ASSESSING STUDENTS PERCEPTION OF ARTIFICIAL INTELLIGENCE TUTORING SYSTEM IN SHAPING LEARNING EXPERIENCE IN MATHEMATICS INSTRUCTION IN LAGOS STATE

TOPIC IS SUITABLE FOR:
1-Department of Education
2-Department of Mathematics Education
3-Department of Educational Technology

TOPIC:ASSESSING STUDENTS PERCEPTION OF ARTIFICIAL INTELLIGENCE TUTORING SYSTEM IN SHAPING LEARNING EXPERIENCE IN MATHEMATICS INSTRUCTION IN LAGOS STATE

TABLE OF CONTENT
Abstract
CHAPTER ONE: INTRODUCTION
1.1 Background to the Study
1.2 Statement of the Problem 
1.3 Purpose of the Study 
1.4 Research Questions 
1.5 Research Hypotheses 
1.6 Significance of the Study 
1.7 Scope of the Study 
1.8 Operational Definition of Terms 
CHAPTER TWO: REVIEW OF RELATED LITERATURE 
2.1 Theoretical Framework 
2.2 Empirical Framework 
2.3 Conceptual Studies
2.4 Appraisal of Review Literature
CHAPTER THREE: METHODOLOGY 
3.1 Research Design 
3.2 Population of the Study 
3.3 Sample and Sampling Techniques 
3.4 Research Instrument 
3.5 Validity and Reliability of the Instrument 
3.6 Procedure for Data Collection 
3.7 Data Analysis Techniques
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 Results 
4.2 Discussion of Findings 
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
5.2 Summary
5.3 Conclusion
5.4 Limitations of the Study
5.5 Recommendations
5.6 Suggestions for Further Studies
REFERENCES
APPENDIX  

ABSTRACT
This study was carried out to assess students’ perception of Artificial Intelligence tutoring systems in shaping learning experience in mathematics instruction in Lagos State. The study was specifically carried out to examine students’ awareness of Artificial Intelligence tutoring systems in mathematics instruction in Lagos State, assess students’ perceptions toward the use of AI tutoring systems in enhancing their mathematics learning experience, investigate students’ perceived effectiveness of AI tutoring systems in improving their understanding and performance in mathematics, and identify the challenges students face while using AI tutoring systems in mathematics learning. The study employed the survey descriptive research design. Hence, the questionnaire was used for data collection, while the data collected were analyzed using mean, standard deviation, Chi-square, ANOVA, and logistic regression statistical tools. The population of the study comprised 2,230 mathematics students from some selected secondary schools in Ikeja, Lagos State. A total of 339 respondents were selected as sample size using multi-stage sampling technique combining stratified and convenience sampling, while 324 valid responses were used for analysis. From the responses obtained and analyzed, the findings reveal that students have a high level of awareness of AI tutoring systems at mean = 3.97, standard deviation = 0.94, and positive perceptions toward the use of AI tutoring systems in enhancing their mathematics learning experience at mean = 3.79, standard deviation = 1.01. The results further show that AI tutoring systems significantly improve students’ understanding and performance in mathematics at F = 11.36, P = 0.000 and that students face significant challenges in using AI tutoring systems including limited internet access, high device costs, poor technical skills, lack of teacher guidance, and unstable electricity supply as revealed by logistic regression at p < 0.05. The study therefore recommends that schools should provide stable internet and digital infrastructure, teachers should be trained on AI systems guidance, policymakers should ensure that digital devices are affordable to every student, and AI developers should design user-friendly platforms that provide clear explanations to enhance a student's learning experience.

CHAPTER ONE
INTRODUCTION (Preview)
1.1 Background of the Study
The rapid development of Artificial Intelligence (AI) has dramatically changed the face of education around the world especially in the formation of Intelligent Tutor Systems (ITS) for personalised and adaptive learning experiences. Scholars like Chen et al. (2020) confirm that AI has become key instructional technology that have the ability to promote individualised learning pathways, as well as provide targeted feedback for learners. This evolution is consistent with Kamalov et al. (2023) who suggest that the integration of AI in education has brought a sustainable paradigm shift that can improve the quality and accessibility of education in a variety of contexts. In response to this global movement, there is currently a discussion of the importance of AI-tutoring programmes in increasing the effectiveness of instruction and engagement in learning, with a view to the significance of analysing the students perceptions of these programmes in subject areas such as mathematics.
In line with this rising interest in the global marketplace, the role of AI tutoring systems have come to the fore owing to their potential to offer intelligent support that is similar to that of one-on-one tutoring. According to Yarlagadda (2025) AI systems have expanded the possibilities of personalised learning, where educational materials can be tailored to the profiles of individual learners and build an interactional environment that can lead to greater levels of engagement. Similarly, Lin et al (2023) note that intelligent tutoring systems are increasingly made to be aligned with the principles of sustainable learning through integration of machine learning algorithms which improve the quality of instruction. These scholars perspectives combine to highlight the importance of AI tutoring systems in modern education and may serve as a basis to discuss how students comprehend and work with such systems in learning mathematics.
The widespread use of AI tutoring systems has also been influenced by the role of AI tutoring systems in adaptive learning processes, particularly in science, technology, engineering and mathematics (STEM) education. Villegas-Ch. et al. (2025) state that adaptive ITS platforms offer a real-time personalised real feedback that fosters a better cognitive processing and proficiency of the learner. In line with this, Alkhasawneh (2025) highlights the fact that AI driven mathematics learning applications enable learners, by providing instructions based on their proficiency levels and pace of learning, leading to improved motivation and achievement. These emergent understandings emphasise the instructional value of AI on facilitating learning mathematics and therefore exemplify the need for more sunk inquiry on the awareness of, and interaction with, these technologies for learners.
Furthermore, some studies have found that the student-AI interaction in AI tutoring systems has an effect on student learning to positive student learning experiences. Ackermann et al., (2025) note the anthropomorphic features of AI tutors may make a difference in student enjoyment and academic performance through the implementation of a more relatable instructional interface. Complementing this, Lyu et al found that teachable AI agents with different personality attributes encourage productive student engagement and increase conceptual understanding in mathematics. For this reason, the student perceptions about AI tutoring systems constitute a crucial component in the evaluation of the broader implications of the integration of AI in instructional environments. The emphasis on learner interaction with the AI reinforces the need for understanding the interpretation and experiences of students that occur with their interaction with these interactions.
In addition to encouraging engagement, AI tutoring systems support a form of autonomous learning experience, which may have a positive effect on learner motivation. Dembitska et al. (2024) argue that AI tutors are useful for enhancing the effectiveness of students learning as they provide for active participation and independent problem-solving. This argument is reinforced by Sarfaraj 2025 who states that conversational AI-based tutoring allows for interactive learning processes which play an important role in the comprehension and performance of learners. These are just a couple of the academic contributions that illustrate the role that AI tutoring systems play in shaping individual patterns of independent learning, and thus the need to study the student perceptions about these evolving instructional tools.
As the research continues to advance, scholars are also pressing home the implications of the use of AI tutoring systems in relation to the ways in which they impact the creation of pedagogical innovation. Aslam et al. (2025) claim that the artificial intelligence technologies support the adaptive learning designs that bring a revolution to the traditional approaches of the instructional methods in higher and secondary education. Likewise, Son (2024) argues through the use of ITS models driven by artificial intelligence, the teaching of mathematics can be restructured by changing the conventional approach to teaching and offer more dynamic learning experiences. Such evolving pedagogical considerations therefore transform understanding student perceptions of effectiveness and relevance of AI tutoring systems as a part of modern mathematics instruction as a necessity.
In the Nigerian context, the discourse on the adoption and awareness of AI-based learning systems has been continually growing, which is in tune with educational innovations around the world. Ezurike and Akinsulire (2024) indicate a growing level of exposure of students to AI learning tools in Lagos State as a sign of gradual acceptance and awareness of digital tutoring platforms by students. Similarly, Awofala et al. (2025) reported a progressive shift in the Nigerian educators towards the adoption of AI-driven tools to improve the learning of students in the STEM disciplines. Within Lagos State, the educational district of Maryland is a particularly interesting context for such inquiry because the schools in this locality have shown some integration of digital learning technologies as a result of the urban positioning of these schools as well as access to facilities of ICT tools. The increasing interactions between students in Maryland and AI-enabled learning platforms highlight the appropriateness of this area for use as a case study and provide insight into learner interaction with emerging technological learning tools in mathematics instruction. Therefore, it is necessary to investigate the awareness, perceptions, and feelings of effectiveness and challenges faced by students in using AI tutoring systems in the context of Maryland, Lagos State.

1.2 Statement of the Problem
The increased emergence of Artificial Intelligence tutoring systems in the field of mathematics education globally has not been matched by adequate knowledge about how students perceive and experience these systems in the Nigerian context, particularly in Lagos State. Although there are existing studies showing that AI tutoring systems increase personalization, motivation, and performance (Alkhasawneh, 2025; Villegas-Ch. et al., 2025), there are few empirical studies showing how secondary school students view their interactions with AI-based mathematics tutors. Current trends show that learners in Lagos State with the educational district in Maryland are becoming more exposed to AI tools (Ezurike and Akinsulire, 2024). However, how aware students in Maryland are of these systems, how comfortable they are working with them, or their confidence engaging with AI-supported teaching of mathematics, is not well documented. Ideally, students who interact regularly with AI tutoring platforms should be able to show explicit awareness of their roles, view them as constructive learning partners, and observe their own better understanding and performance as suggested by Lyu et al. (2025) and Ackermann et al. (2025). Yet, existing literature does not clearly show enough light on whether or not learners in Maryland, Lagos State, do share with others around the globe in terms of positive perception and effective usage.
Furthermore, while there are still many international studies focusing on the effectiveness, adaptability, and motivational power of the use of AI tutoring systems (Sarfaraj, 2025; Dembitska et al., 2024) there is still a contextual gap in understanding the specific challenges that students encounter when interacting with AI tutors during mathematics instruction at the local level. Research such as Yarlagadda (2025) and Lin et al. (2023) argue that the success of sustained learning is highly dependent on the student's perception and interaction with the AI systems, however, little is known about how these dynamics occur between students in Lagos State; specifically, in Maryland, where there is a gradual increase in the use of digital learning tools. The ideal situation would be for the students to seamlessly interact with AI tutors, receive adaptive feedback, and gain an improved understanding of mathematical concepts. Yet, the lack of evidence-based information on student awareness, perception, perceived effectiveness, features of participatory visualisation and user-related challenges in the context of Maryland are a significant knowledge gap. This study is therefore aimed at filling these gaps by offering context-specific evidence on student's perceptions of AI tutoring systems in mathematics teaching in the state of Maryland, Lagos state, and thus contribute to the national and global discourse of AI supported learning.

1.3 Purpose of the Study
The objectives of this study are as follows:
i.To examine students’ awareness of Artificial Intelligence tutoring systems in mathematics instruction in Lagos State.
ii.To assess students’ perceptions toward the use of AI tutoring systems in enhancing their mathematics learning experience.
iii.To investigate students’ perceived effectiveness of AI tutoring systems in improving their understanding and performance in mathematics.
Iv.To identify the challenges students face while using AI tutoring systems in mathematics learning
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 Definition of Terms

CHAPTER TWO
REVIEW OF RELATED 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 tutoring systems, students’ perception of artificial intelligence tutoring systems, learning experience in mathematics instruction, the role of artificial intelligence tutoring systems in mathematics instruction, benefits and challenges of artificial intelligence tutoring systems, etc. 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
METHODOLOGY (Preview)
Research Design: The study adopted a descriptive research design.
Population of the Study: The population of the study comprises all mathematics students from selected secondary schools in Ikeja, Lagos State.
Sample Size Determination: The study adopted the Taro Yamane formula to determine a sample size of 339 participants.
Sampling Technique: The study employed a stratified random sampling technique to select the individual respondents from public and private secondary schools.
Research Instrument: The study utilized a structured questionnaire titled “Perception of Artificial Intelligence Tutoring System” 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 Chi-square, ANOVA, and logistic regression, were used to test the hypotheses.

CHAPTER FOUR: RESULTS AND DISCUSSION
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 students in Lagos State have a high level of awareness of Artificial Intelligence tutoring systems for mathematics instruction, as evidenced by mean scores ranging from 3.27 to 3.97, all of which are above the acceptance benchmark of 3.00. The corresponding hypothesis test further showed a chi-square value (X²) of 23.45, degree of freedom of 1, and p-value of 0.000 (p < 0.05), leading to the rejection of the null hypothesis.

ii. The study found that students in Lagos State have a positive perception toward the use of Artificial Intelligence tutoring systems in enhancing their mathematics learning experience, as evidenced by mean scores ranging from 3.47 to 3.79, all of which are above the acceptance benchmark of 3.00. The corresponding hypothesis test further showed a chi-square value (X²) of 32.18, degree of freedom of 1, and p-value of 0.000 (p < 0.05), leading to the rejection of the null hypothesis.

iii. The study found that students in Lagos State perceive Artificial Intelligence tutoring systems as effective in improving their understanding and performance in mathematics, as evidenced by mean scores ranging from 3.50 to 3.75, all of which are above the acceptance benchmark of 3.00. The corresponding hypothesis test further showed an F-value of 11.36 and p-value of 0.000 (p < 0.05), leading to the rejection of the null hypothesis.

iv. The study found that students in Lagos State face significant challenges while using Artificial Intelligence tutoring systems for mathematics learning, as evidenced by mean scores ranging from 3.38 to 3.83, all of which are above the acceptance benchmark of 3.00. The corresponding hypothesis test further showed that limited internet access (p = 0.000), high cost of digital devices (p = 0.001), poor technical skills (p = 0.002), lack of teachers’ guidance (p = 0.003), unstable electricity supply (p = 0.000), and confusing AI explanations (p = 0.007) were all statistically significant (p < 0.05), leading to the rejection of the null hypothesis.

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

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