Project – Effect of Artificial Intelligence-Assisted Learning on Students’ Academic Engagement

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Artificial Intelligence (AI) has emerged as one of the most significant technological developments influencing contemporary education. In higher education, AI refers broadly to computer-based systems capable of performing tasks associated with human intelligence, including information processing, prediction, natural language interaction, automated feedback, adaptive instruction and learning support. The application of AI in education has expanded from traditional intelligent tutoring systems and learning analytics to generative AI applications capable of producing explanations, summaries, examples, questions and other learning resources. Crompton and Burke (2023), in their systematic review of AI in higher education, identified assessment and evaluation, prediction, AI assistants, intelligent tutoring systems and student-learning management as major areas of AI application. Similarly, Wang et al. (2024) reported that AI has become an important area of educational research because of its capacity to support teaching, learning, assessment and personalised educational experiences. These developments have consequently created new possibilities for universities to reconsider how students access knowledge, interact with learning materials and participate in academic activities.

Artificial intelligence-assisted learning represents a particularly important dimension of this technological transformation because it places AI tools directly within students’ learning processes. AI-assisted learning may involve intelligent tutoring systems, adaptive learning platforms, automated feedback systems, AI-powered educational applications, conversational agents and generative AI tools such as ChatGPT. These technologies can assist students in understanding difficult concepts, generating examples, obtaining explanations, receiving immediate feedback and organising their learning activities. Zawacki-Richter et al. (2019) observed that AI applications in higher education have considerable potential for supporting student learning, although pedagogical and ethical issues remain important considerations. More recently, Crompton and Burke (2023) established that AI applications in higher education are increasingly being used to provide learning assistance, assessment support and personalised educational experiences. Consequently, AI-assisted learning is increasingly being considered not simply as a technological innovation but as a learning support mechanism capable of influencing how students participate in academic activities.

A major issue in the adoption of AI-assisted learning is its possible influence on students’ academic engagement. Academic engagement describes the extent to which students actively participate in, concentrate on and invest effort in their learning. It encompasses behavioural dimensions such as attending classes, completing assignments and participating in academic activities; emotional dimensions such as interest, enthusiasm and sense of belonging; and cognitive dimensions such as concentration, critical thinking, self-regulation and investment in understanding academic materials. The relationship between AI and engagement is therefore more complex than simply determining whether students use AI tools. Chiu et al. (2023) found that motivation and engagement constituted an important category of student outcomes in the literature on artificial intelligence in education, while Nkomo et al. (2021) emphasised that engagement with digital technologies involves multiple dimensions and should not be reduced to frequency of technology use alone. This suggests that AI-assisted learning may influence students’ academic engagement through the quality, immediacy and personalisation of learning support provided to them.

The emergence of generative AI has further intensified discussions concerning the relationship between AI-assisted learning and student engagement. Generative AI tools can respond to questions, explain academic concepts, generate examples, provide feedback and support students during different stages of academic tasks. Such capabilities may encourage students to interact more frequently with learning materials and seek clarification when they encounter difficulties. However, the effect of generative AI on engagement may depend on how students use the technology. Lo, Hew, and Jong (2024), in a systematic review of 72 empirical studies, found evidence of both engagement and disengagement in ChatGPT-supported learning. Their review reported evidence of behavioural engagement through students’ interaction with ChatGPT, while also identifying concerns relating to academic dishonesty, overreliance and reduced critical thinking. Similarly, UNESCO (2023) argues that generative AI can provide meaningful opportunities for teaching and learning but should be implemented in ways that protect human agency and encourage meaningful learning rather than simply replacing students’ intellectual effort.

AI-assisted learning may also strengthen collaborative and interactive dimensions of academic engagement. AI systems can support students in brainstorming ideas, developing questions, discussing academic concepts and receiving explanations that can subsequently be examined with peers and lecturers. Research on AI-supported collaborative learning demonstrates that AI techniques have been applied to analyse learner behaviour, discourse, emotions and collaborative learning processes. Tan, Lee, and Lee (2022), in a systematic review of AI techniques for collaborative learning, found that AI applications have been used to support learning outcomes as well as social interactions and learning processes among students. Likewise, Wang et al. (2024) identified several AI applications that facilitate personalised learning and interaction within educational environments. Therefore, when properly integrated into academic activities, AI-assisted learning may create additional opportunities for students to become active participants in the learning process rather than passive recipients of information.

Despite these potential benefits, the use of AI in higher education also presents challenges that may affect academic engagement. Students may become overly dependent on AI-generated responses, use AI to complete assignments without developing adequate understanding, or accept inaccurate information without critical evaluation. Questions have also emerged concerning academic integrity, privacy, transparency, equity and the development of students’ independent thinking skills. UNESCO (2023) emphasises that generative AI raises important concerns relating to data privacy, academic integrity, human agency and equitable access. In a systematic review, Lo et al. (2024) similarly identified both positive engagement and disengagement associated with ChatGPT, including concerns about overreliance and reduced critical engagement. These concerns demonstrate that the presence of AI technology does not automatically translate into meaningful academic engagement; rather, the educational value of AI depends on the nature, purpose and manner of students’ interaction with the technology.

The issue is particularly relevant to higher education in Africa and Nigeria, where universities are increasingly exposed to digital technologies while simultaneously dealing with challenges relating to infrastructure, digital competence, access and responsible technology use. A systematic review of AI integration in African higher education by Akinyemi et al. (2024) indicates growing interest in the adoption of AI tools among academics and undergraduate students, while also highlighting the need to consider institutional capacity and the specific educational contexts within which AI is deployed. Adewale et al. (2024), in a systematic review involving researchers from Nigerian institutions, also examined the impact of AI adoption on students’ academic outcomes and identified AI as an emerging component of technology-enhanced learning. Nevertheless, much of the existing literature has focused on general AI adoption, academic performance, perceptions or technology acceptance, while fewer studies have specifically examined how AI-assisted learning relates to the behavioural, emotional and cognitive engagement of students within a particular Nigerian university environment. It is against this background that this study examines the effect of artificial intelligence-assisted learning on students’ academic engagement, with particular reference to the University of Nigeria, Nsukka (UNN).

1.2 Statement of the Problem

The increasing availability of artificial intelligence tools has introduced new opportunities and challenges into university education. Students can now obtain explanations, generate ideas, summarise academic materials, receive instant responses and access personalised learning support through AI-powered applications. While these developments may make learning more accessible and interactive, there is uncertainty concerning whether frequent use of AI actually translates into meaningful academic engagement. Crompton and Burke (2023) observed that AI applications in higher education are expanding rapidly across different functions, but the educational implications of these technologies remain an important area of investigation. Similarly, Lo et al. (2024) found evidence of both engagement and disengagement among students using ChatGPT for learning. This creates a need to examine whether AI-assisted learning encourages students to participate actively in their academic activities or whether some forms of AI use may encourage passive learning and dependence on automated responses.

A second problem concerns the nature and quality of students’ engagement when AI tools are incorporated into learning. Academic engagement is not limited to the completion of academic tasks; it also involves students’ attention, participation, motivation, intellectual effort, interaction and willingness to understand course content. AI may increase students’ interaction with academic information by providing immediate explanations and personalised assistance, but increased interaction with technology does not necessarily mean increased cognitive engagement. Lo et al. (2024) reported evidence of behavioural engagement in ChatGPT-supported learning while also identifying concerns relating to overreliance and reduced critical thinking. Chiu et al. (2023) similarly identified motivation and engagement as important student outcomes in AI education research, indicating that AI can affect students’ learning experiences in different ways. The problem, therefore, is to establish whether AI-assisted learning at UNN contributes to meaningful academic participation, intellectual effort and sustained involvement in learning activities.

Another concern relates to responsible and effective use of AI-assisted learning among university students. The ability of generative AI systems to produce convincing academic responses may make it difficult for students to distinguish between using AI as a learning assistant and using it as a substitute for independent academic work. Excessive dependence on AI may reduce opportunities for students to develop independent reasoning, critical evaluation, writing and problem-solving abilities. UNESCO (2023) has emphasised that educational use of generative AI should protect human agency and promote meaningful learning, while Tan et al. (2022) demonstrate that AI can support learning processes when it is appropriately integrated into collaborative learning environments. Thus, there is a need to understand how students at UNN use AI-assisted learning tools, the extent to which such use supports their engagement, and the challenges that may limit the educational benefits of these technologies.

Finally, there remains a contextual gap concerning the relationship between AI-assisted learning and academic engagement among students of the University of Nigeria, Nsukka. Much of the existing research on AI in higher education has been conducted across broad international contexts, with emphasis on AI adoption, intelligent tutoring systems, generative AI, academic performance, perceptions and technological acceptance. Although studies such as Crompton and Burke (2023), Wang et al. (2024), and Lo et al. (2024) provide important evidence concerning AI and student learning, their findings cannot automatically be assumed to represent the experiences of students in Nigerian universities. Differences in technological infrastructure, digital literacy, institutional policies, academic practices and students’ patterns of AI use may influence the relationship between AI-assisted learning and academic engagement. Therefore, this study seeks to address this gap by examining the effect of artificial intelligence-assisted learning on students’ academic engagement at the University of Nigeria, Nsukka.

1.3 Objectives of the Study

The general objective of this study is to examine the effect of artificial intelligence-assisted learning on students’ academic engagement at the University of Nigeria, Nsukka.

The specific objectives are to:

1. Examine the extent to which students of the University of Nigeria, Nsukka use artificial intelligence-assisted learning tools for academic purposes.
2. Determine the effect of artificial intelligence-assisted learning on students’ behavioural engagement in academic activities at the University of Nigeria, Nsukka.
3. Examine the effect of artificial intelligence-assisted learning on students’ cognitive and emotional engagement with academic activities at the University of Nigeria, Nsukka.
4. Identify the major challenges associated with the use of artificial intelligence-assisted learning and students’ academic engagement at the University of Nigeria, Nsukka.

1.4 Research Questions

The following research questions will guide the study:

1. To what extent do students of the University of Nigeria, Nsukka use artificial intelligence-assisted learning tools for academic purposes?
2. What effect does artificial intelligence-assisted learning have on students’ behavioural engagement in academic activities at the University of Nigeria, Nsukka?
3. What effect does artificial intelligence-assisted learning have on students’ cognitive and emotional engagement with academic activities at the University of Nigeria, Nsukka?
4. What are the major challenges associated with the use of artificial intelligence-assisted learning and students’ academic engagement at the University of Nigeria, Nsukka?

1.5 Research Hypothesis

The following null hypothesis will be tested at 0.05 level of significance:

H₀: Artificial intelligence-assisted learning has no significant effect on students’ academic engagement at the University of Nigeria, Nsukka.

1.6 Significance of the Study

The study will be significant to students because it will provide a better understanding of how AI-assisted learning can be used to support meaningful academic engagement. The findings may help students distinguish between productive academic use of AI and excessive dependence on AI-generated information. It may also encourage students to use AI tools as supplementary learning resources while maintaining independent thinking, critical evaluation and active participation in academic activities.

The study will be useful to lecturers and academic staff at the University of Nigeria, Nsukka. The findings may provide information on how students are incorporating AI into their academic activities and the ways in which lecturers can integrate AI-supported learning activities into teaching. The findings may also assist lecturers in developing learning activities that encourage students to critically evaluate AI-generated information rather than merely reproducing it.

The study will also be relevant to university administrators and educational policymakers. The findings may provide empirical information that can support the development of institutional guidelines on the responsible use of artificial intelligence in teaching, learning and assessment. Such policies may address academic integrity, appropriate use of generative AI, digital literacy, data privacy and responsible student engagement.

The study will benefit educational technology practitioners and curriculum developers by providing evidence concerning the relationship between AI-assisted learning and different dimensions of academic engagement. Such evidence may support the design and implementation of technology-enhanced learning environments that encourage active participation, cognitive investment and meaningful interaction with academic content.

Finally, the study will contribute to the academic literature on artificial intelligence and higher education in Nigeria. It may provide context-specific evidence concerning AI-assisted learning and academic engagement among university students. The study may also serve as a reference for future researchers who wish to investigate artificial intelligence, student engagement, digital learning, academic performance, generative AI and related issues in Nigerian universities.

1.7 Scope of the Study

The study focuses on the effect of artificial intelligence-assisted learning on students’ academic engagement at the University of Nigeria, Nsukka (UNN).

The content scope covers students’ use of AI-assisted learning tools, the extent of AI use for academic purposes, behavioural academic engagement, cognitive academic engagement, emotional academic engagement and challenges associated with AI-assisted learning.

The geographical scope is limited to the University of Nigeria, Nsukka, Enugu State, Nigeria.

The population scope will comprise students of the University of Nigeria, Nsukka who have experience using digital or artificial intelligence-assisted tools for academic purposes.

The study is concerned specifically with AI-assisted learning and academic engagement and does not attempt to examine every possible application of artificial intelligence in university administration, institutional management, staff recruitment or non-academic activities.

1.8 Operational Definition of Terms

Artificial Intelligence (AI): Computer-based technologies designed to perform tasks that normally require aspects of human intelligence, such as learning, reasoning, language processing, prediction and decision-making.

Artificial Intelligence-Assisted Learning: The use of AI-powered technologies and applications to support students’ learning activities through functions such as explanation, tutoring, feedback, information retrieval, content generation, personalisation and academic assistance.

Academic Engagement: The degree to which students actively participate in and invest behavioural, emotional and cognitive effort in their academic activities.

Behavioural Engagement: Students’ observable participation in academic activities, including attending lectures, completing assignments, participating in discussions, studying and carrying out academic tasks.

Cognitive Engagement: The extent to which students invest mental effort in understanding academic materials, solving problems, applying knowledge, thinking critically and regulating their own learning.

Emotional Engagement: Students’ feelings, interest, enthusiasm, motivation, satisfaction and sense of involvement in academic learning activities.

Generative Artificial Intelligence: AI systems capable of generating new content such as text, explanations, summaries, images, code or other outputs in response to user instructions.

AI Learning Tools: Digital applications or systems that use artificial intelligence to provide educational assistance, including intelligent tutoring systems, AI chatbots, adaptive learning applications and generative AI platforms.

Students: Undergraduate learners enrolled at the University of Nigeria, Nsukka who constitute the population of the study.

Effect: The measurable influence or relationship between the use of artificial intelligence-assisted learning and students’ academic engagement.

Project – Effect of Artificial Intelligence-Assisted Learning on Students’ Academic Engagement