ASSESSMENT OF THE IMPACT OF ARTIFICIAL INTELLIGENCE TOOLS ON THE ACADEMIC PERFORMANCE OF BUSINESS EDUCATION STUDENTS IN PUBLIC UNIVERSITIES.

TOPIC IS SUITABLE FOR:
1. Department of Computer Science

2. Department of Educational Technology
3. Department of Business Education

4. Department of Educational Management

TOPIC: ASSESSMENT OF THE IMPACT OF ARTIFICIAL INTELLIGENCE TOOLS ON THE ACADEMIC PERFORMANCE OF BUSINESS EDUCATION STUDENTS IN PUBLIC UNIVERSITIES.

TABLE OF CONTENT
Abstract
CHAPTER ONE: INTRODUCTION
1.1 Background of the Study
1.2 Statement of the Problem
1.3 Objective of the Study
1.4 Research Questions
1.5 Research Hypothesis
1.6 Significance of the Study
1.7 Scope of the Study
1.8 Definition of Terms
CHAPTER TWO: LITERATURE REVIEW  
2.1.Conceptual Framework
2.2.Theoretical Framework
2.3.Empirical Review
2.4 Summary of Literature Reviewed
CHAPTER THREE: RESEARCH METHODOLOGY
3.0 Introduction
3.1 Research Design
3.2 Population of the Study
3.3 Sampling Size
3.4 Sampling Technique
3.5 Instrument for Data Collection
3.6 Validity of Instrument
3.7 Reliability of Instrument
3.8 Method of Data Collection
3.9 Method of Data Analysis
3.10 Ethical Consideration
CHAPTER FOUR: DATA PRESENTATION, ANALYSIS AND DISCUSSION OF FINDINGS
4.1 Presentation and Analysis of Demographic Data
4.2 Analysis on Level of Adoption of Artificial Intelligence Tools
4.3 Influence of Artificial Intelligence Tools on Academic Integrity
4.4 Effect of Artificial Intelligence Tools on Skill Acquisition
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
5.2 Summary of the Study
5.3 Conclusion
5.4 Recommendations
5.5 Limitations of the Study
5.6 Suggestions for Further Studies
References
Appendix

ABSTRACT
This study was carried out to assess the impact of artificial intelligence tools on the academic performance of Business Education students in public universities using University of Uyo (UNIUYO) and Akwa Ibom State University (AKSU) as a case study. The study was specifically carried out to examine the level of adoption of artificial intelligence tools such as ChatGPT and Gemini among Business Education students in public universities, assess the influence of artificial intelligence tools on academic integrity among Business Education students in public universities, evaluate the effect of artificial intelligence tool usage on skill acquisition among Business Education students in public universities, and examine the impact of artificial intelligence tools on the academic engagement level of Business Education students in public universities. The study employed the descriptive survey research design. The study population comprised 6,200 Business Education students from UNIUYO and AKSU, while a total of 376 respondents were selected as sample size using the Taro Yamane formula. Out of the 376 questionnaires distributed, 338 were returned and validated for analysis. The questionnaire was used for data collection, while the data collected were analyzed using frequency tables, mean scores, standard deviation, and Pearson correlation analysis with the aid of SPSS version 25. From the responses obtained and analyzed, the findings revealed that artificial intelligence tools are highly adopted among Business Education students with mean scores above 3.00 across all items. The study also showed that artificial intelligence tools have a significant influence on academic integrity at r = 0.52, t = 11.32, P = .000. Likewise, artificial intelligence tools were found to have a significant effect on skill acquisition at r = 0.49, t = 10.48, P = .000, while artificial intelligence tools also showed a significant impact on academic engagement level at r = 0.55, t = 12.41, P = .000. The study concluded that artificial intelligence tools have a significant impact on academic performance of Business Education students in public universities, influencing academic integrity, skill acquisition, and academic engagement at varying degrees, with academic engagement emerging as the most influenced area. The study therefore recommends that Business Education students should be guided on the ethical use of artificial intelligence tools to prevent academic misconduct, universities should integrate AI literacy into academic curricula, institutions should develop clear policies regulating AI usage in academic work, and students should be encouraged to balance artificial intelligence usage with independent learning to enhance critical thinking and skill development.
Keywords: Artificial Intelligence Tools, Academic Performance, ChatGPT, Gemini, Academic Integrity, Skill Acquisition, Academic Engagement.

CHAPTER ONE
INTRODUCTION (Preview)
1.1 Background of the Study
The evolution of knowledge systems in the digital age has been fundamentally shaped by the integration of advanced technologies into learning environments. There has been gradual transition of education to traditional, instructor centered paradigms to more interactive and technologies based education systems, which focus on the aspects of accessibility, flexibility and learner autonomy. Aiyedun (2024) states that digital transformation has allowed students to interact with various sources of information, thus redefining the way knowledge is created and shared. This change is also supported by the fact that the use of information and communication technologies in institutions of higher learning is becoming more and more dominant and has changed the pedagogical process and outcomes of learning (Oluyomi and Adedeji, 2022). With the ongoing growth of technological infrastructures across the world, universities not only adapt to the changes but also integrate new technologies to support independent inquiry and collaborative learning and, therefore, create an environment in which students can better cope with the demanding academic requirements.
Artificial intelligence is an important aspect of this technological change especially in its generative form. Generative artificial intelligence is defined as computational systems that are able to generate human-like text, data and insights by using machine learning algorithms that have been trained on large datasets (Henkel et al., 2024). Examples of this ability include conversational AI platforms, which help users to create explanations, summaries, and analytical material. Ayemoba and Chikwendu (2022) found that personalized learning experiences are improved with the introduction of artificial intelligence in education, as it adjusts to the needs and learning patterns of individual students. Likewise, as Okunade (2024) remarks, they can be used as fast access to information and aid cognitive processes through the provision of immediate feedback. The interaction between students and such systems makes the learning process more dynamic and incorporates human intelligence with a machine generated insight, thus affecting the approach and completion of academic tasks.
In academia, the use of generative AI tools has gained more and more popularity in activities related to research among students of universities. Literature exploration, idea generation, and synthesizing of complex business concepts are all tasks that are undertaken using these tools to allow learners to more effectively handle academic workloads (Edem & Yakubu, 2022). These technological interventions are, therefore, transforming the concept of academic performance, which is the quantifiable results of the learning processes in students, usually in the form of grades and competencies (Somadina and Olabisi, 2024). Ajuluchukwu and Ezeonwuka (2022) state that the incorporation of AI-based tools in scholarly studies promotes critical thinking as it offers multiple insights and coherent replies. Additionally, Ngonso et al. (2025) point out that the engagement of AI systems by students can also affect their cognition, since they usually assist in arranging and interpreting information, thus, contributing to the emergence of intellectual skills in the field of business education.
The application of generative AI tools in public universities, especially in the business education courses, is emerging as a characteristic aspect of the academic experiences of students. Learning Business education oriented towards providing managerial, entrepreneurial, and analytical abilities to learners is becoming more and more intertwined with digital innovation (Owusu et al., 2024). This discipline is often used by students who work with AI tools to resolve case studies, interpret financial data and create research arguments. Aiyedun (2024) notes that the structured character of the subjects related to business, in particular, makes them specifically favorable to the implementation of intelligent systems that could help solve problems and make decisions. Here, the communication between students and generative AI tools is indicative of a larger shift in the scholarly practice whereby technology is not only supportive of the learning process but also influences the cognitive practices that learners assume in academic institutions of higher learning.

1.2 Statement of the Problem
Adoption of digital technologies in the learning process in institutions of higher learning has remained to reshape the way learning is delivered, how it is learned and acquired knowledge. In modern educational setting, students are being exposed more to new tools that enable them to engage in independent inquiry and provide access to various sources of information. Generative artificial intelligence, especially in its generative form, has become an eminent element of this transformation and it possesses more than traditional learning technologies. Aiyedun (2024) argues that the development of digital learning spaces has developed the ability of students to engage with knowledge in more dynamic ways. On the same note, Oluyomi and Adedeji (2022) also note that colleges and universities are increasingly integrating smart systems in the teaching and learning activities, thus influencing the way students learn and acquire the competencies that can support them in their areas of specialization.
Regardless of these developments, an empirical understanding on the specific effects of the application of generative AI tools on the academic achievement of students of business education in open universities is still lacking. Although modern practice suggests that students often use such tools as AI-driven platforms to conduct their research, generate ideas and develop their content, the level to which these tools can lead to any measurable outcomes in terms of critical thinking and problem-solving skills remains relatively unclear. Okunade (2024) states that AI can facilitate thinking but Edem and Yakubu (2022) provide evidence that when students overuse automated products, it can distort their autonomous thinking. The optimal academic environment assumes that the students acquire good analytical and problem-solving skills and technological skills, but the present reality is an enhanced reliance on AI systems without adequate knowledge of what it means to education.
Moreover, the literature does not give specific details on how artificial intelligence is implemented in education, but it provides general information on the subject and lacks specific attention to the results in the field, especially in business education programs of state universities. The discontinuity is in the absence of evidence based on context that looks at the impact of generative AI tools on the development of intellectual skills and overall performance of students in this area. Although some researchers, like Somadina and Olabisi (2024), recognize the dynamic quality of academic performance measures, a more specific research on the impact of AI-based learning practices on these measures is required. The proposed study thus aims to fill this gap by critically evaluating how the use of generative AI instruments affects the research processes of students, their critical thinking and problem-solving skills, and offer data-driven information that would reconcile existing levels of technology use with the required learning outcomes.
1.3 Objective of the Study
The main purpose of this study is to examine the impact of artificial intelligence tools on the academic performance of business education students in Public Universitie. The specific objectives of the study seek to:
1.Examine the level of adoption of artificial intelligence tools such as ChatGPT and Gemini among Business Education students in public universities.
2.Assess the influence of artificial intelligence tools on academic integrity among Business Education students in public universities.
3.Evaluate the effect of artificial intelligence tool usage on skill acquisition among Business Education students in public universities.
4.Determine the impact of artificial intelligence tools on the academic engagement level of Business Education students in public universities.
OTHER PARTS OF CHAPTER ONE INCLUDE:
1.4 Research Questions
1.5 Research Hypotheses
1.6 Significance of the Study
1.7 Scope of the Study.
1.8 Operational Definitions of Terms.

CHAPTER TWO
REVIEW OF LITERATURE (Preview)
This chapter critically examines relevant literature that would assist in explaining the research problem and, furthermore, recognizes the efforts of scholars who had previously contributed immensely to similar research. The chapter intends to deepen the understanding of the study and close the perceived gaps. This chapter, therefore, focuses on the concept of artificial intelligence, the concept of artificial intelligence tools, the use of artificial intelligence tools in education, academic performance, the impact of artificial intelligence tools on the academic performance of Business Education students, and challenges associated with the use of artificial intelligence tools in public universities. The chapter covers the following subheadings:
2.1 Conceptual Framework
2.2 Theoretical Framework
2.3 Empirical Review
2.4 Summary of Literature Review

CHAPTER THREE
RESEARCH METHODOLOGY (Preview)
Research Design: The study adopted a descriptive survey research design.
Population of the Study: The population of the study comprises Business Education students in the University of Uyo (UNIUYO) and Akwa Ibom State University (AKSU), Akwa Ibom State, for the 2025 academic session.
Sample Size Determination: The study adopted the Taro Yamane formula to determine a sample size of 376 participants.
Sampling Technique: The study employed stratified and purposive sampling techniques to select the individual respondents.
Research Instrument: The study utilized a structured questionnaire titled “Impact of Artificial Intelligence Tools on Academic Performance Questionnaire (IAITAPQ)” as the instrument for data collection.
Methods of Data Analysis: The collected data were analyzed using frequencies, percentages, means, and standard deviations to summarize and present the demographic characteristics of the respondents and their responses. Inferential statistics, such as Pearson correlation, were used to test the hypotheses.

CHAPTER FOUR; DATA PRESENTATION, ANALYSIS AND INTERPRETATION
MAJOR FINDINGS (Preview)
The analysis results were presented in tables. Based on the results obtained from the analysis, the following findings are made:

i. The study found that Business Education students in public universities have a high level of adoption of artificial intelligence tools, as evidenced by mean scores ranging from 3.44 to 3.77, which are above the acceptance benchmark of 3.00. The findings indicate that students use artificial intelligence tools for academic assignments, research purposes, examination preparation, and daily academic activities, with the tools being easily accessible to them.

ii. The study found that artificial intelligence tools significantly influence academic integrity among Business Education students in public universities, as evidenced by mean scores ranging from 3.53 to 3.63, which are above the acceptance benchmark of 3.00. The corresponding hypothesis test further showed a correlation coefficient (r) of 0.52, t-value of 11.32, and p-value of 0.000 (p < 0.05), leading to the rejection of the null hypothesis.

iii. The study found that artificial intelligence tools significantly affect skill acquisition among Business Education students in public universities, as evidenced by mean scores ranging from 3.54 to 3.76, which are above the acceptance benchmark of 3.00. The corresponding hypothesis test further showed a correlation coefficient (r) of 0.49, t-value of 10.48, and p-value of 0.000 (p < 0.05), leading to the rejection of the null hypothesis.

iv. The study found that artificial intelligence tools significantly impact the academic engagement level of Business Education students in public universities, as evidenced by mean scores ranging from 3.53 to 3.71, which are above the acceptance benchmark of 3.00. The corresponding hypothesis test further showed a correlation coefficient (r) of 0.55, t-value of 12.41, and p-value of 0.000 (p < 0.05), leading to the rejection of the null hypothesis.

CHAPTER FIVE:  SUMMARY, CONCLUSION AND RECOMMENDATIONS
This chapter covers the following outline:
5.1 Introduction
5.2 Summary of the Study
5.3 Conclusion
5.4 Recommendations
5.5 Limitations of the Study
5.6 Suggestions for Further Studies

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