the return rate 95.9 per
cent while 6 copies representing 4.1 per cent were not returned. Of the 139 participants, 84 (60%) were male
while 55 (40%) were female. Their ages ranged from 18 to 59 years. While 71 (51%) participants were senior
cadre 68 (49%) participants were junior cadre, 90 (65%) participants were SSCE holders, 25 (18.%)
participants were OND/NCE holders, 15 (11%) participants were HND/First Degree holders and 9 (6%)
participants were M. Sc. /PhD holders. Finally, 67 (48%) participants were single, 51 (37%) were married, 14
(10.1%) were divorced and 7 (5%) participants were widows.
The researcher made used of Cronbatch’s Alpha test of reliability to determine norms of the instruments used:
ranging from 0 to 1 (George & Miller 1995) which was based on SPSS/PC+ step by step interpretation. The
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Cronbatch’s Alpha test of reliability according to George & Miller holds that, higher value denotes higher
internal consistency. These values and the norms were considered as follows:
1. A value below 0.5 range shows unacceptable level of reliability
2. A value between 0.5 and 0.6 range could be considered as a poor level of reliability
3. A value between 0.6 and 0.7 range could be considered as a weak level of reliability
4. A value between 0.7 and 0.8 range would be refer to an acceptable level of reliability
5. A value between 0.8 and 0.9 range would be considered as a good level of reliability
6. A value above 0.9 range would be refer to an excellent level of reliability
The result of The Pilot study showed that: the item total correlation of the 6 items for Police Perceived AI Use
Questionnaire ranged from .44 to .79. The output of the result yielded a Cronbach’s Alpha of .74 which was
considered adequate for use in this study.
Procedure
The researcher personally traveled to the study areas as proportionally sampled and administered the
questionnaire to the Personnel. At each point, the researcher established rapport with the respondents; after
which their consent was sought. Finally, questionnaires were administered to them with assurance that the
information will be handled confidentially.
Participants
The participants for this research cut-across Police Personnel of different sex, age, rank, marital status,
education and income in Police Zone 1 (Kano & Jigawa States), Zone 2 (Lagos & Ogun States), Zone 4
(Benue, Nasarawa & Plateau States) and Zone 7 (Abuja & Niger States), which were purposely drawn from
Police Personnel of Intelligence, Investigation and Operation Units. The size of the population is 193,336
Police Personnel working within the sampled areas 384 were sample for the study.
Ethical Consideration
The proposal for the study was submitted to the Police for approval for approval. Approval was granted, all
the participants were informed clearly about the study as well as the data collection procedure. They were
allowed to voluntarily participate in the study. They were also allowed to withdraw at any time without
consequences if they so wish. The participants’ anonymity was respected. The study did not asked for the
participants’ name, however, other demographic characteristics such as Sex, Rank, Marital Status, Income
Level, Ethnic Group, Educational Level, LGA, State and Age were asked. All the information collected by the
researcher was kept safe and protected for the purpose of this study only.
Data Analysis
The researcher used 21 version of statistical package for social sciences (SPSS) to analyze the data in which
the correlation analysis was first used to find out the reliability and validity of the instruments. The final
statistics used was descriptive Statistics. Simple Percentages were chosen to test whether AI integration in
policing will significantly lead to improved crime prevention in Nigeria, AI integration in policing will
significantly lead to improved crime investigation in Nigeria and whether AI integration in policing will
significantly lead to improved surveillance in Nigeria
RESULTS
This study examined the Integration of Artificial Intelligence in Policing: A Study of Future Directions of the
Nigeria Police Force. In regards to this, data were collected, tested and this chapter presents results derived
from data analysis according to the stated hypotheses:
Table 1 Shows Questionnaire Response Information
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Description
Number
Percentages
Administered questionnaire
400
100%
Questionnaire returned
384
96%
Questionnaire not returned
16
4.%
Total
384
100%
Source: Researcher’s Survey, (2025)
The table 1 indicates the total number of questionnaire distributed, returned as well as the number not
returned. Based on the result presented above; out of 400 (100%) questionnaires administered; 384 (96%)
were returned while 16 (4%) questionnaires were not returned.
Table 2 Shows the Demographic Characteristics of the Respondents/Participants
Sex/Gender
Frequency
Percentage
201
52%
183
48%
384
100%
Rank
223
58%
161
42%
384
100%
Age
204
53%
180
47%
384
100%
Marital Status
163
42%
87
23%
134
35.%
384
100%
Education
156
41%
94
24%
88
23%
46
12%
384
100%
Income
206
54%
178
46%
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384
100%
Source: Researcher’s Survey, (2025)
Results presented on Table 2 above shows that, out of 384 respondents; 201 (52%) were male while 183
(42%) were female. Also, out of 384 respondents; 223 (58%) were junior cadre while 161 (42%) senior cadre.
Result further shows that, out of 384 respondents; 204 (53%) were between the age of 18-39 years while 180
(47%) were between the age of 40-59 years. Also, out of 384 respondents; 163 (42%) were married, 87 (23%)
were divorced while 134 (35%) were singles. On educational status of the respondents; 156 (41%) were SSCE
holders, 94 (24%) were OND/NCE holders, 88 (23%) were HND/First Degree Holders and 46 (12%) were
M.Sc./PhD holders. Finally, result on income shows that, out of the 384 respondents; 206 (54%) were of low
income status while 178 (46%) were of high income status.
Hypotheses Testing
The four hypotheses of this study were tested using the responses on table 3 below:
Table 3 Indicating Participants’ Responses
S/N
Items
Yes
Frequency
Percentage
No
Frequency
Percentage
1
I often apply AI tools in the
cause of my Police duties?
335
335
87%
49
49
13%
2
I have not applied any AI tool
in the cause of my Police
duties?
50
50
13%
335
335
87%
3
Applying AI tools can
improve crime prevention?
315
315
82%
69
69
18%
4
Applying AI tools can
improve crime investigation?
323
323
84%
61
61
16%
5
Applying AI tools can
improve improved
surveillance?
333
333
87%
51
51
13%
6
AI integration can brighten
the future directions of
policing?
345
345
90%
39
39
10%
Total
1,701
1,701
443%
604
604
157%
Source: Researcher’s Survey, (2025)
Hypotheses I: This hypothesis states that AI integration in policing will significantly lead to improved crime
prevention in Nigeria. This hypothesis was tested using Simple Percentages; results (responses) tabulated as
shown on table 3 above and interpreted.
The results revealed that, AI integration in policing will significantly lead to improved crime prevention in
Nigeria: f = 315(82%)>69(18%) N=384). This means that, the more effort is made to applying AI tools in
policing; the more Police can achieve improved crime prevention in Nigeria.
Hypotheses II: This hypothesis states that, AI integration in policing will significantly lead to improved
crime investigation in Nigeria. This hypothesis was tested using Simple Percentages; results (responses)
tabulated as shown on table 3 above and interpreted.
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The results revealed that, AI integration in policing will significantly lead to improved crime investigation in
Nigeria: f = 323(85%)>61(16%) N=384). This implies that, the more Police Personnel apply AI tools in
policing; the more Police can achieve improved crime investigation in Nigeria.
Hypotheses III: This hypothesis states that AI integration in policing will significantly lead to improved
surveillance in Nigeria. This hypothesis was also tested using Simple Percentages; results (responses)
tabulated as shown on table 3 above and interpreted.
The results revealed that, AI integration in policing will significantly lead to improved surveillance in Nigeria:
f = 333 (87%)>51(13%) N=384). This means that, the more Police Personnel apply AI tools in policing; the
more Police can achieve improved surveillance in Nigeria.
Hypotheses IV: This hypothesis states that, AI integration in policing will significantly lead to Future
Directions of the Nigeria Police Force. This hypothesis was also tested using Simple Percentages; results
(responses) tabulated as shown on table 3 above and interpreted.
The results revealed that, AI integration in policing will significantly lead to Future Directions of the Nigeria
Police: f = 1,701 (443%)>604 (157%) N=384). This implies that, the more Police integrate AI in policing the
more Police can actualize desirable future directions in the Police Force.
DISCUSSION
Various hypotheses in relationship to the study were discussed in this section:
Hypothesis One: Hypothesis one was tested to find out if AI integration in policing will significantly lead to
improved crime prevention in Nigeria. This hypothesis was confirmed due the result of data analysis that was
enough to give statistical significance. The finding supports the work of Weise, Metz, Grant, & Isaac, (2023).
According to them, AI integration in policing will help the Police to improve crime prevention and achieve a
maximum secured environment. This finding is also in line with the work of (Brayne, 2020), whose study
revealed that, AI integration in Policing is the major remedy for crime prevention. This leads to accept the fact
that “psychological well-being of police officers should. This finding is instrumental to the Police Force and
the society because it gives clear way and need to integrate AI in crime prevention.
Hypothesis Two: Hypothesis two sought to find out if AI integration in policing will significantly lead to
improved crime investigation in Nigeria. Again, this hypothesis was confirmed. This finding agreed with the
work of Copeland, 2025) whose work revealed that, the more modern technologies such as AI tools are used
in crime investigation; the more to improve crime investigation with needed evidence for prosecution. A
similar research finding by Roumeliotis, and Tselikas, (2023) also revealed that, AI integration in policing
will reduce human error and incapacitations in crime investigation. However, this finding disagreed with the
finding of Richardson, (2023) whose work revealed that, AI integration in policing cannot replace human
resource roles. Also, in line with this finding is the work Robert, (2024) who noted that, AI integration is the
most needed solution to every aspect of human endeavor including the security of lives and properties. The
implication of this finding to these findings to the current study is the limited extent to which AI integration
can improve crime investigation.
Hypothesis Three: Hypothesis Three was to find out if AI integration in policing will
significantly lead to improved surveillance in Nigeria. The Hypothesis was statistically
examined and findings indicated that, AI integration in policing will significantly lead to
improved surveillance in Nigeria. This finding is similar to the finding of a study carried out by
John, (2024) whose work revealed that, while AI integration in policing remained the major
breakthrough in achieving adequate security surveillance. Weise, Metz, Grant, & Isaac, (2023)
also conducted a study that is in line with finding. His work revealed that, AI integration in
policing stand the chance of revitalizing all aspects of policing. While these findings are similar
to the finding of the current study; but failed to give clear direction on how the AI tools can be
applied.
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Hypothesis Four: Hypothesis four sought to find out how AI integration in policing will
significantly lead to Future Directions of the Nigeria Police Force. The result made revelation
on the contributions of Respondents which show that, AI integration in policing will
significantly lead to Future Directions of the Nigeria Police Force. This finding is in line with
the finding of a study conducted by Copeland, (2025). His finding holds that, AI integration in
policing will brighten the Future Directions of the Nigeria Police Force. This finding is also in
line with the study carried out by Weise, Metz, Grant, and Isaac, (2023). Findings from their
studies revealed that, AI integration in policing remain the contemporary approach toward
achieving general security in general and future directions of the Nigeria Police Force in
particular. While these findings remain similar and relevant to the finding of the current study;
the implication of the findings is that, future directions associated with AI integration requires
high experts’ services
CONCLUSION AND RECOMMENDATIONS
Conclusively, the present study examines The Integration of Artificial Intelligence in Policing: A Study of
Future Directions of the Nigeria Police Force and the main findings of the study are summarized as follows:
1. AI integration in policing significantly leads to improved crime prevention in Nigeria.
2. AI integration in policing significantly leads to improved crime investigation in Nigeria.
3. AI integration in policing significantly lead to improved surveillance in Nigeria.
4. AI integration in policing significantly leads to future Directions of the Nigeria Police Force.
Based on the findings of this study, the following recommendations were hereby advanced:
1. Nigeria Police Officers should be trained on AI integration in policing for improved crime prevention in
Nigeria.
2. Government/Nigeria Police Management Team should fully integrate AI in policing in order to improve
crime investigation in Nigeria.
3. The Police, Government and all relevant security stakeholders should insist on AI integration in Policing
for improved surveillance in Nigeria.
4. Finally, more researches should be encouraged on The Integration of Artificial Intelligence in Policing.
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