AI technologies are successfully incorporated into the classroom to help students succeed academically. AI tools offer tailored learning materials to strengthen comprehension of the course content, sharpen critical thinking abilities, and foster problem-solving skills. It can boost student motivation and involvement in the learning process by achieving higher success rates and good academic performance. On the flip side of it, AI’s ever pervasive penetration in academics might affect the quality of learning due to its overemphasis, differences in students’ ability to use it, and ethical issues involved therein. Quite often, students effortlessly resort to AI for problem solving, which raises a serious concern in learning, comprehension and analysis. However, in reality not all of them could possibly do so. The digital split between students in terms of their proficiency may also widen the gap in academic performance. Guidoum & Elkhansa (2024), mentioned that some instructors are worried that overuse of AI could cause students’ critical thinking abilities to decline and increase their reliance on technology rather than their own capacity for analysis and inference [4].

Students use AI to get pre-written replies with little consideration for ethical and moral issues. Besides this, learning takes a dip as students collect answers with no effort to think and learn. Several studies claim that students who excessively depend on AI struggle to learn on their own, which handicaps them in learning, critical analysis and problem solving [5][6]. Universities find it difficult to create specific regulations that manage the use of AI in the classroom in order to get the benefits without compromising the integrity of education. The effect of these technologies on students’ academic performance in Oman universities, particularly in UoN, ought to be investigated as little research is done in this area especially, the moderating variables—academic specialization and academic level—that influence the association between the use of AI and academic performance. A study by Adewale et al. (2024) found that there is a shortage of research on how students benefit from AI technologies differently across self-controls, highlighting the need for a more thorough examination of these tools’ effects in various learning circumstances [7]-[10].

The purpose of this study is to observe the moderating factors, such as academic specialization and academic level, which may impact the relationship between the use of AI technologies and the students’ academic performance in UoN. Furthermore, it offers suggestions for incorporating AI into higher education, which supports efficient learning and increases academic achievement without undermining students’ learning quality and critical thinking abilities.

1) Assess the effect of applying AI technologies on students’ academic achievement.

2) Investigate the correlation between how often artificial intelligence is used and the level of academic success for students.

3) Investigate the impact of academic specialization on students’ benefits from AI technologies.

4) Explain the issues that students face in using AI technologies.

5) Suggest methods for increasing the use of artificial intelligence in the education system to improve academic effectiveness.

1) What are the effects of artificial intelligence technologies on the academic performance of students at the UoN?

2) Is there a positive relationship between the frequent use of AI resources and the improvement of students’ academic achievements?

3) How does educational specialization influence students’ gains from AI technologies?

4) What are the challenges and risks students encounter in utilizing AI technologies in their learning?

5) What recommendations can be made to improve the usage of AI in education to improve learning performance?

This study offers an evidence-based understanding of how AI tools impact students’ academic performance in the context of higher education in Oman, particularly at the University of Nizwa. The essence of this study can be viewed through several dimensions:

Addressing a Local Research Gap

While numerous international studies mention both the benefits and challenges of AI in education, little study has been conducted locally with regard to how the usage of AI tools impacts learning results at universities within Oman. This study contributes to addressing this gap by providing context-specific insights relevant to the digital transformation happening in higher education institutions in Oman.

Improving the Quality of Education and Student Participation

AI tools, including adaptive learning platforms and intelligent tutoring systems, are able to offer individual pathways of learning, continuous feedback, and support students in their cognitive growth. The study shows how AI can customize educational content according to the needs of each student, which contributes to improving understanding of materials and developing critical thinking skills. Students at the UoN use these opportunities to further advance academic performance and strengthen learning engagement. A study by [1] and [2] shows that AI helps create interactive learning environments that enhance students’ interaction with the educational content and support effective self-learning.

Supporting Institutional Decision-Making and Policy Development

The findings assist university leaders, faculty, and policymakers in designing clear guidelines on mixing AI technologies judiciously into curriculum. It involves a trade-off between the benefits of AI and concerns pertaining to academic integrity, ethical use, and over-dependence. A study by revealed that the use of big data extracted from artificial intelligence systems helps educational institutions make evidence-based decisions about developing curricula that are more compatible with students’ needs. The recommendations provided by this research may inform training programs and institutional digitization strategies [4][11]-[14].

Understanding Differences across Academic Specializations

The study emphasizes how academic major and academic level impact the degree to which students benefit from AI tools. Examining these moderating elements will enable educators to consider distinguished instructional support by disciplines and provide equal learning opportunities for all [10].

Enhancing Students’ Skills

By analyzing the relationship between the use of AI and academic achievement, this research contributes to guiding training programs that aim to enhance students’ critical thinking skills and learning. A study by [2] stated that AI technologies can be effective tools for improving student’s analysis and inference skills, especially when these tools are used in an integrated manner with traditional teaching methods.

It thus makes the following contributions: theoretical and practical value through providing a more localized and systematic understanding of how AI tools influence learning performance and empirically grounded recommendations for responsible and effective integration of AI in higher education. This study is a significant step towards a deeper understanding of the impact of current technologies in education, simultaneously, serving as a tool to help the development of teaching tactics that are well-matched with the requirements of the digital age.

The purpose of this research is to analyze the encroachment of AI on students’ academic performance at the UoN, and study the relation between the adoption of these technologies and academic achievement. The goal is to provide practical insights to improve the quality of education and develop teaching strategies that match the requirements of the digital age. Adewale et al. [7] says that artificial intelligence can improve the learning experience by providing personalized content which improves understanding and comprehension, which is reflected in academic performance. Additionally, it evaluates how students benefit from the use of AI tools such as smart assistants, adaptive learning platforms, and automated assessment programs. Hamadneh etal. [12] also indicated that AI can contribute to improving students’ skills through adaptive interaction with educational content.

Population and sample

Students numbering around 115 were randomly selected from different specializations and academic levels at the UoN to participate in this study. The study relies on a randomly selected representative sample to ensure the inclusion of all academic categories.

Variables includedIndependent Variable (IV): AI technologies used in the educational process.Dependent Variable (DV): Students’ academic performance, measured by GPA, test scores, and academic achievement level.Moderating variable: Factors that might impact the connection between AI use and students ‘academic achievement, such as academic specialization, academic level, and level of technology literacy. Jiao et al. mentioned that these factors play a significant role in determining the extent to which students benefit from smart tools.Time frame

The research was conducted during the academic year 2024/2025, for data collection and analysis in order to monitor the impact of using AI over a specific time period.

Geographical framework

The study is limited to the UoN, which clarifies the local context of the research and provides a deep understanding of the challenges and opportunities besieging the educational environment in Oman.

Research methodology

An empirical approach was chosen for the purpose of analyzing the results. Besides this, there were no interviews, no open-ended responses, and no qualitative coding in the study. The questionnaire was earlier distributed electronically to around 125 students. Finally, 115 students responded.

Limitations of the study

The study did not look into the impact of AI techniques on other aspects such as teaching skills development or the educational environment, which the researchers felt would fall outside the scope of students’ academic performance. Yet another limitation of this research is that academic performance was examined through self-reported perceptions instead of official GPA or academic transcripts, which may introduce subjective bias.

Artificial Intelligence (AI) is a term used to describe computer systems or software designed to handle tasks usually requiring human cognitive capacities including problem-solving, decision-making, learning, and natural language processing. Usually it refers to instruments and platforms applied in educational settings (e.g., ChatGPT, AI-based tutoring systems, and learning analytics platforms) and GenAI.Educational Performance: Normally, this is measured in terms of continuous internal and external assessments such as tests, assignments, projects, presentations, etc. Frequency of AI Application: Students’ frequency or number of using AI tools for academic purposes including learning, research, writing, or assignment completion.Interaction Level: How actively and successfully the student uses the technology to improve their learning process in relation to artificial intelligence programs? Academic Specialization: It is the field, or discipline, a student is registered for study. This study investigates variations among students from different majors who use and benefit from artificial intelligence technologies.Technical and ethical issues: Using AI tools involves a range of technical and ethical issues, which might pose challenges if not properly addressed.

Artificial Intelligence (AI), in simple terms is a computer-assisted development that has come to replace skills hitherto done by humans with regard to intelligence, recognizing speech, decision making and identifying patterns [15]. Generative AI or GenAI represents tools that are meant to make teaching process easier by incorporating Natural Language Processing (NLP), ChatGPT, machine learning, etc. GenAI is a type of AI encompassing an array of learning patterns (including music) from massive datasets. It “creates new content, like text or images based on patterns in data” [16][17].

Educational sector is all set to benefit from technological innovations in providing educational content and learning processes. Undoubtedly, AI has brought a sea change in the quality and efficiency of educational practices throughout the world albeit its influence is yet to be seen. The study aims to analyze the impact of AI on students’ academic performance at the UoN and the factors affecting this relationship. Furthermore, this study shall examine some theories that augment the academic performance through AI. Based on [8], students who use AI technologies successfully show an improvement in self-learning, thereby leading to more productivity in the classroom.

“Academic achievement” or “performance” is a student’s accomplishment of school tasks like examinations, projects, and presentations in relation to set standards [5]. It is one of the most significant parameters on which schooling quality is rated. Measuring academic performance has developed from conventional tests to assessment techniques incorporating technology, i.e., intelligent systems capable of providing detailed reports and analysis of students’ progress [1][2]. Any improvement in academic performance results in increased confidence in the education system and also in student/parent satisfaction. Hamadneh et al. contend that overdependence on AI tools could result in lower independent learning and problem-solving ability [12]. Further research is needed on problems including digital access, academic integrity, and ethical concerns in AI-assisted learning.

Education and Artificial Intelligence

Artificial Intelligence (AI) is a range of technological models that can replicate human intellectual abilities in decision-making and information processing. AI utilization in education is diverse, ranging from adaptive learning settings (that adapt learning materials to meet the individual needs of learners) to learning robots that facilitate learner interaction with learning content [18]. AI is utilized in a wide range of educational software, including autonomous learning, instantaneous feedback, and studying students’ learning habits. Adewale et al. said that by providing interactive learning environments befitting students’ needs, artificial intelligence applications have shown promise in enhancing engagement and motivation [7].

Various studies show that AI has a positive impact on students’ learning outcomes. Jiao et al. (2022) showed that AI-based evaluation systems in the context of distance learning enabled higher performance by students in technical fields like engineering, through a system of repeated assessment that modifies educational content according to the learner’s level. Students exploit these evaluation systems, especially in home assignments for quick completion without any safeguards [3].

Garcia-Martinez et al. (2023) indicated that AI led to a greater level of motivation, and increased students’ willingness to learn [1][2]. According to Klimova and Pikhart (2025), AI tools create a healthy rapport between the students and instructors, wherein the instructors come to know the students’ strengths and weaknesses in learning [18]. Shahzad et al. (2024) claimed that interaction with AI technologies makes the students engaged in active learning thereby motivating them to perform better in academics [6].

Moderating factors are essential in establishing the effects of AI tools on students’ academic performance. Its use may directly affect or influence other variables either positively or negatively. The interaction level with the AI tool, the type of academic specialization, and the technical challenges presented to the students are included as variables.

2.4.1. Interaction Level with AI

The effect of AI on students’ academic performance is based on the level of students’ engagement with AI tools. However, there is a widespread belief that students using AI tools very seriously in interactive learning activities perform much better. But a study by [2], posits that low engagement with AI will have a negative effect.

2.4.2. Academic Major

Garcia-Martinez et al. (2023) state that academic specialization substantially affects the usage of AI tools. Technical students fared much better than non-technical students [1][2]. For instance, computer science and engineering students use very sophisticated technological tools that greatly enhance their ability to learn. According to Ben [15], academic specialization influences the readiness of an individual to adopt AI technologies. In fact, students in highly technology-intensive disciplines such as engineering and computer science become familiar with AI tools and make the maximum benefit. However, students in non-technical disciplines find it difficult to use such technologies since their studies require little use.

2.4.3. Technical Issues

Despite the importance of AI tools in improving academic performance, technical issues could possibly undermine the efficacy of such tools. Hamadneh et al. [12] showed that a hard technical infrastructure is necessary for the students to benefit from AI. In virtual learning environments, technical issues like low speed internet, frequent load shedding and inadequate hardware can dampen AI-powered educational technologies. Ben et al. [15] continued that technical difficulties may contain a variety of infrastructure-related problems, including poor network facilities and restricted access to suitable devices causing poor utilization of AI tools [15]. These difficulties may prevent the students from interacting with AI-supported learning resources, thus affecting their performance.

AI has advanced significantly in relation to educational needs. The performance of students is improved by using AI tools; conversely, the benefit of tool’s usage depends on a number of variables such as age, AI experience, academic level, and allocated time for study.

2.5.1. Age Appropriate Use of AI Tools

Age is a core variable that can dictate whether a student will use AI tools. Recent studies show that younger students with a technological background get acclimatized in dealing with AI tools more effortlessly than those from a non-technical environment [8]. Tanveer et al. [19][20] claim that undergraduates below age 25 were more inclined to use AI applications like ChatGPT probably because they are more exposed to modern technology.

2.5.2. The Effect of Prior Experience in Using Technology

Prior experience in using technology is another factor in determining the benefits that students accrue from AI tools. A similar study conducted by Altememy et al. [13], reveals that those students with prior technology experience such as the use of educational software or AI tools were relatively better equipped than those with less technological exposure. Hence, their academic performance is comparatively better. Subsequently, they integrate the technological tools into their day-to-day academic activities to a considerable extent, resulting in better academic outcomes [12][13].

2.5.3. AI Usage and Academic Level Interaction

The academic level is a measurable variable in the students’ response toward the advantages AI tools offer. Hamadneh et al. [12] made it evident that the higher the academic level (graduate studies) is, the more frequently is the usage of AI tools are used as compared to undergraduates. It clarified that advanced students require AI tools to assist them in their complex research and analysis as well as engage AI more extensively in their academic challenge assignments.

2.5.4. The Impact of Study Time

Improved academic performance extensively depends on how much time the students spend with AI. Based on the study by Tanveer et al. [19], there is a correlation between the amount of time students spend studying using these technology tools and their academic performance. Pacheco-Mendoza et al. [21] in their research proposed that any increase in amount of study time, which has been required in adjusting to digital learning spaces, can lead to improved academic performance. Although the number of study hours decreased, students became more efficient in utilizing their time, thanks to the support of artificial intelligence. As Ward et al. [22] noted, the use of AI tools enabled students to manage their study routines more effectively, leading to improved academic outcomes despite reduced study time.

Theories that describe the relationship between Artificial Intelligence and academic performance are as follows.

2.6.1. Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) is a simple theoretical model designed to explain an individual’s behavior towards adopting new technologies. It presumes that two factors significantly influence an individual’s likelihood of adopting the technology: Perceived Usefulness. This refers to an individual’s belief in the degree to which his/her performance is expected to be enhanced by the technology. In an academic setting, this implies the manner in which AI technologies contribute to enhancing learning performance, through customized learning, analysis of learning analytics, and giving feedback in real-time. Perceived Ease of Use: This indicates the degree to which the students perceive using AI technologies as being easy and convenient. If the technology is found to be user-friendly, then it is likely that students will adopt AI technologies in their studies [18]. Based on this theory, students who perceive the usefulness of AI tools, such as intelligent learning systems and automatic evaluation tools, in meeting their needs are more likely to adopt these resources in their pursuit of improved academic performance.

2.6.2. Self-Determination Theory (SDT)

Self-Determination Theory (SDT) is a psychological perspective that focuses on people’s motivation and the functionality of their context in promoting or undermining their experience of autonomy and competence. At school, SDT offers theoretical insight into children’s achievement and motivation by being aware of the way children who believe they have control over what they are doing and employing materials that aid them in skills development perform more academically. It consists of three parts. First is the autonomy wherein the need to feel free to make choices arises. Secondly, it is the competence stressing the need to feel confident in completing a task successfully followed by social connectedness—the need to feel connected and interact with others. AI-powered tools, such as adaptive learning platforms, can be used in education to create individualized learning experiences to meet their academic needs. For example, AI might provide immediate feedback to students so as to gain improvement in certain areas while also increasing their intrinsic motivation in academics [23].

By using Technology Acceptance Theory (TAM), it clarifies how students’ perception of the value of AI technologies may influence their acceptance of their use in classroom setting. However, none can undermine the potential use of Self-Determination Theory (SDT) in relation to students engaging continuously with AI tools enhancing their motivation, autonomy and improving their academic performance.

For want of space, this study limits itself only to handful of theories. For example, Jiao et al. showed that AI-based test systems can enhance students’ academic performance because they can provide immediate feedback, quite similar to SDT, which highlights competence, motivation, and performance concepts [3]. Furthermore, the Technology Acceptance Model (TAM) gives an account of the quick adoption of systems by students, if they believe the systems are useful and easy to use. Technology Acceptance Theory (TAM) and Self-Determination Theory (SDT) both provide the connection between the use of AI technologies and enhanced student academic performance. Both these theories influence student conduct and engagement in AI-based learning environments with a holistic framework established to assess the effect of AI-enabled technologies on student academic performance at the UoN.

Theoretical model of the research clarifies the relationship between the independent variable—Artificial Intelligence (AI) usage—and the dependent variable, i.e., academic performance. Based on previous research, the model expands that AI impacts academic performance through adaptive and personalized learning [2][3]. Furthermore, the model contains moderating variables, which have the potential to change the direction of this relationship. These are:

Academic Specialization: Based on previous studies, technical students have more opportunities to use AI tools since their curriculum is aligned with digital study platforms [13][14].The frequent utilization of AI-supported learning systems is linked to better academic performance [2]. Technical Challenges: Issues like the presence of stable internet connection and proper hardware can hamper or improve the efficiency of artificial intelligence in learning environments [12]. They also synergistically interact within the model to describe the conditions and situations in which the application of AI influences the performance of students at the tertiary education level.

This theoretical framework not only helps in building research hypotheses but also facilitates the choice of appropriate methodologies for collecting and analyzing data. Through analysis of interaction between usage, context, and outcomes, the framework provides a wide range of insight into the strength of artificial intelligence in higher learning, in this case, the University of Nizwa. (Diagram 1)

Diagram 1. Hypotheses, objectives, research questions, and variables.

This study is based on the premise that the integration of artificial intelligence (AI) technologies in higher education environments can positively affect students’ academic performance. Accordingly, the following hypotheses are proposed:

H1: The use of Artificial Intelligence (AI) has a positive impact on the performance of students. Earlier research has shown that artificial intelligence tools can greatly improve the learning outcomes of students through adaptive feedback and individual learning pathways [3][12]. H2: Students utilizing artificial intelligence tools experience less difficulty in understanding and contributing to academic analysis. Artificial intelligence software, including natural language processing and intelligent tutoring software, assists students in comprehending difficult learning content through the decomposition of content and the delivery of explanatory feedback [6]. H3: Academic achievement is influenced in varying ways by AI according to academic specialization. Altememy et al. [13] also found that students in technical fields (e.g., computer science, engineering) showed greater gains in AI use than students in non-technical majors, suggesting academic major moderates AI effectiveness.H4: More regular use of AI tools is positively related to better academic performance. Empirical research suggests that regular use of AI platforms correlates with improved academic performance due to higher levels of engagement and maintained learning continuity [6][19].H5: In spite of the positive effect, students might face some difficulties while using AI. Technical limitations such as availability of internet, device compatibility, or inadequate knowledge of interfaces for AI might hinder the use of AI’s complete potential [11][12]. H6: Suggestions intended to improve the use of AI help in elevating its usefulness in academic outcomes. Customizing artificial intelligence implementation plans based on user reviews and situational requirements improves student receptiveness and learning gains, as reflected in results that are consistent with the Technology Acceptance Model [1][2][24]-[26].

The hypothetical propositions will be empirically examined using data gained from a framed questionnaire distributed to the students of UoN. The data will examine various aspects of the incorporation of AI into the students’ academic performance and its outcome.

This study surveys a quantitative research design since the data were collected using a structured questionnaire and were analyzed through statistical techniques. In this regard, numerical data and statistical evaluations as part of the quantitative approach can be activated to analyze the relationship among the variables objectively and empirically. The data collected were analyzed through descriptive statistics, Pearson correlation analysis, multiple regression analysis and independent samples t-test to objectively examine the relationships among the study variables. In this quantitative approach, hypotheses such as the influence of AI tools on students’ academic performance, academic specialization, and academic level can be tested.

3.4.1. Study Population

The sample comprises 115 undergraduate students at the University of Nizwa, representing a range of academic disciplines and study levels. The heterogeneous group provides a panoramic view in terms of the use of AI across disciplines and students’ level of exposure.

3.4.2. Sampling Technique

Random sampling technique was applied to make it possible for every student to have an equal probability of being selected. This minimizes bias and increases the generalizability of the findings, hence making it possible for the sample to reflect the larger population of students.

3.4.3. Sample Size

115 students took part in the research study cumulatively. Linkert scale was used to interpolate the data collected from the students. This sample is considered adequate for statistical analysis according to standards established by Krejcie & Morgan for social research and is reliable while striking a balance between practicality and statistical efficacy.

3.5.1. Questionnaire Development

The questionnaire was prepared in both Arabic and English and subsequently examined by academic professionals for clarity and reliability. The questionnaire is also segmented into five distinct sections aimed at measuring various classes of variables that form the core of the study.

3.5.2. Layout of the Questionnaire

Section 1: General Information (Gender, Age, Academic Major, Level, etc.)—These are control variables that are used to understand the demographic composition of the sample.Section 2: Utilization of AI Technology—This section evaluates the independent variable, bringing to light the prevalence and kinds of AI tools that students utilize.Section 3: Academic Performance—The dependent variable in question examines students’ perceived academic performance in relation to their use of artificial intelligence.Section 4: Moderating Variables—Discusses factors that may affect the relation between DV and IV as the interaction level, academic specialization, and technical issues encountered by the students.Section 5: Control Variables—The questions are about the duration of AI usage, previous experience with technology, and other elements that may affect the outcome, and are in this section.

3.5.3. Measurement of Variables

It is important to note that academic performance in this study was measured based on students’ self-reported perceptions of their progress rather than official GPA records. Therefore, the performance scores reflect subjective perceptions, which should be considered when interpreting the findings.

Independent Variable (IV) evaluated through the frequency of artificial intelligence utilization, the variety of tools employed, and the dependence level on AI technologies. The assessment employs a Likert scale alongside multiple-choice questions.Dependent Variable (DV): Academic performance, as assessed through students’ perceived improvements in grades, understanding of course material, and effectiveness in completing tasks. Control Variables:Demographic variables like age, level of degree, prior technological experience, and duration of AI use, are measured using categorical and ordinal questions.

It involved two major stages of analysis:

1) Descriptive Statistics: The demographic characteristics of the sample and general trends linked to the AI used are summarized.

2) Inferential Statistics: The following statistical methods were applied in examining the research hypotheses:

Correlation Analysis: studying connecting between AI usage and academic performance.Regression Analysis to ascertain the predictive power of AI usage on academic performance.Independent Samples t-test: to analyze the differences in academic performance across academic specializations.

These tests assist one in assessing the gathered data against each hypothesis to see whether it supports or rejects it.