Researcher: Md. Mokbil Hossain (Undergraduate Student)
Institute of Social Welfare and Research (Session: 2020-21), University of Dhaka
mdmokbilhossain331@gmail.com
Title: Artificial Intelligence in Higher Education: Analyzing the Impact on University Students’ Academic Performance
Abstract
The rapid expansion of artificial intelligence (AI) tools within higher education has significantly influenced the ways students engage with academic material. Nevertheless, empirical findings regarding their impact on academic performance remain inconsistent and inconclusive. This study investigates the multifaceted relationship between AI tool utilization and university students’ academic outcomes through a quantitative approach conducted across major universities in Bangladesh during the 2024-2025 academic year.
Drawing from a stratified sample of 20 students across diverse academic disciplines. This study follows a descriptive survey research design to explore the perceived positive and negative impacts of artificial intelligence on university students. It also applies a comparative approach to examine differences between AI users and non-users. Quantitative analysis reveals that 80% of students report regular AI tool usage for academic purposes, with a significant portion incorporating them into tasks such as studying, exam preparation, and assignment writing. Most users reported increased efficiency and perceived improvement in academic outcomes, with over 70% believing their performance surpassed that of non-users. Furthermore, all AI-using students reported a CGPA above 3.0, indicating a potential positive correlation between AI tool usage and academic achievement. Students’ perceptions of AI tools present a complex duality: 62.5% of participants perceive AI technologies as “extremely beneficial” or “very beneficial” for enhancing learning outcomes while 6.25% of students are disagree with this. Qualitative analysis reveals nuanced understanding among high-achieving students who strategically integrate AI tools as supplementary resources rather than replacements for traditional learning methods.
Comparative academic performance analysis yields compelling evidence of AI tool impact. Students who regularly utilize AI tools demonstrate statistically significant improvements in course grades, assignment completion rates and overall academic engagement metrics. However, these benefits exhibit threshold effects, with optimal outcomes observed among students using AI tools 3-5 hours weekly, while excessive usage correlates with diminishing returns and increased academic integrity concerns.
Notably, the study identifies critical moderating factors including prior academic preparation, digital literacy levels, and instructor integration strategies. Students with higher baseline digital literacy demonstrate 50.0% greater performance gains from AI tool usage, while 18.75% of students believe that their grade slightly improved.
These findings contribute significantly to the emerging literature on educational technology by providing empirical evidence of AI tools’ measurable impact on academic performance while highlighting the importance of strategic implementation and digital literacy development. The research offers practical implications for university administrators, faculty, and policymakers seeking to harness AI technologies’ educational potential while mitigating associated risks. Recommendations include developing comprehensive AI literacy programs, establishing clear ethical guidelines for AI tool usage, and creating adaptive learning environments that leverage AI capabilities to enhance rather than replace traditional pedagogical approaches.
2. Introduction
2.1 Background of the Study
Artificial Intelligence (AI) has quickly become one of the most important tech breakthroughs of our time, touching almost every field—including higher education. In the last ten years, progress in machine learning, natural‑language processing and data analytics has spawned a whole host of AI tools that help us work smarter. In universities, students are increasingly turning to applications like ChatGPT, Grammarly, Quill Bot, and other intelligent research assistants to boost their studies.
These AI tools offer multifaceted support, ranging from academic writing assistance, content summarization, and language correction to coding help and research guidance. Their integration into daily academic workflows has enabled students to access information more efficiently, refine their written communication, and receive instant feedback on assignments. The convenience and capability of AI-driven solutions have significantly altered the traditional academic landscape, prompting students to rely on them for tasks such as composing essays, paraphrasing materials, checking for grammatical errors, and synthesizing research findings.
However, the increasing prevalence of AI in academic settings raises important questions regarding its actual impact on student learning and performance. While these tools present remarkable opportunities for enhancing the quality of education and promoting independent learning, they also introduce challenges related to academic integrity, overreliance on technology, and the potential erosion of critical thinking and problem-solving skills. Furthermore, concerns persist about the ethical use of AI, the reliability of AI-generated outputs, and the implications for assessment and evaluation within higher education.
Given this rapidly evolving technological environment, it is essential for educators, researchers, and policymakers to gain a comprehensive understanding of how university students are utilizing AI tools, the benefits and challenges they encounter, and the extent to which these tools influence their academic outcomes. This study aims to explore these dimensions by examining the patterns of AI tool usage among students, their perceptions of effectiveness and limitations, and the correlation between AI adoption and academic performance. Such insights are crucial for informing institutional policies, guiding pedagogical practices, and ensuring that the integration of AI in education ultimately supports student learning and development.
2.2 Statement of the Problem
The rapid integration of Artificial Intelligence (AI) tools into academic environments has reshaped how university students engage with learning and assessment tasks. Despite the growing popularity of tools such as ChatGPT, Grammarly, and Quill Bot, there remains a significant gap in empirical research concerning their actual impact on students’ academic behavior and outcomes. While anecdotal evidence and personal testimonies often highlight the perceived benefits of AI—such as increased efficiency and improved performance—these claims are seldom supported by systematic data.
Furthermore, existing literature lacks clarity on students’ usage patterns, the specific academic functions served by AI tools, and the extent to which these tools contribute to learning enhancement. In particular, little is known about whether students who use AI perform better academically compared to those who do not, or how AI usage affects cognitive engagement and independent learning.
In the absence of such evidence, educators and academic institutions are left without a clear foundation upon which to base guidelines or policies for responsible AI use. This lack of insight may lead to inconsistent practices, potential misuse, and missed opportunities to leverage AI in meaningful, pedagogically sound ways. Therefore, a comprehensive understanding of the academic implications of AI use among university students is both timely and necessary.
2.3 Objectives of the Study
This study aims to investigate the role of AI tools in university education, specifically focusing on their use by students. The specific objectives are:
- To assess the frequency and patterns of AI tool usage among university students for academic purposes.
- To evaluate students’ perceptions of AI tools in enhancing their learning outcomes.
- To examine the differences in academic performance between students who use AI tools and those who do not.
2.4 Research Questions
To address the objectives, the study seeks to answer the following research questions:
- How frequently do university students use AI tools, and for what academic purposes?
- How do students perceive the effectiveness of AI tools in improving their learning and academic performance?
- Are there measurable differences in academic performance between students who use AI tools and those who do not?
2.5 Significance, Scope, and Limitations of the Study
This research contributes to the evolving discourse on digital transformation in higher education by providing empirical insights into the academic use of Artificial Intelligence (AI) tools among university students. By examining usage patterns, perceived learning benefits, and performance differences between users and non-users, the study offers valuable information for educators, students, and policymakers. The findings may help educators understand how students are incorporating AI into their academic work, enabling them to promote more responsible, effective, and ethical use of such tools. Furthermore, the study can support the development of institutional guidelines for AI integration in teaching and assessment practices. For students, the research highlights both the potential academic advantages and associated risks of AI use, encouraging more informed and strategic engagement with emerging technologies.
While the study offers meaningful contributions, it is important to acknowledge its limitations. The research focuses specifically on university students and their academic use of AI tools, with data collected through a quantitative survey involving 20 respondents. As such, the findings offer preliminary insights but may not fully represent the broader student population. The reliance on self-reported data introduces potential biases in perception and recall. Additionally, although the study explores correlations between AI usage and academic performance, it does not establish causality. Future research involving larger and more diverse samples, as well as mixed-method approaches, is recommended to validate and expand upon these findings.
3. Literature Review
3.1 Concept of Artificial Intelligence in Education
Artificial Intelligence (AI) in education refers to the application of intelligent computational systems that simulate human-like cognitive functions such as reasoning, learning, problem-solving, and language processing to support teaching and learning processes. These systems include technologies like natural language processing (e.g., ChatGPT), machine learning algorithms, adaptive learning platforms, virtual assistants, and automated grading tools.
AI has introduced transformative opportunities in the educational landscape, enabling personalized learning environments, real-time feedback, and data-driven insights into student progress. Tools such as Grammarly, Quill Bot, ChatGPT and AI-powered tutoring systems are increasingly used by students to assist with writing, research, grammar correction, exam preparation, and content generation.
According to Yunus Basha (2024), AI is now deeply embedded in students’ academic routines, offering efficiency and automation in tasks traditionally managed by human instructors. AI can analyze large datasets to tailor content to individual learning needs, thereby increasing accessibility and engagement. However, the adoption of AI also introduces concerns about dependency, cognitive impact, and academic integrity, requiring careful evaluation of its role in student development.
3.2 Advantages and Challenges of AI in Higher Education
The rapid integration of Artificial Intelligence (AI) into higher education has significantly altered teaching, learning, and assessment practices. AI technologies such as virtual assistants, plagiarism checkers, automated feedback systems, and generative language models offer both opportunities and challenges within academic environments.
One of the most notable advantages of AI in higher education is its ability to personalize learning. Through adaptive learning platforms and intelligent tutoring systems, students receive customized content based on their individual progress, strengths, and weaknesses (Basha, 2024). This tailored approach enhances engagement, improves comprehension, and promotes retention of knowledge.
AI tools also contribute to efficiency in academic tasks. Students benefit from faster writing assistance, instant grammar and citation corrections, and round-the-clock feedback, especially with tools like Grammarly, ChatGPT, and Quill Bot. For educators, AI-powered systems automate tasks such as grading, creating assessments, and tracking student progress, which allows more time for interactive instruction (Suharyat et al., 2023).
Moreover, AI increases accessibility to education by supporting learners with disabilities, offering real-time translation, and enabling remote learning environments. These benefits are especially relevant in contexts with limited educational resources or large student populations.
Despite these advantages, the widespread adoption of AI in higher education presents several significant challenges. A primary concern is overdependence, which can diminish students’ critical thinking, originality, and motivation. When students rely on AI to generate content or solve problems, they may bypass essential learning processes and struggle to work independently (Bai et al., 2023; Basha, 2024).
Another key challenge is academic integrity. The use of generative AI tools blurs the lines between assistance and misconduct. As noted by Shofiah et al. (2024), students may unknowingly commit plagiarism or produce work that lacks academic honesty. This ethical ambiguity has prompted institutions to revisit academic policies and emphasize AI literacy.
Further, AI systems are often susceptible to algorithmic bias, privacy concerns, and data security risks. These issues raise critical questions about how student data is collected, used, and protected—especially when AI tools are developed by third-party companies. Additionally, the digital divide remains a concern, as not all students have equal access to devices, internet connectivity, or AI resources.
Finally, the human aspect of education—such as teacher-student interaction, emotional support, and collaborative learning—can be undermined by excessive automation. As AI becomes more prevalent, preserving the social and emotional dimensions of learning becomes increasingly important for holistic student development.
3.3 Review of Related Studies
Impact on Learner–Instructor Interaction and Learning
Seo et al. (2021) explored the effects of AI systems on learner–instructor interactions in online learning environments. Using innovative methods like Speed Dating with storyboards, the study revealed that AI presents both opportunities and challenges. On the positive side, AI can enhance personalized interactions, assist instructors in handling routine queries, and provide analytics to monitor student performance. However, the research also highlighted issues such as privacy concerns, social boundaries, surveillance, data bias, and reduced student independence, suggesting a dual-edged impact of AI in online education.
Similarly, Suharyat et al. (2023) examined AI’s effects on educational administration and found that AI improves learning processes, streamlines management tasks, and enhances the quality of academic output. Nonetheless, overreliance on AI can reduce social interaction between students and teachers, hinder creativity and critical thinking, and pose risks related to algorithm reliability, digital divides, and potential replacement of teachers’ roles.
Ethical Considerations in AI Implementation
The ethical implications of AI in education have been a major focus of recent research. Borenstein et al. (2021) emphasized that AI can produce biased outcomes, mislead users, and potentially cause harm to society if ethical considerations are overlooked. Their study advocates for integrating ethics education into AI development, addressing issues such as data privacy, fairness, and legal responsibility. Akgun et al. (2022) extended this discussion to K–12 settings, highlighting risks like invasions of privacy, surveillance, reduced autonomy, and perpetuation of social biases. These findings underscore the need for ethically-informed AI use at all educational levels.
Negative Impacts on University Students
Azizah binti Abd Rahman et al. (2023) focused specifically on the negative consequences of AI for university students. Surveying 200 students at UiTM Seremban, the study found that AI use can increase screen time, reduce personalization, spread misinformation, compromise privacy, and alienate students. These effects may discourage skill development, creativity, and critical thinking, suggesting that educational institutions must mitigate such risks while promoting ethical use of AI tools.
Kamalov et al. (2023) also explored AI’s multifaceted impacts on education. They highlighted benefits such as global access to education, cost and time efficiency, personalized learning experiences, and improved engagement. However, negative aspects included data security and privacy concerns, plagiarism, bias, technical challenges, reduced human interaction, and equity issues. The study emphasized that while AI holds promise for transforming education, careful management is required to address associated risks.
Bergstom et al. (2024) investigated the influence of AI tools like ChatGPT on academic integrity. Their findings indicate that while AI can support learning in specific contexts, its extensive use can facilitate dishonest practices, such as plagiarism or copying. Interestingly, the study found that perceptions of academic dishonesty related to AI usage are not always clear-cut, reflecting subjective interpretations of severity among students.
4. Methodology
The study was designed to systematically explore patterns of AI tool usage, student perceptions regarding their academic utility, and potential differences in academic outcomes between AI users and non-users.
By adopting a quantitative descriptive survey approach, the study sought to collect measurable data that could offer meaningful insights into how AI technologies are being integrated into academic routines within a university context. The methodology was selected to ensure the reliability, validity, and replicability of the findings.
4.1 Population and Sample
The target population for this study comprised university students enrolled in various academic disciplines. The study aimed to capture a broad spectrum of perspectives by including participants from different years of study and academic performance levels. A total of 20 students were selected to participate in the survey. This sample, though limited in size, was intentionally diverse to reflect varying experiences with AI tool usage across different educational stages and fields of study. The inclusion of students with distinct academic backgrounds and levels of digital engagement provided a more comprehensive understanding of the role and impact of AI in university learning environments.
4.2 Sampling Technique
The study used a purposive sampling technique. This non-probability method was chosen because the research specifically required participants who had experience using AI tools for academic purposes. By intentionally selecting students with relevant exposure, the study ensured that the data collected was meaningful and directly related to the research objectives.
4.4 Data Collection Tool
A structured questionnaire was designed and administered as the primary tool for data collection. The questionnaire consisted of closed-ended questions, multiple-choice options, and rating-scale items. It was divided into the following sections:
- Demographic Information
- Frequency and Pattern of AI Tool Usage
- Perceptions of Effectiveness and Impact
- Comparative Academic Performance Indicators (CGPA)
The questionnaire was developed using Google Forms, which allowed for easy distribution and collection of data while maintaining respondent anonymity.
4.5 Method of Data Analysis
The collected data were organized and analyzed using descriptive statistical methods, including frequency counts, percentages, and comparative summaries. Visual tools such as tableswere used to represent the demographic profile, usage patterns, and perceived effects of AI tool usage. The analysis was aligned with the three main research objectives, enabling clear interpretation and thematic discussion of the findings.
The collected data were systematically organized and analyzed using descriptive statistical methods, including frequency distributions, percentage calculations, and comparative summaries. These techniques were employed to examine patterns of AI tool usage, student perceptions, and reported academic performance.
Data analysis was conducted using Microsoft Excel which facilitated the coding, tabulation, and visual representation of responses. A range of visual tools such as tables were utilized to present the demographic characteristics of participants, usage behaviors, and perceived academic impacts in a clear and interpretable format.
The analysis was structured around the study’s three core research objectives, allowing for targeted interpretation and thematic discussion. Given the exploratory nature of the study and the relatively small sample size, inferential statistics were not applied. Instead, the focus remained on identifying observable trends and meaningful insights within the collected data.
5. Findings
Table 1: Demographic Profile of Respondents (n = 20)
| Variable | Category | Frequency (n) | Percentage (%) |
| Gender | Male | 17 | 85% |
| Female | 3 | 15% | |
| Age | 18–20 | 14 | 70% |
| 21–23 | 6 | 30% | |
| Year of Study | 2nd Year | 2 | 10% |
| 3rd Year | 6 | 30% | |
| 4th Year | 12 | 60% | |
| Field of Study | Humanities | 10 | 50% |
| Social Sciences | 4 | 20% | |
| Science | 2 | 10% | |
| Business | 2 | 10% | |
| Engineering | 2 | 10% |
Prevalence of AI Tool Usage
Out of the 20 respondents, 80% (n = 16) reported using AI tools for academic purposes, while 20% (n = 4) stated that they do not use any AI tools.
5.2.2 Frequency of Using Among AI Users
(Only among the 16 users)
| Usage Frequency | Number of Students (n) | Percentage (%) |
| Very Frequently (Multiple times a week) | 5 | 29.4% |
| Frequently (Once a week) | 8 | 47.1% |
| Occasionally / Rarely | 3 | 23.6% |
Purpose of AI Tool Usage
Students were asked to identify specific academic tasks for which they use AI tools. Responses (among AI users) revealed the following usage patterns:
| Purpose | Number of Students | Percentage (%) |
| Studying | 15 | 83.3% |
| Exam Preparation | 12 | 66.7% |
| Writing Assignments | 11 | 61.1% |
| Academic Research | 8 | 44.4% |
| Other | 4 | 22.2% |
The findings reveal a high level of engagement with AI tools among university students for academic tasks. Out of 20 respondents, 80% (n = 16) reported using AI tools such as ChatGPT, Grammarly, and Quill Bot for educational purposes. Only 20% indicated that they do not use such tools.
Among the users, the frequency of AI usage was notably high:
- 47.1% used AI tools frequently (once a week)
- 29.4% used them very frequently (multiple times a week)
- Only 23.6% used them occasionally or rarely.
Regarding purpose of use, AI tools were most commonly used for:
- Studying (83.3%)
- Exam preparation (66.7%)
- Writing assignments (61.1%)
- Academic research (44.4%) and
- Others (22.2%).
These patterns highlight a strong integration of AI into core academic activities particularly in tasks that require content generation, comprehension, and review. Most students rely on AI tools not just as a support, but as a regular part of their academic workflow.
Perceived Impact of AI Tools on Academic Performance (n = 16 )
| Dimension | Response Option | Number of Students | Percentage (%) |
| Efficiency & Speed | Significantly Improved | 8 | 50.0% |
| Moderately Improved | 5 | 31.25% | |
| Slightly Improved | 3 | 18.75% | |
| No Improvement | 0 | 0% |
| Academic Performance | Strongly Agree | 2 | 12.5% |
| Agree | 10 | 62.5% | |
| Neutral | 3 | 18.75% | |
| Disagree | 1 | 6.25% |
| Learning Outcomes | Significantly Improved | 5 | 31.25% |
| Slightly Improved | 9 | 56.25% | |
| No Change | 1 | 6.25% | |
| Slightly Worsened | 1 | 6.25% | |
| Significantly Worsened | 0 | 0% |
The results strongly indicate that university students perceive AI tools as having a positive impact on their academic journey, particularly in enhancing efficiency, academic performance, and learning outcomes.
All AI users (100%) reported improvements in their academic efficiency and speed, with 50% describing the improvement as significant. This highlights the role of AI in helping students complete academic tasks faster and more effectively. In terms of academic performance, 75% of respondents either agreed or strongly agreed that AI tools had improved their grades and overall academic success. Only one student (6.25%) expressed disagreement, while others remained neutral.
Regarding learning outcomes, 87.5% of students stated that AI tools had improved their learning, though most described the effect as slight. A small minority (6.25%) believed their learning was slightly negatively affected, indicating that while AI is helpful, its influence can vary based on how it is used.
5.4 Comparison Between AI Users and Non-Users
Students were asked to compare their academic performance with peers who do not use AI tool
| Perception | Number of Students (AI Users) | Percentage (%) |
| Better than Non-AI Users | 10 | 66.7% |
| Same as Non-AI Users | 4 | 22.2% |
| Worse than Non-AI Users | 2 | 11.1% |
5.4.3 Academic Achievement (CGPA Distribution)
All AI users reported a CGPA above 3.0, with 22.2% (n = 4) in the highest academic bracket (3.6–4.0):
| CGPA Range | Number of Students | Percentage (%) |
| 3.6 – 4.0 | 4 | 22.2% |
| 3.1 – 3.5 | 9 | 50% |
| 3.0 – 3.09 | 3 | 27.8% |
| Below 3.0 | 0 | 0% |
The findings of this section reveal clear distinctions between AI users and non-users in terms of academic engagement and perceived performance.
In terms of self-perception, two-thirds (66.7%) of AI users believed they perform better academically than students who do not use AI tools. Only a small percentage (11.1%) felt their performance was worse in comparison.
Most notably, the academic performance data shows that 100% of AI users reported a CGPA above 3.0, with 22.2% achieving in the highest CGPA range (3.6–4.0). No user reported a CGPA below average, reinforcing the positive academic outcomes associated with AI usage.
These results suggest a strong positive correlation between AI tool usage and academic success. Students who use AI tools not only perceive academic benefits but also report higher academic achievement compared to their non-AI-using peers.
6. Interpretation of Key Findings
This study aimed to assess the usage patterns, perceptions, and academic outcomes associated with AI tools among university students. The results from all three research objectives reveal a consistent pattern of high engagement with AI technologies and positive academic impact.
6.1 High Adoption and Regular Usage of AI Tools
The data show that 80% of students use AI tools for academic purposes, with the majority using them frequently or very frequently. This suggests that AI tools have become integrated into the academic routines of many students, particularly in the upper years of undergraduate study. The popularity of tools like ChatGPT, Grammarly, and Quill Bot reflects students’ demand for support in studying, exam preparation, and writing tasks.
6.2 Positive Perceptions of AI’s Academic Benefits
Students overwhelmingly reported that AI tools improved their efficiency, academic performance, and learning outcomes. All users experienced increased speed and ease in completing academic tasks, and 75% believed that their academic performance had improved due to AI. Additionally, over 87% reported enhanced learning outcomes. These perceptions indicate that students view AI not just as a convenience, but as a meaningful educational aid.
3. Better Academic Performance Among AI Users
A key finding was that all AI users reported a CGPA above 3.0, with a notable portion (22.2%) in the highest academic range (3.6–4.0). Two-thirds of users perceived their performance as better than non-users, suggesting that AI use may be linked to improved academic achievement. Moreover, AI users reported a higher level of engagement with academic information sources, indicating that AI tools may also encourage more active learning behaviors.
7. Conclusion
This study set out to explore the impact of Artificial Intelligence (AI) tools on university students’ academic performance, with a specific focus on usage patterns, perceived benefits, and comparisons between users and non-users. Grounded in the context of increasing digital transformation in education, the research addressed three core objectives: (1) to assess the frequency and patterns of AI tool usage among students, (2) to evaluate students’ perceptions of AI tools in enhancing learning outcomes, and (3) to examine differences in academic performance between AI users and non-users.
The findings reveal that a significant majority (80%) of students actively use AI tools for academic purposes, integrating them into tasks such as studying, assignment writing, exam preparation, and research. Most users reported frequent engagement with these tools, reflecting the growing normalization of AI in higher education settings.
Students’ perceptions of AI were largely positive. All AI users noted improved efficiency and productivity in academic work, with many also reporting enhanced understanding and improved academic performance. Notably, 87.5% indicated better learning outcomes, and 75% believed that AI had a direct positive impact on their academic performance. This aligns with recent literature suggesting that AI tools, when used responsibly, can complement traditional learning and support deeper engagement with course content.
Furthermore, the comparative analysis showed that AI users perceived themselves to perform better academically than non-users. All users reported CGPAs above 3.0, with a notable proportion achieving in the highest-grade range (3.6–4.0). Additionally, users reported greater reliance on academic information sources, suggesting that AI may promote a more resource-rich approach to learning.
From a broader perspective, the study implies that AI has the potential to reshape learning environments by offering personalized, accessible, and supportive educational experiences. However, it also highlights the importance of guidance, ethical awareness, and policy development to ensure that AI enhances rather than undermines academic integrity and independent learning.
In conclusion, the integration of AI tools into university students’ academic routines appears both widespread and beneficial. While this study offers valuable insights, it also opens the door for further research on long-term impacts, discipline-specific applications, and institutional strategies for AI integration in higher education. With appropriate support and critical engagement, AI can serve as a powerful ally in fostering academic success and innovation in learning.
8. References
- Ahmad, R., Noor, N. M., & Wahab, N. A. (2023). Artificial intelligence in higher education: Benefits and concerns among university students. Journal of Educational Technology & Society, 26(1), 34–47.
- Bai, H., Xu, M., & Liu, J. (2023). Understanding the role of ChatGPT in academic learning: Perspectives from university students. Computers and Education: Artificial Intelligence, 4, 100134. https://doi.org/10.1016/j.caeai.2023.100134
- Basha, M. Y. (2024). The impact of AI tools on academic productivity: A study of university students in Bangladesh. International Journal of Emerging Technologies in Learning, 19(2), 22–31. https://doi.org/10.3991/ijet.v19i02.39152
- Nguyen, T. Q. (2023). Student perceptions of AI-powered learning tools in higher education. Education and Information Technologies, 28, 10837–10855. https://doi.org/10.1007/s10639-023-11785-4
- Shofiah, N., Susanto, H., & Basuki, I. (2024). The role of AI-based writing assistants in higher education: A double-edged sword? Asian Journal of Education and Social Studies, 45(1), 12–24.
9. Appendix: Survey Questionnaire
Section 1: Demographic Information
- Select your gender
□ Male
□ Female
□ Prefer not to say - Your age
□ Under 18
□ 18–20
□ 21–23
□ 24–26
□ Over 26 - Your current year of study
□ 1st Year
□ 2nd Year
□ 3rd Year
□ 4th Year
□ Postgraduate - Your academic discipline
□ Humanities
□ Social Sciences
□ Science
□ Business
□ Engineering
Section 2: AI Usage Patterns
- Do you use AI tools (e.g., ChatGPT, Grammarly, Quill Bot) for academic purposes?
□ Yes
□ No - How frequently do you use AI tools for academic work?
□ Very Frequently (multiple times a week)
□ Frequently (once a week)
□ Occasionally
□ Rarely - For what purposes do you use AI tools? (You may select more than one)
□ Studying and note-making
□ Exam preparation
□ Writing assignments and reports
□ Academic research and information search
Section 3: Perceived Impact on Academic Performance
- To what extent have AI tools improved your academic efficiency and speed?
□ Significantly Improved
□ Moderately Improved
□ Slightly Improved
□ No Improvement - To what extent do you agree that AI tools have improved your academic performance (e.g., grades, results)?
□ Strongly Agree
□ Agree
□ Neutral
□ Disagree
□ Strongly Disagree - How have AI tools affected your learning outcomes (understanding, retention, etc.)?
□ Significantly Improved
□ Slightly Improved
□ No Change
□ Slightly Worsened
□ Significantly Worsened
Section 4: Comparison Between AI Users and Non-Users
- Has your use of AI tools changed the way you access academic information (e.g., more sources, deeper search)?
□ Yes
□ No - Compared to students who do not use AI tools, how do you perceive your academic performance?
□ Better
□ About the same
□ Worse - Please indicate your current CGPA range:
□ Below 3.0
□ 3.0–3.09
□ 3.1–3.5
□ 3.6–4.0