INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 722

www.rsisinternational.org

AI-Driven Monitoring and Evaluation: The Future of Transparent

and Accountable Governance in Public Project Implementation

Bildad Awere1, Daniel Mishael Masetu2

1Researcher, Strategy and Policy Expert, Tripex Oddsey Limited

2HSC, Director, Efficiency Monitoring and Evaluation, Nairobi City County Government, Kenya

DOI: https://doi.org/10.51584/IJRIAS.2025.1010000057

Received: 04 October 2025; Accepted: 10 October 2025; Published: 05 November 2025

ABSTRACT

Artificial intelligence (AI) is emerging as a disruptive technology in public governance, particularly in the form

of monitoring and evaluation (M&E) systems that are required for transparent and accountable implementation

of public projects. Focusing on AI's potential to improve efficiency and reduce corruption, as well as its role in

fostering institutional integrity, the paper explores how it can be employed to bolster government, specifically

through its incorporation into M&E processes. The objectives were the analysis of AI in promoting innovation

and transparency in M and E systems; the assessment of the institutional and infrastructural conditions related

to AI; and the impact of AI-enabled M and E systems on reduction of corruption. The study drew on the systems

theory of technology, human resource, and institutional structures and used a mixed-method approach involving

quantitative survey and qualitative interviews. Results indicated that the use of AI positively affected governance

by automating the data collection process, providing the ability to track projects in real-time and identifying

inefficiencies. Nevertheless, only a few challenges were noted in developing countries specifically and referred

to as infrastructural incompatibilities, bandwidth limitations, and ethics, contrary to developed countries where

a vast range of negative experiences were noted. But these objections did not nullify the positive impact of AI-

powered systems, they only limited it. While there is limited empirical research on the topic, the study confirmed

global trends in the use of AI in governance as well as the unique challenges of developing countries like Kenya.

The paper concluded that AI holds significant potential for supporting transparent government, but it should not

be implemented without critical thought being given to infrastructure development, capacity, and ethical

controls. Among these recommendations are concrete actions for the integration of AI into existing governance

processes, as well as policy recommendations for investments in infrastructure and training for AI. Future work

should focus on, for example, AI ethics, blockchain technologies for M&E systems, and global benchmarking

to enhance the scalability and ethics of public sector governance using AI.

Keywords: Artificial intelligence, Monitoring and evaluation, Governance, Transparency, Accountability,

Systems Theory, Corruption, Public project implementation, Mixed methods

BACKGROUND

Over the last couple of years, artificial intelligence (AI) has emerged as a transformative trend for governmental

operations, offering fresh opportunities to drive transparency, accountability, and data-driven decision-making.

The integration of AI with public administration has surged in popularity around the globe and revolutionized

how governments organize, carry out, and monitor the progress of development schemes (Aldemir & Ucma

Uysal, 2025; Misic et al., 2025). Broadly speaking, across all advanced economies, applications of AI-from

predictive analytics to automated surveillance-are changing the practice of governance by making it more

productive, human-error free, and responsive to the public sector (Dickinson et al., 2023). This digitalization has

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 723

www.rsisinternational.org

brought about a shift from government governance from a reactive to a proactive approach in performing its

public duties by providing information on how things are going and where the risks are (Choi et al., 2024).

This evolution is written into the evolution of monitoring and evaluation (M&E) systems. Traditionally, M&E

relied on data collection through manual systems, human analysis and ex-post implementation critique, which

limited its timeliness and accuracy. However, there has been recently a surge of AI-based M&E platforms that

can support an automatic and continuous analysis of data for the predictive assessment process and adaptive

project management (Batool et al., 2025; Gomes Rego de Almeida et al., 2025). Apart from assisting with the

improved monitoring of performances, such systems also allow stakeholders to determine decisions throughout

the project cycles (Babsek & Spacek, 2024). Also, AI is no longer understood as merely an innovation in

technology, but as an instrument of governmentality, which can strengthen institutional responsibility and trust

in the public sector (de Fine Licht & Christensen, 2023).

However, irrespective of these developments, corruption is a global phenomenon and hence must not be ignored

because it adversely affects development and erodes peoples' trust. Flowering corruption: Historical abuse of

public funds as a result of weak oversight mechanisms, manipulation of data, and a lack of transparency (Kobis,

2022; Omotoye, 2025) In response, governments and international organizations are exercising devotional

efforts to understand how these artificial intelligence (AI) technologies can be adopted to detect anomalies, flag

irregular transactions, and predict potential cases of corruption in the implementation of projects (Robles &

Rios24). The introduction of such Systems that incorporate integrity through AI is a monumental shift away

from reactive audits to a paradigm of proactive governance centered on monitoring and accountability grounded

in predictive analytics (Bahrad & Nguyen, 2025). Scholars argue that if developed responsibly, AI can be part

of the solution to bolstered integrity systems through a technology, ethics, and institutional capacity convergence

(Yang & Goldsmith, 2025; Pi 2023).

As countries aspire to be on track to fulfill the Sustainable Development Goals, and Goal 16 in particular on

peace, justice and strong institutions, the emergence of AI for M&E also offers a unique opportunity to

institutionalize transparency and combat corruption globally. The fusion of digital transformation, governance

reform and smart monitoring, therefore, is the upcoming frontier of public project accountability (Yang & Al-

Masri, 2025).

Problem Statement

Despite the impressive progress made in the digitalization of governance, the M&E of government-supported

projects in most countries only relies on the old existing manual systems with unconnected data systems, sluggish

reporting and limited analytical capacity. These traditional practices complicate prompt decision-making and

undermine oversight, and tend to obscure pathways to accountability (Batool et al., 2025; Gomes Rego de

Almeida et al., 2025). In developing economies, M&E is a bureaucratic activity rather than an intelligent adaptive

system that finds inefficiencies and threats of corruption in real times (Babsek & Spacek, 2024).

Lack of data-driven monitoring models has resulted in the constant resurgence of poor governance practices,

which in turn has affected the performance of projects and the resultant loss of confidence in the governance of

it. The traditional tools of M&E are particularly weak in detecting deviations or manipulation of cash use in the

context of large scale public spendings that results in inflated budgets, uncompleted projects, and compromised

service delivery (Aldemir & Ucma Uysal, 2025, and de Fine Licht & Christensen, 2023). While AI technologies

have shown potential for automating data validation and predicting delays, but also in the area of transparency,

the adoption of AI technologies in public institutions is sluggish due to infrastructural gaps and capacity

constraints, as well as ethical considerations (Misic et al., 2025; Choi et al., 2024).

The use of traditional evaluative models versus the latest AI-based data is therefore a critical governance issue.

Without using artificial intelligence to predict and analyse data, in order to continue to perpetuate inefficiencies

and corruption, governments will not be able to deliver on the outcomes of sustainable development. Therefore,

there is an urgent need to investigate how AI-enabled M&E could change accountability structures and proceed

to the implementation of projects in a transparent public manner (Kobis, 2022; Omotoye, 2025; Yang &

Goldsmith, 2025).

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 724

www.rsisinternational.org

OBJECTIVES

General Objective:

To assess how AI-driven monitoring and evaluation can promote transparent and accountable governance in

public project implementation.

Specific Objective

To analyze the role of AI in strengthening innovation and transparency in M&E systems.

To examine institutional and infrastructural factors affecting AI adoption in project monitoring.

To evaluate the impact of AI-enabled M&E on reducing corruption and enhancing governance outcomes.

Significance of the Study

This research made a theoretical contribution by enriching the debate on technology-informed governance by

developing AI as part of monitoring and evaluation systems (Batool et al., 2025; Misic et al., 2025). For policy

and practice, it provided practical insights to governments and devel. Culturally, it helped deepen empirically

the role that intelligent systems had in enhancing institutional integrity and decision-making. The research was

in line with the global digital governance agenda and Sustainable Development Goal 16, which advocates for

innovation, ethical governance and data driven accountability (Yang & Al-Masri, 2025).

LITERATURE REVIEW

Systems Theory

Systems Theory focuses on the interdependency among different elements of a system and argues that successful

AI implementation involves integration between technology, human resources, and institutional arrangements.

AI technologies, especially in the area of monitoring and evaluation (M&E), need to be aligned across these

subsystems in order to maximize effective integration. Inconsistencies can cause inefficiencies, like data

miscalculations or wrong decision-making. Organizations with strong technological infrastructures and well-

trained personnel are more likely to effectively adopt AI (Batool, Zowghi, & Bano, 2023). Therefore, it is

essential to take a systems approach when integrating AI technologies with the existing workflows, to ensure

that adoption is seamless and that AI can help improve the overall performance of the system.

Such can be framed into Systems Theory with an emphasis on transparency and feedback loops for accountability

in AI adoption. Accountability not only refers to role clarity but also demands that decisions made by AI entities

can be traced back, accounted for, and governed. Systems Theory points out that for AI to be accountable, the

system needs to incorporate mechanisms that track how it's being used and assess how it's affecting the world.

This involves monitoring for decisions and outcomes of an AI, for tracking any biases, and for holding

accountable entities responsible for any problems that occur (Bartsch, et al., 2025). Accountability mechanisms

in AI systems ensure that the technology is developed and used in an ethical and responsible manner, adhering

to public trust and governance norms.

Performance monitoring in a systems context is simply the act of providing ongoing feedback to assess how well

the system is performing. When AI is used in M&E systems, it can provide real-time tracking and predictive

analytics which can alert us to system inefficiencies and provide insights to improve performance. Systems

Theory: Adaptive feedback mechanisms: Emphasis on systems that allow the adaptation and optimization of the

AI system based on real-time feedback. AI systems need continuous adaptation to ensure they maintain

effectiveness and stay on track to meet the objectives of performance monitoring. Such dynamic feedback loops

to monitor how AI is performing can provide ongoing better decision making and encourage better governance

outcomes.

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 725

www.rsisinternational.org

Empirical Review

International Innovations in M&E Systems

In developed countries, the applications of AI, big data and machine learning in monitoring and evaluation

(M&E) systems have dramatically changed the way projects are monitored and decisions are made. These

technologies will facilitate the analysis of data in real time, prediction of the needs of people, and better decision-

making tools so that governments can react to emerging problems in a timely fashion with a higher level of

efficiency in public sector work projects (Aldemir & Ucma Uysal, 2025; Batool et al., 2025). In the US, for

instance, AI-based M&E software has been used to observe how federal infrastructure projects are progressing;

identifying potential hazards like cost overruns and project delays before they become problematic (Kobis, 2022;

Robles & Rios, 2024). One common example of AI being effectively applied is in the groups of urban

development, where machine learning algorithms are used to analyze huge amounts of construction and traffic

data in order to predict times for projects and anticipated resources required (Yang & Goldsmith, 2025).

Likewise, efforts have been made in the United Kingdom to use big data analytics in public health projects for

tracking disease outbreaks and measuring the performance of interventions in real time. The National Health

Service (NHS) uses machine learning for patient outcomes assessments and service delivery enhancement,

which can make M&E more responsive and adaptive (Misic et al., 2025; Yang & Al-Masri, 2025) These systems

utilise large volumes of health data from electronic records, wearable devices and mobile applications, to provide

actionable information which guides policy changes and better health outcomes.

Yet, despite these gains, scaling these technologies to be more accurate, transparent, and ethical in using data

still poses challenges. AI and big data applications in M&E systems have elicited concerns from northern

countries regarding data privacy and bias in algorithms because the decisions of machine learning models have

the potential to reproduce biases (de Fine Licht & Christensen, 2023). Moreover, the infrastructure requirements

may be a barrier for adoption, as advanced technology may be out of reach in developing regions with limited

infrastructure.

Developing Country Applications

And emerging evidence from developing nations in Africa, Asia, and Latin America is revealing the growing

role of digital governance and adaptive monitoring and evaluation (M&E) systems in driving transparency,

accountability, and effective governance. In Kenya, M-Akiba mobile platform has changed public finance

management by allowing citizens to invest in government bonds, increasing transparency of national fiscal

operations (Karanja et al., 2024). For example, India's Digital India program has greatly enhanced the delivery

of public services through the use of digital tools, reducing corruption and making government services more

accessible to citizens (Sharma et al., 2025).

In Latin America, Brazil has used digital M&E systems to track deforestation, leveraging satellite data to track

conservation policies in real-time. This allows for dynamic changes to environmental legislation, making it

increasingly effective for the efforts of governments to curb deforestation (Lima et al., 2023). Colombia has also

incorporated adaptive M&E tools into its peace-building processes, gathering data from local communities to

adapt interventions and react to changing dynamics of peace (Ramirez et al., 2024). Disruptive technologies can

enhance the flexibility and openness of governance especially in multifaceted and dynamic circumstances such

as conflict resolution.

Despite these potential benefits, a number of challenges prevent digital governance and M&E systems from

scaling up in developing countries. The lack of adequate Internet infrastructure, especially in rural communities,

poses a great challenge. Additionally, in Sub-Saharan Africa, despite the success of mobile money platforms,

there remain many areas with unreliable internet access which limits the full implementation of digital

governance solutions (Munyua et al., 2024). Furthermore, concerns surrounding data privacy and the ethical

application of technology are not just crucial but especially so in parts of the world with weaker regulatory

frameworks; such that misuse of digital tools further entrench inequalities (Karanja et al., 2025).

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 726

www.rsisinternational.org

However, implemetation of digital governance and adaptive M&E systems, which show great potential to

increase transparency and responsiveness in public administration in these regions. As these systems develop,

they are serving as a platform for greater accountability and more adaptive governance.

Context: Nairobi, Kenyan context

M&E systems in Kenya, and Nairobi County specifically, suffer from several challenges, such as lack of capacity

and data gaps. A major problem is the lack of capacity of county governments to establish effective M&E

frameworks. Though the capital of the country, Nairobi County is still faced with shortages of adequate M&E

staffing and training, which negatively impacts proper tracking of development works (Mwende, 2018). This

capacity shortage particularly impacts infrastructure; projects go unmonitored, unevaluated and are executed

with inefficiencies and poor results (Mokua & Mungai, 2022).

There is also an ongoing lack of data, where county departments have been collecting data in a fragmented and

inconsistent way. This makes integration of information for any comprehensive reporting that is crucial to

evaluate the impact of government projects challenging (Otundo, 2024). Key issues surrounding the

infrastructure in the county are the few internet networks in rural areas which makes it difficult to collect and

analyze real-time data (Kenya National Bureau of Statistics, 2021).

Various national initiatives including the Kenya Vision 2030 and the National M&E Policy have identified the

need for enhanced M&E systems at the county level. However, although the policy has met its goals, the

implementation has been inconsistent due to lack of resources and finances (Kenya National Monitoring and

Evaluation Policy, 2022). Moreover, the mobile platform for M-Akiba used by Nairobi County to promote public

finance management has proved potential in enhancing transparency. However, its wider success has been

constrained by issues around staff capacity and technical infrastructure (Karanja et al., 2024).

In spite of these challenges, strides continue to be made to enhance M&E in Nairobi County. For instance, while

the national government has implicitly made efforts in improving the quality of statistical information held by

the Kenya National Bureau of Statistics, major gaps still continue to exist especially in the areas of health and

education, where this data is critical to assessing policy effectiveness (Kenya National Bureau of Statistics,

2021).

These issues underscore the need for greater capacity building and data interoperability at the county level to

help improve transparency and accountability in public administration.

Research Gaps

While digital technologies such as AI are increasingly being integrated into governance and M&E, no prior

research has broadly looked into the insertion of AI into the governance M&E frameworks in developing

countries, especially in meeting the challenges associated with corruption dynamics. Much of the research above

is often around the technical side, the capability of AI systems for their data analysis and predictive potential,

without considering the larger context of governance where AI tools can be employed to fight corruption and

encourage transparency.

In developing countries, such as Kenya and other African countries the use of AI in M&E systems is still under-

examined as is its use to correct governance inefficiencies and corruption. There is a dearth of research on how

AI could be used to detect and mitigate corrupt practices in both public administration and procurement processes

(Karanja et al., 2024). Although the use of AI for detecting anomalies and informing on financial operations has

been successful in developed countries, no systematic studies have been conducted beyond those countries to

understand how well it performs in African government governance systems, where corruption is often endemic.

Furthermore, most of the research around AI adoption in M&E has focused on technical infrastructure and

neglected the issues surrounding data quality and integrity, accountability, and institutional capacity to

effectively integrate such technologies. Furthermore, there is a need for further research that addresses the

questions of AI adoption from a systemic perspective and to understand the effects at the level of governance,

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 727

www.rsisinternational.org

specifically how AI can be leveraged to reduce corruption and enhance accountability in the countries of the

developing world (Munyua et al., 2024).

METHODOLOGY

Research Design

The research design utilized mixed methods research and used both qualitative and quantitative methods. This

design was used as it provided an opportunity to explore in depth the adoption of AI within governance and how

this impacts monitoring and evaluation (M&E) systems. Mixed methods allowed understanding underlying

factors using qualitative interviews, while quantitative data collected using structured questionnaires to quantify

the extent to which there is a link between AI adoption and governance efficiency. Creswell and Plano Clark

(2023) proposed that mixed methods are well-suited for such studies as it enables the researcher to gather various

types of data that include quantitative information and information about the context of the phenomenon under

study. The two approaches combined yielded more nuanced understandings of how AI systems are actually used

in applied governance contexts, particularly in relation to issues related to data synthesis, accountability, and

corruption. Quantitative methods provided statistical analysis of how AI affects governing outcome, while

qualitative interviews provided rich context-specific insight into the barriers and facilitators of AI adoption

(Bryman, 2022). This design was appropriate for capturing not only breadth, but depth, in how AI is being

integrated in governance systems.

Population and Sampling

The target population was made up of government officials, ICT specialists as well as project managers in

government bodies from the various government departments involved in M&E activities. These organizations

were chosen due to their hands-on experience with governance structures and M&E practices, as well as their

potential to share pertinent knowledge on the implementation of AI in governance processes. Stratified sampling

was used to ensure that subgroups (e.g., public officer, ICT expert) were proportionately represented, which is

consistent with the recommendations by Teddlie and Tashakkori (2022), for providing assurance that a range of

perspectives are captured. From the population, a sample size of 100 participants was kept throughout the study

because it was felt that this sample size would give a robust dataset for analysis (Fink, 2023). We calculated this

sample size to be representative of AI adoption across sectors and departments of governance.

Sample Size Table:

Group

Population Size

Sample Size

Sampling Technique

Public Officers

500

Stratified

ICT Experts

150

Stratified

Project Managers

100

Stratified

Total

750

100

Stratified

Data Collection Methods

Mixed methods research was undertaken through structured questionnaires and interviews, document analysis,

and AI data mining. The quantitative data on the impact, effectiveness, and challenges of AI in measuring and

evaluating (M&E) systems were gathered through an analysis of structured questionnaires. This approach is

similar to the approach proposed by Saunders, Lewis and Thornhill (2019) for the collection of standardised data

in a large sample. A sub-set of participants were interviewed, specifically ICT specialists and project managers,

in order to gain qualitative insights into realising AI for governance (Flick, 2020). These interviews added

context and profundity to the survey data. Finally, document analysis was undertaken of official government

reports, policies and M&E guidelines to place the use of AI in governance in context. In addition, AI data mining

was used to monitor real-time project data and see how AI systems influenced decision-making in government.

According to Leech and Onwuegbuzie 2023, triangulating multiple data sources using different tools ensures

validity and reliability of research in mixed methods research.

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 728

www.rsisinternational.org

Data Analysis

The data analysis included a mixture of statistical and computational techniques. Quantitative data in the form

of questionnaires were summarized using descriptive statistics, providing insight about AI usage and the

perceived effectiveness of AI against M&E systems. We performed regression analysis and looked at the effect

of AI adoption on different governance outcomes such as efficiency and transparency in accordance with the

methods proposed by Field (2022). Correlation analysis was used to determine if there were any meaningful

correlations between AI usage and improved governance. With the qualitative data obtained from interviews,

thematic coding was used to extract recurrent themes centered on the challenges of adopting AI and the elements

that support AI adoption. This was accomplished taking inspiration from Braun and Clarke (2023) in thematic

analysis. Similarly, AI predictive modelling was employed to understand the real-time project information on

the ground and identify patterns that can guide decision-making related to governance, thus resonating with the

trend of AI predicting for better decision making (Binns et al., 2024).

Ethical Considerations

The utilitarian aspect of the study was of lesser importance than its ethical considerations. According to ethical

standards set by the American Psychological Association (APA, 2022), informed consent from all participants

was obtained and they understood the characteristics of the study and their participation in the research. Ethical

standards for researching were followed and the data was anonymized and stored securely, preventing any one

person from identifying themselves as the data source (Flick 2023). Data Protection and Privacy: Data protection

practices complied with GDPR standards, ensuring that participants' personal information was safeguarded

against exposure or misuse. Finally, it was stressed that appropriate use of AI must be a core principle throughout

the study. The AI models which were applied were critically examined to avoid bias, and to ensure that the

technologies employed were fair and transparent in line with ethical principles outlined by the OECD 2023

regarding AI.

RESULTS AND FINDINGS

Population Characteristics of Survey Respondents

A total of 100 questionnaires were issued among members of the target groups, out of which 100% of

respondents returned the surveys after completing the questionnaire. The response rate for each group is

indicated in the following table, illustrating full participation of the sample selected.

Response Rate Table:

Category

Questionnaires Distributed

Questionnaires Returned

Response Rate (%)

Public Officers

100%

ICT Experts

100%

Project Managers

100%

Total

100

100%

The high response rate reflects a high level of participation by all subjects and thus the reliability and validity of

the results. This is consistent with earlier research that has found high levels of engagement in studies involving

professionals in the public sector, where the research can be viewed as concerned with the internalisation of new

technologies such as AI during their incorporation into governance (Saunders et al., 2019).

Findings by Objective

New Developments in M&E and Project Tracking

One study concluded that AI-based M&E systems improved project tracking and decision making substantially.

Respondents pointed to the success of predictive analytics and real-time data processing in spotting risks, like

cost overrun and project delay. Technologies like AI, including machine learning algorithms and big data

analytics, emerged as imperative tools in streamlining project efficiency. These findings are evidenced by

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 729

www.rsisinternational.org

previous literature that indicates the potential of AI to enhance governance through automation in both developed

and developing countries in aspects such as project evaluation and monitoring performance (Aldemir & Ucma

Uysal, 2025; Batool et al., 2025).

Table 1: AI-Driven Innovations in M&E and Project Tracking

Innovation Type

Frequency (%)

Respondents Reporting Positive Impact

Predictive Analytics

65%

Real-Time Data Processing

55%

Automated Monitoring

45%

Risk Identification

35%

Infrastructure and Service Delivery

The adoption of AI in the governance allowed for a positive effect on infrastructure delivery and service.

Respondents cited AI applications, such as automatically managing projects, as being part of the improved speed

and accuracy of service delivery, particularly in the urban development and public health sectors. Third, the

capacity of AI to take in vast amounts of data and to optimize the use of resources was seen as a critical aspect

of improving the delivery of infrastructure projects. Moreover, these results corroborate the empirical findings

of AI to overcome infrastructure bottlenecks through the automation of complex tasks and the more efficient

management of public resources (Misic et al., 2025; Yang & Al-Masri, 2025).

Table 2: Impact of AI on Infrastructure and Service Delivery

AI Application

Frequency (%)

Positive Outcomes Reported

Automated Project Management

60%

Resource Allocation Optimization

70%

Infrastructure Monitoring

50%

Human Resources and Institutional Capacity

AI implementation was also associated with better management of human resources and institutional capacity.

AI technologies, respondents said, allowed agencies to better plan their workforce, make better decisions and

streamline their operations. Security: AI-enabled tools also enabled skills development and capacity building by

analyzing training and performance gaps in real-time. This research supports earlier research that suggest the

relevance of AI for improving institutional capacity, especially in public sector organisations, through a data-

driven human resource management and improved operational processes (Bryman, 2022; Teddlie & Tashakkori,

2022).

Table 3: AI Impact on Human Resources and Institutional Capacity

Impact Area

Frequency (%)

Institutional Improvements Noted

Workforce Planning

55%

Enhanced Decision-Making

60%

Operational Streamlining

65%

Knowledge Management and Learning

AI's role in knowledge management and organizational learning was identified as one that would transform

learning. Respondents identified how AI systems were benefiting by helping to acquire, store, and analyze large

amounts of knowledge, and by enabling faster decision-making processes and sharing best practices across

departments. Machine learning algorithms supported continuous learning and the discovery of trends, patterns

and areas for improvement. These conclusions tally with other research highlighting the potential for AI in other

contexts to benefit organizational learning and decision support, especially in governmental sectors, by giving

actionable insight that supports better governance and knowledge sharing environments. (Patton, 2015; Saunders

et al., 2019).

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 730

www.rsisinternational.org

Table 4: AI in Knowledge Management and Learning

Knowledge Management Activity

Frequency (%)

Positive Influence Reported

Knowledge Capture and Storage

68%

Sharing Best Practices

72%

Continuous Learning

65%

Statistical Analysis

Correlation Analysis

Correlation analysis was performed to assess the strength and direction of the correlation between AI uptake in

monitoring and evaluation (M&E) systems and improvement of governance outcomes, efficiency, transparency,

and accountability. The results showed significant positive correlations between AI adoption and the three key

governance outcomes, suggesting that with the integration of AI technologies into M&E systems the key

governance outcomes improved. Specifically, adoption of AI was found to have a high correlation with real-

time monitoring and the prediction of project delays and inefficiencies, which are key for improving project

tracking and accountability for public sector projects. These results are in line with other research indicating that

AI improves transparency of governance systems by generating real-time data analysis and predictive tools

(Aldemir & Ucma Uysal, 2025). The correlation coefficients of 0.79 and above across all variables indicate that

AI adoption plays a significant role in improving the responsiveness of public projects, decision-making and

public sector accountabilities. This positive relationship reflects the importance of AI in effort for M&E systems

to be more effective, timely and able to identify inefficiencies and risks in the governance process, which in turn

enhances public confidence in government transparency.

Table 1: Correlation Matrix

Variable

Efficiency

Transparency

Accountability

AI Adoption (Predictive)

0.82**

0.75**

0.79**

Project Tracking Automation

0.79**

0.77**

0.80**

Real-time Data Processing

0.85**

0.80**

0.83**

Note: p < 0.01 indicates statistically significant correlation.

Model Summary

The model summary provides an overview of the regression model fit, with information on how well AI adoption

predicts governance outcomes. The model produced an R squared value of 0.83 meaning that 83% of the variance

in the outcomes of governance including efficiency, transparency, and accountability could be accounted for by

the adoption of AI in M&E systems. This indicates that there is likely a significant relationship between the

adoption of AI and improvements in these outcomes. The high R2 value reports that AI technologies -

particularly those that make use of predictive analysis and the processing of real-time data - are major players in

improving the effectiveness of public sector governance. The adjusted R2 value of 0.82 further validates that the

model is a good fit to the data, which gives us a good framework to understand the impact of AI on governance.

Additionally, the standard error of the estimate of 0.123 indicates that the predictions made by the regression

model were accurate and there was a relatively low degree of error associated with the model's predictions. These

results add to the results of past studies that showed the use of AI-powered tools improved governance outcomes

by allowing for more efficient and transparent management of government projects (Yang & Goldsmith, 2025).

The summary of the model highlights the predictive power of AI to improve M&E systems for improved

governance.

Table 2: Model Summary

Model

R²

Adjusted R²

Std. Error of the Estimate

0.91

0.83

0.82

0.123

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 731

www.rsisinternational.org

ANOVA (Analysis of Variance)

ANOVAs were employed to determine whether significant differences existed between the governance

outcomes of projects with AI-based M&E systems and that of traditional and non-AI-based methods. The results

of the analysis conducted through the process of the analysis of variance showed a significant difference between

the two groups and therefore showed that there was a measurable positive impact of both the efficiency of

governance outcomes such as Projects, accountability and transparency by the use of AI-based M&E systems.

The F value of 25.78 and p-value of 0.000 indicates that AI adoption in M&E systems is significantly improving

M&E governance compared to traditional systems. These findings complement existing literature that underlines

the potential of AI to enhance decision-making and accountability in public administration (Misic et al., 2025).

The significant F value suggests the variation between the governance outcomes of the AI-driven and non-AI-

driven projects is not because of random chance, but because of the intervention itself. This adds fuel to the

argument that artificial intelligence technologies, through their ability to automate data collection, improve data

reporting speed, and support real-time analysis, strengthen the effectiveness and accountability of governance

systems generally. Furthermore, the results from the ANOVA support the potential for AI tools to revent

inefficiencies in the public administration by providing more accurate and timely data, which would, in turn,

allow for better decision making and more responsive governance.

Table 3: ANOVA Results

Source

Sum of Squares

Mean Square

Sig.

Between Groups

12.68

6.34

25.78

0.000

Within Groups

8.34

0.086

Total

21.02

Coefficients

The coefficients table describes the relative contribution of individual variables to forecasting outcomes with

respect to governance. The high weights for AI adoption, real-time data processing, and automation of project

management suggest that these AI technologies are at the heart of increasing efficiency, transparency, and

accountability in public sector projects. Adoption of AI (predictive) accounted for the most variance in effect

size (beta coefficient = 0.71) in terms of its impact on governance outcomes. Important coefficients were also

found for the processing of data in real time and the automation of project tracking, which can further indicate

that these are important for making the administration more effective and responsive. These results confirm the

hypothesis that the use of AI, especially when embedded in M&E systems, has the potential for more proactive

decision making and can improve governance through more efficient use of resources, better risk management,

and quicker resolution of issues. The large t-values and p-values (<0.01) confirm that these variables are

important for prediction of the model.

Table 4: Coefficients

Variable

Std. Error

Beta

Sig.

Constant

0.45

0.23

1.96

0.05

AI Adoption (Predictive)

0.63

0.12

0.71

5.25

0.000

Real-time Data Processing

0.48

0.11

0.57

4.36

0.000

Project Tracking Automation

0.54

0.09

0.64

6.00

0.000

Summary

The statistical analysis revealed that there were statistically significant relationships between AI adoption and

improvements in governance outcomes such as efficiency, transparency, and accountability. Correlation analysis

showed strong positive links between the adoption of AI in M&E systems and these findings: this suggests that

AI-supported resources, such as predictive analytics and real-time data handling, are largely responsible for

improving governance. The AI was further validated by the regression model showing very high predictive

power with an R2 value of 0.83, which means that AI adoption is able to explain 83% of the variation in

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 732

www.rsisinternational.org

governance performance. ANOVA findings showed significant differences between AI and traditional M&E

systems, reinforcing notions that adoption of AI brings about significant improvement in governance practices.

The coefficients table also revealed that the adoption of artificial intelligence, real-time processing of data from

various sources, and the use of automated tracking for projects were the key drivers of the improvements. These

results highlight the breakthrough potential of AI in governance, by providing data-driven means to dismantle

inefficiencies, impart transparency, and establish accountability in public sector initiatives.

DISCUSSION

Interpretation of Results in Relation to Objectives;

The results of this study tend to strongly support the overall goals of the research project, showing the significant

contribution of AI-based monitoring and evaluation (M&E) systems to increased governance, transparency and

accountability in public project implementation.

Objective 1 aimed to determine the role of AI in supporting innovation and transparency in M&E systems. Our

findings further indicated that AI technologies, such as predictive analytics and processing real-time data, played

an important role in promoting innovation within governance. Respondents emphasized the efficiency and

transparency of AI applications in data collection and analysis, leading to more effective project follow-up. The

correlations found between AI adoption and transparency in general, and specifically in tracking government

spending, lend strongly to this goal. These results corroborate previous studies, which show that AI facilitates

more transparent decision-making by automating and confirming information (Aldemir & Ucma Uysal, 2025).

The second purpose concerned the analysis of institutional and infrastructural factors that influence the use of

AI sources in project monitoring. The report found that there are still infrastructure and capacity constraints, but

mainly in the developing economies such as Kenya. Respondents noted that lack of sufficient technological

infrastructure and skilled human resources was a constraint to effective integration of AI in M&E. These

challenges aligned with Teddlie and Tashakkori's (2022) assertion that robust institutional support and the

alignment of technological infrastructure and governance needs are key for AI adoption. The report highlights

the need to invest in technological infrastructure and institutional capacity to fully unlock the potential of AI in

governance.

The third objective was to assess the impact of AI-enabled M&E systems in reducing corruption and in

improving governance outcomes. However, it was found that AI helped significantly in mitigating corruption

risks by identifying anomalies and reporting suspicious financial transactions in real-time transactions. This is

in line with literature indicating that AI can be used to prevent corruption through automated audit and increased

transparency (Yang & Al-Masri, 2025). Regression analysis indicated a strong correlation between AI adoption

and governance improvement, firming the connection between AI and the ability to improve transparency and

accountability in public sector projects.

These results show that an AI-enabled M&E system not only increases efficiency and transparency, but also

fixes longstanding problems associated with corruption and inefficiencies.

Comparison with Other Research.

The findings of this study are consistent with those of other studies carried out in various countries, regions, and

a sole case study in Uganda, shedding more light on the transformative impact of AI in governance and M&E

systems.

In the case of Uganda and other African countries, the interest in using AI for governance is developing in areas

such as agriculture, public health and financial administration among other areas. However, as per Munyua et al

(2024) these countries have major infrastructure and capacity challenges that must be addressed in order to

facilitate the effective implementation of AI. Transport: Like other countries across Africa, Kenya's transport

sector is constrained by inadequate and unreliable internet access and a paucity of skilled personnel that prevent

AI's systems from scaling up into M&E organizations. This is corroborated by AI adoption research in Uganda,

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 733

www.rsisinternational.org

documenting the potential of solutions to enhance service delivery but being hampered by similar infrastructural

deficiencies.

The results are also in line with research from around the world, including developed economies like the United

States and United Kingdom, where AI in governance is more advanced. In the United States, AI M&E tools are

already used to monitor infrastructure projects and make risk-based predictions, including in terms of cost

overruns and delays (Kobis, 2022). Similarly, the UK's National Health Service (NHS) has successfully used AI

technology to track disease outbreaks and the effectiveness of healthcare interventions in real-time (Misic et al.,

2025). As these international examples illustrate, AI has the potential to transform governance practices by

demystifying M&E systems and making them more efficient and data-informed. Contrary to what might be

thought, the results of the study imply that even in developing countries with infrastructure constraints, AI-based

systems will be a major tool for improving the quality of governance by increasing transparency and improving

proactive management of public projects.

In terms of regional variations, countries in Latin America like Brazil and Colombia have proven successful in

utilizing AI in the oversight of environmental policies and peace-building operations respectively (Ramirez et

al., 2024). Similar to this study's findings, AI-driven M&E systems have been found to improve organizations'

governance outcomes, making them more accountable and less inefficient in combatting corruption in regions

like Sudan, Burkina Faso, and Ethiopia. In comparison, AI in African countries such as Kenya has yet to

overcome the challenges of scaling up such technologies, mainly due to the pace of infrastructural development

and policy development which is rather slow.

Despite these challenges, there is a notable convergence across the world towards the potential of AI to augment

governance and M&E systems when appropriate infrastructure and institutional capacity is present. While the

adoption rate might be lower in developing countries, this study's findings affirm that AI can still take a leading

role in making public project management more transparent, reducing corruption, and more efficient.

Implications for Governance and Project Management

The results of this research have great implications for governance and project management, especially in the

case of increasing transparency, accountability, and efficiency with M&E systems powered by AI. There are

numerous revolutionary benefits of using AI in the governance framework.

First, AI can have a huge positive effect in helping to shape policy and strengthen institutions. By automating

the data collection and analysis process, AI systems can help to create transparent, hard-to-manipulate records

that can help to build public trust in government actions. This last one is especially important in a world where

corruption and inefficiency have destroyed public trust. By enabling real-time monitoring of public funds and

projects, AI systems can provide various mechanisms to policymakers, helping them make informed decisions

for promoting integrity in governance. The results indicate that the use of AI can provide a pre-emptive strategy

to corruption as AI can constantly observe the projects for irregularities and therefore replace reactive audit with

proactive management of a project (Bahrad and Nguyen 2025).

Second, AI has a profound role in service delivery. AI can also automate service delivery tracking in fields like

healthcare, education, and infrastructure by helping to track and manage projects and resource scheduling. This

is especially true for governments, who are working towards achieving the Sustainable Development Goals

(SDGs) as efficiency in delivering services is critical for achieving SDG outcomes such as improved health and

education and infrastructure improvements. The results of the study support the previous studies which indicate

a potential of AI in improving public sector operation and facilitate timely, cost-effective, and transparent service

delivery.

Lastly, the study also emphasizes the role of AI in boosting the decision-making process in project management.

Resource Management: AI-powered tools like predictive analytics and real-time monitoring can assist project

managers in pinpointing and resolving inefficiencies before they become significant, resulting in better resource

allocation, cost management, and on-time project delivery. By refining these areas of project management, AI-

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 734

www.rsisinternational.org

driven M&E systems can help governments ensure that their public projects are being finished within budget

while providing the benefits they're intended to deliver.

Theoretical Contribution

The results of this study contribute to Systems Theory by showing how an AI-based M&E system can help the

improvement of the efficiency of the public administration system through the integration of technology and

human resources with institutional systems. Systems Theory centers upon the notion of interconnectedness of

system elements, and this study furthers the notion that AI integration into government necessitates alignment

factors across the technological, organizational, and human elements. The research results indicate that AI is

most effective if it blends with the existing institutional framework, so that it can be embedded in the public

governance workflow processes.

Additionally, the research contributes to the theory as it showcases the importance of feedback loops in AI

systems. Systems Theory sends a message that the mechanism of feedback is essential to optimise system

performance and the findings from this study supports this perspective. The AI tools deployed in M&E systems

enabled continuous feedback on project performance and adaptive management practices. This iterative

methodology allowed decision makers to adjust project plans quickly enough to guarantee the punctual

completion of taxpayer-funded projects. By integrating these real-time feedback loops, AI-powered M&E

systems can advance accountability and transparency in governance, a key component of well-functioning

institutions as defined by SDG 16.

Finally, the results not only provide support for Systems Theory, but also extend it by showing how AI can act

as vibrant feedback system of the governance systems while ultimately enhancing decision-making and

performance in public administration.

CONCLUSION AND SUGGESTIONS

Key Findings

This paper offers insights into the transformative capacity of artificial intelligence (AI)-supported monitoring

and evaluation (M&E) systems for public governance. Our main findings are that public sector project

implementation becomes significantly more transparent, accountable and more efficient through AI application.

AI technologies like predictive analytics and real-time data processing equip public authorities with a reserve to

transition from reactive to proactive management, detecting risks and inefficiencies at an early stage in a project's

lifecycle. The regression analysis further validated that implementation of AI is positively associated with an

increase in project performance, and that AI tools play a critical role in improving governance results. Also, the

study indicated that lack of infrastructure, capability and ethical concerns are still major constraints to the

adoption of AI in emerging economies. However, in spite of these challenges, AI technologies were identified

as improving institutional integrity by automating monitoring and developing the ability to detect anomalies,

thus reducing risks of corruption. The research is consistent with international patterns, showing that although

there may be variation in the speed of adoption of AI in different parts of the world, the potential for AI to help

improve governance is universally understood. These results highlight the need to develop institutional capacity

and technological infrastructure for AI to fulfill its potential in public administration and governance projects.

Policy Recommendations

For AI-based M&E frameworks to be successful, governments should focus on building strong technological

infrastructure and build capacity for public sector institutions. This includes investment in high speed internet,

modernization of data collection tools, and ongoing training initiatives for public officers, ICT experts and

project managers. Further, policy frameworks must facilitate the integration of AI with existing governance

structures while ensuring that the implementation process is inclusive and transparent. Governments should also

prioritize ethical standards and regulatory frameworks to manage issues associated with data privacy and AI

discrimination. Policies need to be constructed to encourage public-private partnerships, providing access to

leading-edge AI technologies while maintaining accountability and transparency in the deployment of AI tools.

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 735

www.rsisinternational.org

Finally, there needs to be a greater effort to involve stakeholders to ensure AI tools are configured to address the

public sector project needs and contexts.

Practical Recommendations

Recommendation: For project managers, M&E officers, and ICT departments, this study recommends investing

in comprehending the adoption and integration of AI technologies that are most suitable for existing project

management workflows. Project managers need to prioritize real-time data collection and invest in AI tools that

can help with predictive analytics to anticipate potential project delays and risks. Moreover, M&E officers need

to undergo training in AI to become adept in using AI systems, so that they effectively interpret the AI-generated

insights and adapt project strategies accordingly. IT departments will need to prioritize providing the technology

infrastructure needed by projects, including making sure to have cybersecurity measures in place to keep

sensitive project data safe. Furthermore, ongoing collaboration between project teams and ICT experts is key to

driving integration and ensuring that AI tools are being customized to fit the specific requirements of each

project. Consistent performance reviews for AI systems will be necessary to gauge their success and help refine

their application to governance and public project management.

Areas for Further Research

AI ethics have become a prominent aspect of public governance and need to be further studied to understand

how AI systems can be used responsibly without perpetuating biases and infringing upon privacy. Similarly,

blockchain-based M&E systems should be considered as a means for improving transparency and accountability

in project monitoring, particularly in the areas of fraud and corruption prevention. Secondly, benchmarking AI-

enabled M&E systems against one another would be beneficial; across the regions and in the various sectors, to

compare their effectiveness, pinpointing best practices and gaps. Scalability is another key area for future

research, investigating the scalability of AI in resource-limited settings while ensuring performance and

reliability.

REFERENCES

1. Mokua, M. A., & Mungai, A. W. (2022). Results-based monitoring and evaluation systems on

performance of CDF funded projects in Nairobi County, Kenya. Social Sciences & Humanities Open, 3,

287–296.

2. Koima, Z. C., & Ombui, K. (2024). Monitoring and evaluation practices and performance of the National

Digital Superhighway Project in Nairobi City County, Kenya. International Journal of Social Sciences

Management and Entrepreneurship, 8(3), 43–54.

3. Otundo, M. R. (2024). Impact of monitoring and evaluation (M&E) capacity on the performance and

sustainability of non-governmental projects in Kenya's coastal region. ResearchGate.

4. Kenya National Bureau of Statistics. (2021). Kenya Strategy for the Development of Statistics 2021–

2030.

5. Kenya National Monitoring and Evaluation Policy. (2022). Ministry of Devolution and Planning.

6. Strengthening Monitoring and Evaluation at the Counties. (2024). Kenya Institute for Public Policy

Research and Analysis (KIPPRA).

7. Baseline Assessment of Monitoring and Evaluation Capacity in Kenyan Counties. (2016). MEASURE

Evaluation.

8. Aldemir, C., & Uçma Uysal, T. (2025). Artificial intelligence for financial accountability and governance

in the public sector: Strategic opportunities and challenges. Administrative Sciences, 15(2), 58.

https://doi.org/10.3390/admsci15020058

9. Aldemir, C., & Uçma Uysal, T. (2025). Artificial intelligence for financial accountability and governance

in the public sector: Strategic opportunities and challenges. Administrative Sciences, 15(2), 58.

https://doi.org/10.3390/admsci15020058

10. Babšek, M., & Špaček, D. (2024). Artificial intelligence adoption in public administration: Opportunities

and challenges for evaluation systems. Central European Public Administration Review, 22(1), 69–88.

https://doi.org/10.17573/cepar.2024.1.04

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 736

www.rsisinternational.org

11. Bahrad, A. S., & Nguyen, L. T. (2025). Local AI governance: Addressing model safety and policy

challenges. Digital Society, 3(2), 89–103. https://doi.org/10.3390/digsoc302001

12. Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and

Ethics, 5, 3265–3279. https://doi.org/10.1007/s43681-024-00653-w

13. Beauchamp, E. (2024). Toolkit for monitoring, evaluation, and learning for national adaptation plan

processes. NAP Global Network.

14. https://napglobalnetwork.org/wp-content/uploads/2024/05/napgn-en-2024-mel-toolkit-nap-

processes.pdf

15. Bravo, S., et al. (2024). Toward enhanced climate ambition: Transparency and digital governance in

Latin America and the Caribbean. Inter-American Development Bank.

16. https://publications.iadb.org/en/toward-enhanced-climate-ambition-transparency-and-digital-

governance-latin-america-and-caribbean

17. Bryman, A. (2022). Social research methods (6th ed.). Oxford University Press.

https://global.oup.com/academic/product/social-research-methods-9780199575325

18. Choi, H., Kim, S., & Lee, J. (2024). AI-enabled monitoring and evaluation for smart governance: Lessons

from East Asia. International Journal of Public Sector Management, 37(3), 214–231.

https://doi.org/10.1108/IJPSM-05-2023-0134

19. Creswell, J. W., & Plano Clark, V. L. (2023). Designing and conducting mixed methods research (4th

ed.). Sage Publications. https://doi.org/10.4135/9781071802858

20. de Fine Licht, K., & Christensen, T. (2023). Artificial intelligence, accountability, and transparency in

the public sector. Government Information Quarterly, 40(2), 101790.

https://doi.org/10.1016/j.giq.2023.101790

21. Dickinson, H., Sullivan, H., & Mergel, I. (2023). Implementing artificial intelligence in the public sector:

Understanding capacity and accountability. Public Management Review, 25(12), 2271–2290.

https://doi.org/10.1080/14719037.2023.2231950

22. Firman, B. (2024). Digital governance adoption: Exploring drivers, impacts, and challenges. Atlantis

Press.

23. https://www.atlantis-press.com/proceedings/osc-24/126007005

24. Flick, U. (2020). An introduction to qualitative research (6th ed.). Sage Publications.

https://uk.sagepub.com/en-gb/eur/an-introduction-to-qualitative-research/book240576

25. Gomes Rêgo de Almeida, P., Santos, A. C., & Lima, R. C. (2025). Understanding how public

organizations implement AI governance. Government Information Quarterly.

https://www.sciencedirect.com/science/article/abs/pii/S0740624X24000959

26. Goodwin, S. (2025). Revisiting the challenges to monitoring, evaluation, and learning for adaptation: A

conceptual, empirical, and methodological review. ScienceDirect.

27. https://www.sciencedirect.com/science/article/pii/S1462901125002151

28. https://doi.org/10.1016/j.ssaho.2020.100101

29. https://erepository.uonbi.ac.ke/bitstream/handle/11295/104747/Mwende_Strategic%20Planning%20an

d%20Implementation%20Challenges%20in%20the%20County%20Government%20of%20Nairobi%2

C%20Kenya.pdf?sequence=1

30. https://kippra.or.ke/strengthening-monitoring-and-evaluation-at-the-counties/

31. https://twendembele.org/wp-content/uploads/2022/09/KENYA-NATIONAL-ME-POLICY-2022.pdf

32. https://www.knbs.or.ke/

33. https://www.measureevaluation.org/resources/publications/tr-16-145.html

34. https://www.researchgate.net/publication/386566958_IMPACT_OF_MONITORING_AND_EVALUA

TION_ME_CAPACITY_ON_THE_PERFORMANCE_AND_SUSTAINABILITY_OF_NON-

GOVERNMENTAL_PROJECTS_IN_KENYA%27S_COASTAL_REGION

35. https://www.sagepublishers.com/index.php/ijssme/article/view/606

36. Köbis, N. C. (2022). The corruption risks of artificial intelligence: A research agenda. Perspectives on

Politics, 20(4), 1123–1136. https://doi.org/10.1017/S1537592722001226

37. Leech, N. L., & Onwuegbuzie, A. J. (2023). Qualitative data analysis: A step-by-step guide (2nd ed.).

Sage Publications. https://uk.sagepub.com/en-gb/eur/qualitative-data-analysis/book246327

38. Lubis, S. (2024). Electronic governance in advancing sustainable development goals: A systematic

literature review. SpringerLink.

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025

Page 737

www.rsisinternational.org

39. https://link.springer.com/article/10.1007/s44282-024-00102-3

40. Mišić, J., van Est, R., & Kool, L. (2025). Good governance of public sector AI: A combined value

framework for good order and a good society. AI and Ethics. https://doi.org/10.1007/s43681-025-00751-

41. Mwende, C. (2018). Strategic planning and implementation challenges in the County Government of

Nairobi, Kenya. University of Nairobi.

42. Njuguna, L. (2024). Monitoring, evaluation, and learning systems for adaptation. International Institute

for Environment and Development.

43. https://www.iisd.org/system/files/2024-07/monitoring-evaluation-learning-systems-adaptation.pdf

44. Omotoye, A. M. (2025). Integration of digital technologies in anti-corruption initiatives: A public sector

perspective. Journal of Financial Crime, 32(1), 45–61. https://doi.org/10.1108/JFC-11-2023-0254

45. Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage Publications.

https://us.sagepub.com/en-us/nam/qualitative-research-evaluation-methods/book227206

46. Pi, Y. (2023). Algorithmic governance for explainability: A comparative overview of progress and

trends. arXiv preprint arXiv:2303.00651. https://arxiv.org/abs/2303.00651

47. Robles, P., & Ríos, P. C. (2024). Artificial intelligence for integrity: Strengthening transparency in public

project monitoring. Journal of Public Administration and Development, 44(1), 33–49.

https://doi.org/10.1002/pad.2012

48. Roux, D. J., Kingsford, R. T., & Cockburn, J. (2025). Strategic adaptive management for transparency,

accountability, and learning: Insights from more than a quarter century of practice. Environmental

Management, 75(10), 2521–2539.

49. https://link.springer.com/article/10.1007/s00267-025-02272-5

50. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.).

Pearson Education. https://www.pearson.com/store/p/research-methods-for-business-

students/P100000318624

51. Silverman, D. (2020). Qualitative research (5th ed.). Sage Publications.

https://doi.org/10.4135/9781529711442

52. Teddlie, C., & Tashakkori, A. (2022). Mixed methods research: The pragmatic path to the future (2nd

ed.). Sage Publications. https://us.sagepub.com/en-us/nam/mixed-methods-research/book239074

53. United Nations Department of Economic and Social Affairs. (2024). E-Government Survey 2024: Digital

government for sustainable development. United Nations.

54. https://desapublications.un.org/sites/default/files/publications/2024-09/%28Chapter%201%29%20E-

Government%20Survey%202024%201392024.pdf

55. Yang, J., & Goldsmith, S. (2025). AI and the transformation of accountability and discretion in urban

governance. arXiv preprint arXiv:2502.13101. https://arxiv.org/abs/2502.13101

56. Yang, W., & Al-Masri, E. (2025). Participatory AI, public sector AI, and citizen engagement: Design

principles for inclusive governance. arXiv preprint arXiv:2504.21297. https://arxiv.org/abs/2504.21297

57. Yin, R. K. (2023). Case study research and applications: Design and methods (7th ed.). Sage

Publications. https://us.sagepub.com/en-us/nam/case-study-research-and-applications/book247703