THE USE OF ARTIFICIAL INTELLIGENCE AND IT'S EFFECTS ON ACADEMIC PERFORMANCE OF UNDERGRADUATES: A STUDY OF AE-FUNAI STUDENTS

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​​​​​​​Background of the study

Artificial intelligence (AI) has become an integral part of many industries, revolutionizing traditional practices and offering innovative solutions to complex problems. In the context of education, AI technologies are increasingly being utilized to enhance teaching and learning processes. These technologies include intelligent tutoring systems, automated grading software, adaptive learning platforms, and virtual teaching assistants, all designed to improve educational outcomes and provide personalized learning experiences (Chen et al., 2020). The use of AI in education dates back to the 1960s with early experiments in computer-assisted instruction. However, significant advancements have occurred in the past two decades due to the development of sophisticated algorithms and increased computational power. Early AI applications, such as the PLATO system, laid the groundwork for more advanced technologies that we see today, such as IBM's Watson Tutor and AI-driven platforms like Coursera and Khan Academy (Luckin et al., 2016). AI applications in higher education are diverse and continually evolving. Intelligent tutoring systems (ITS) like Carnegie Learning's MATHia and Pearson's MyLab provide personalized feedback and adaptive learning paths based on individual student performance (Pane et al., 2014). Automated grading systems, such as Gradescope, streamline the grading process and offer consistent and objective evaluations, freeing up time for instructors to focus on other teaching activities (Jordan, 2019). Moreover, AI-driven tools such as chatbots and virtual teaching assistants enhance student engagement and support. For instance, Georgia State University implemented an AI chatbot named Pounce to assist students with administrative tasks and questions, which resulted in increased student retention rates (Bailey et al., 2018). Research indicates that AI technologies can positively impact academic performance by providing tailored educational experiences that cater to individual learning styles and needs. Personalized learning platforms, which adapt content based on student interactions, have been shown to improve understanding and retention of material (Chen et al., 2020). Furthermore, intelligent tutoring systems can provide immediate feedback and support, helping students to address their weaknesses more effectively (VanLehn, 2011). However, the impact of AI on academic performance is not uniformly positive. Some studies suggest that the effectiveness of AI tools depends on various factors, including the quality of the AI system, the subject matter, and the context in which it is used (Luckin et al., 2016). Additionally, there are concerns about over-reliance on AI, which may lead to decreased critical thinking and problem-solving skills among students (Holstein et al., 2019).  Despite the potential benefits, the integration of AI in education presents several challenges and ethical considerations. One significant concern is the digital divide, where students from underprivileged backgrounds may lack access to AI technologies, exacerbating existing educational inequalities (Jones, 2021). Moreover, issues related to data privacy and security arise as AI systems collect and analyze vast amounts of student data to provide personalized learning experiences (Pardo & Siemens, 2014). There is also the ethical dilemma of replacing human instructors with AI, potentially diminishing the role of educators in the learning process. While AI can provide valuable support, it cannot replicate the empathy, creativity, and critical thinking that human teachers bring to the classroom (Holstein et al., 2019). The future of AI in education holds promise but requires careful consideration of its implementation and impact. Ongoing research and development are essential to enhance the effectiveness and accessibility of AI tools. Policymakers and educational institutions must work together to address the digital divide and ensure that all students benefit equally from AI advancements (Jones, 2021). Moreover, educators must be trained to effectively integrate AI into their teaching practices, leveraging the strengths of AI while maintaining the essential human elements of education. Collaboration between AI developers, educators, and researchers will be crucial to creating AI systems that are ethical, effective, and equitable. Therefore, the researcher sought to assess the use of artificial intelligence and it's effects on academic performance of undergraduates: A study of AE-Funai students.

1.2 Statement of the problem

The rapid advancement of artificial intelligence (AI) technologies has significantly influenced various sectors, including education. As educational institutions increasingly integrate AI tools into their pedagogical practices, understanding their impact on students' academic performance becomes crucial. This research seeks to investigate the effects of AI on the academic performance of undergraduate students. AI technologies, such as intelligent tutoring systems, personalized learning platforms, and automated grading systems, promise to enhance learning experiences by providing customized feedback and support (Chen et al., 2020). However, there is a need for empirical evidence to support these claims and to understand the extent to which these tools contribute to improved academic outcomes (Smith & Anderson, 2019). Moreover, concerns regarding the equitable access to AI tools and their potential to exacerbate existing educational inequalities must be addressed. Some studies suggest that students from underprivileged backgrounds may not benefit equally from AI technologies due to limited access and varying levels of digital literacy (Jones, 2021). Therefore, this research aims to explore not only the overall impact of AI on academic performance but also the differential effects across diverse student populations. Hence, the study assess the use of artificial intelligence and it's effects on academic performance of undergraduates: A study of AE-Funai students.

1.3 Objective of the study

Generaly, the study assess the use of artificial intelligence and it's effects on academic performance of undergraduates: A study of AE-Funai students. The specific objectives is as follows

1. Assess the impact of AI on the academic performance of undergraduate students in AE-Funai.

2. Examine the effectiveness of AI personalized learning platforms in enhancing students' understanding and retention of course material.

3. Analyze the efficiency of AI in in academic support of undergraduate students in AE-Funai.

1.4 Research Questions

The following questions have been prepared for the study

1. What is the effct of AI on the academic performance of undergraduate students in AE-Funai?

2. How effective is AI personalized learning platforms in enhancing students' understanding and retention of course material?

3. How efficient is AI in academic support of undergraduate students in AE-Funai?

1.5 Research hypotheses

The hypotheses have been formulated to further guide the study

H0: The use of artificial intelligence does not have an effects on academic performance of undergraduates students in AE-Funai.

Ha:  The use of artificial intelligence have an effects on academic performance of undergraduates students in AE-Funai.

1.6 Significance of the study

Findings of the study will be significant as AI technologies have the potential to augment the capabilities of educators by automating routine tasks such as grading and providing real-time insights into student performance. This study will explore how these technologies can be leveraged to free up educators' time, allowing them to focus on more impactful teaching activities and provide targeted support to students who need it most.

Findings of the study will also be significant to academia as by identifying the key factors that influence the success of AI integration in educational settings, this research will guide future development and implementation of AI tools. Insights from this study will help developers create more effective and user-friendly AI applications that are tailored to the needs of students and educators

1.7 Scope of the study

The study focus on the use of artificial intelligence and it's effects on academic performance of undergraduates: A study of AE-Funai students. Hence, the study will assess the impact of AI on the academic performance of undergraduate students in AE-Funai, examine the effectiveness of AI personalized learning platforms in enhancing students' understanding and retention of course material and analyze the efficiency of AI in in academic support of undergraduate students in AE-Funai. Therefore, the study is delimited to AE-Funai.

1.8 Limitation of the study

Like in every human endeavour, the researchers encountered slight constraints while carrying out the study. The significant constraint are:

Time: The researcher encountered time constraint as the researcher had to carry out this research along side other academic activities such as attending lectures and other educational activities required of her.

Finance: The researcher incurred more financial expenses in carrying out this study such as typesetting, printing, sourcing for relevant materials, literature, or information and in the data collection process.

Availability of Materials: The researcher encountered challenges in sourcing for literature in this study. The scarcity of literature on the subject due to the nature of the discourse was a limitation to this study.

1.9 Definition of terms

Artificial Intelligence (AI): A branch of computer science that involves the creation of systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding.

Intelligent Tutoring Systems (ITS): Computer systems designed to provide immediate and customized instruction or feedback to learners, without human intervention, using AI to adapt to the needs of individual students.

Automated Grading Systems: Software applications that use algorithms and AI techniques to evaluate and score student assessments automatically, ensuring consistency and objectivity in grading.

Personalized Learning Platforms: Educational technologies that use AI to tailor learning experiences to the individual needs, skills, and interests of each student, often by adapting the content and pace of instruction.

Virtual Teaching Assistants: AI-driven systems that assist educators and students by answering questions, providing instructional support, and managing administrative tasks, thereby enhancing the learning experience.

Digital Divide: The gap between individuals who have access to modern information and communication technology and those who do not, often influenced by socio-economic, geographical, and educational factors.

Adaptive Learning: A method of educational delivery that uses technology to dynamically adjust the learning material based on the learner's performance and engagement, thereby personalizing the learning experience.

Educational Inequality:  Disparities in educational access, resources, and outcomes among different groups of students, often influenced by socio-economic status, race, and other demographic factors.

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