From Prompt to Performance: Assessing Artificial Intelligence's Role in Individualized Exam Preparation Among Undergraduate Engineering Students

Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 58 From Prompt to Performance: Assessing Artificial Intelligence’s Role in Individualized Exam Preparation Among Undergraduate Engineering Students Olumati Aruomachi Kingsley Department of Biomedical Engineering, Achievers University Owo, Ondo State, Nigeria [email protected] 08063990522 Akinsuyi Adeola Ebenezer Department of Civil and Environmental Engineering, Achievers University Owo, Ondo State, Nigeria adeolaa[email protected] 07061181577 Adoghe Benedict Olumhense Department of Electrical and Information Technology Engineering, Achievers University Owo, Ondo State, Nigeria [email protected] 09059596188 DOI: https://doi.org/10.5281/zenodo.17805481 Abstract Students in a southwestern Nigerian university use artificial intelligence (AI) tools for individualized exam preparation. Adopting a descriptive cross-sectional design grounded in the UTAUT2 framework, data were collected from 117 students across six disciplines using a structured questionnaire. Findings revealed that 90.6% of respondents use AI for academic purposes, with 72.9% employing it regularly during exam preparation. Students perceived AI as effective in simplifying complex concepts (M=4.25) and boosting confidence (β=0.521, p<0.001), with 34.7% of confidence variance explained by AI use frequency. Biomedical and Computer Engineering students showed higher adoption and confidence levels than others. The study concludes that AI tools enhance individualized exam readiness but require ethical guidelines and context-specific integration for optimal benefits. Keywords: Artificial Intelligence, Engineering, Undergraduate, Education, Examination Preparation 1.0 Introduction Artificial intelligence (AI) has transformed institutions, industries, and education worldwide, with ongoing advancements shaping future applications (Kamalov et al., 2023; Machucho & Ortiz, 2025; UNESCO, 2023; Granjeiro et al., 2025). In education, AI supports tasks such as summarising content, providing feedback, and exam preparation (Kerimbayev et al., 2025; Khlaif et al., 2023). Higher education has shifted from theoretical discussions of AI to its practical use, with students now interacting with intelligent systems that solve problems or integrate complex ideas alongside traditional study methods (Chan, 2023). Unlike earlier tools, many platforms are adaptive and interactive, offering explanations, reorganising material, and predicting weak knowledge areas. This shift raises important questions for academia, particularly for Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 59 students in science and engineering, who face demanding problem-solving requirements (Kaputa et al., 2022). AI tools are increasingly used to create personalised learning experiences without institutional guidelines, covering activities such as lecture summarisation and essay drafting. While beneficial, these practices raise concerns about student autonomy, academic confidence, and actual exam performance as boundaries blur between algorithm-assisted and independent learning (Vieriu & Petrea, 2025). Despite global growth, few studies have explored how Nigerian undergraduates use generative AI tools (Essien et al., 2024; Yakubu et al., 2025). Limited evidence exists for discipline-specific engagement in biomedical, electrical, computer, mechanical, mechatronics, and civil engineering. As AI tools become more accessible via mobile apps and integrated platforms, it is important to understand which tools students use, how often, and with what impact. Rather than debating AI’s existence, scholarship should focus on how it influences fundamental academic practices such as exam preparation (Bauer et al., 2025; Mat Yusoff et al., 2025). In contexts where oversight is minimal, researchers call for scrutiny of purpose, consistency, and quality in student AI use (Benke & Szőke, 2024). This study investigates how engineering students at a private university in southwestern Nigeria adopt AI to prepare for examinations. It focuses on five dimensions: perceived usefulness, frequency and purpose of use, impact on preparedness and confidence, and ethical or cognitive challenges related to dependence on AI. The aim is not only to assess AI tools in isolation but to situate their use within authentic academic routines, particularly during periods of high cognitive demand and time pressure. A secondary objective is to compare usage trends across engineering disciplines to identify shared patterns and unique approaches. By placing students at the centre, the study contributes to understanding how AI is reshaping undergraduate learning and assessment. Research Aim The aim of this study is to assess the role of Artificial Intelligence (AI) in individualized exam preparation among undergraduate Engineering students Research Objectives 1. To identify the frequency and purpose of AI tool usage during exam preparation among engineering students. 2. To evaluate students’ perceptions of how AI tools support individualized learning and study efficiency. 3. To assess the relationship between the use of AI tools and students perceived academic preparedness and confidence before exams. 4. To compare patterns of AI tool usage and perceived effectiveness across departments (Electrical, Computer, Mechanical, Civil, and Biomedical Engineering). Research Questions 1. How frequently do undergraduate engineering students use AI tools during exam preparation, and for what academic purposes? 2. To what extent do students perceive AI tools as effective in supporting individualized learning and personalized exam revision? 3. Is there a relationship between the use of AI tools and students’ perceived confidence and preparedness for examinations? 4. Are there notable differences in AI usage patterns and perceived effectiveness across engineering disciplines such as Electrical, Computer, Mechanical, Civil, and Biomedical Engineering? Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 60 Hypotheses Null Hypothesis (H₀): There is no statistically significant relationship between the use of AI tools and students perceived exam preparedness. Alternative Hypothesis (H₁): There is a statistically significant relationship between the use of AI tools and students perceived exam preparedness. 2.0 Literature Review This literature review synthesizes studies to explore AI’s role in individualized exam preparation by Engineering students, focusing on its effectiveness, student perceptions, challenges, and discipline-specific applications. An Overview of AI from ChatGPT to Present Day A major milestone in artificial intelligence (AI) came with the release of ChatGPT in November 2022. Developed by OpenAI on the GPT architecture, ChatGPT attracted millions of users for its ability to generate natural-sounding text (Mesko, 2023; Mhlanga, 2023). Its successor, GPT-4, launched in March 2023, introduced multimodal capabilities, enabling the processing of text, images, and diagrams while achieving near-human performance on several academic and professional benchmarks (Dempere et al., 2023; OpenAI, 2023a; Martínez, 2024). The success of these models spurred competition. Anthropic introduced Claude in 2023, emphasising ethical safeguards and value alignment (Anthropic, 2023). Google expanded its portfolio with Bard, later rebranded Gemini, which integrated advanced natural language and multimodal features (Imran & Almusharraf, 2024). By 2024, further diversification appeared with xAI’s Grok, designed for scientific discovery and fact-checking (xAI, 2024; Murillo & Weigang, 2025), and Meta’s LLaMA series, which focused on academic efficiency. In parallel, multimodal systems such as DALL·E 3 and Stable Diffusion 3 advanced image generation, while specialised AI emerged for tasks including autonomous systems, diagnostics, and code production (e.g., GitHub Copilot). By mid-2025, the AI landscape encompassed conversational models (ChatGPT, Claude, Gemini, Grok), creative AI for media generation, domain-specific tools for sectors such as healthcare and finance, and reinforcement learning systems for robotics and gaming. Alongside these innovations, ethical and regulatory debates intensified, focusing on bias, transparency, and social impact. Open-source initiatives and accessible APIs, including those from xAI, further democratised AI technology (xAI, 2024). The rapid evolution since ChatGPT’s release underscores the pace of AI-driven innovation and competition. This period reflects not only technological progress but also wider societal transformation as AI becomes embedded in research, education, and professional practice. Overview of AI in Higher Education and Engineering Applications Artificial intelligence (AI) is reshaping higher education by improving student outcomes, streamlining administration, and enabling personalised instruction (Crompton & Burke, 2023). Globally, institutions employ tools such as chatbots, data analytics platforms, and adaptive learning systems to meet diverse learning needs (Chen et al., 2023; Labadze et al., 2023). In Nigeria, adoption has been driven by challenges such as overcrowded classrooms and limited access to quality resources (Essien et al., 2024; Yakubu et al., 2025; Ebede et al., 2023). A review of seventy-four studies from 2008 to 2022 highlighted the use of Google Classroom, Moodle, and data mining for online learning and performance prediction (Yakubu et al., 2025). While these tools enable elearning, test automation, and affordability, widespread use remains constrained by poor electricity supply and unreliable internet (Essien et al., 2024). Globally, AI is increasingly applied beyond technical fields. Between 2018 and 2022, AI-related Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 61 publications in education rose by 43%, reflecting growing interest in personalised learning (Crompton & Burke, 2023). Adaptive systems in STEM subjects have boosted engagement by up to 15% in some cases (Chen et al., 2023). However, fully realising AI’s benefits requires training educators and aligning curricula with new technologies (Crompton & Burke, 2023). In Nigeria, cultural resistance and inadequate resources pose additional barriers. Only about 30% of universities currently integrate AI tools into teaching and learning (Oseghale, 2024). Building on these general applications, engineering education, with its focus on technical skills and problem-solving, provides a strong arena for AI applications. Virtual labs, intelligent tutoring systems (ITS), and generative AI models such as ChatGPT, Gemini, and Copilot have been integrated into computer, electrical, and mechanical engineering education (Bravo & Cruz-Bohorquez, 2024; Makanju et al., 2025). These tools help students master complex concepts by offering personalised assistance. For instance, a June 2025 study in civil and environmental engineering found that students valued AIdriven learning assistants for homework help and conceptual clarification, though trust depended on institutional transparency (Sajja et al., 2025). Adaptive systems personalise content and feedback in real time, significantly improving outcomes across STEM disciplines (Wang et al., 2024; Almasri, 2024; Mustafa et al., 2024). Cognitive tutoring systems, which provide immediate, context-sensitive hints, are particularly effective for stepwise engineering problem-solving (Létourneau et al., 2025; Lin et al., 2023). However, challenges remain, including potential bias, inaccuracies, and overreliance on automation (Criddle & Jack, 2025; Fošner, 2024; Mittal, 2025; Yan et al., 2025). Overall, AI shows strong potential to enrich engineering education, but its success depends on thoughtful design, institutional frameworks, and alignment with pedagogical goals (Adewale et al., 2024). 2. Discipline-Specific Trends in AI for Exam Preparation and Performance Narrowing to discipline-specific trends, particularly in engineering, empirical studies highlight a tension between short-term support and long-term learning. One study involving nearly a thousand high school students explored the effects of generative AI on math exam prep: although practice test scores improved with AI assistance, performance dropped on exams taken without AI. Researchers warned that AI may shield students from the effort needed to internalize problem-solving skills (Adewale et al., 2024; Axios, 2024; Adoghe et al., 2024; Sajja et al., 2025). An international analysis of exam scores across university students found GenAI tool users scored, on average, about 6.7 points lower on final exams than non-users. The drop was especially pronounced among high-potential learners—suggesting the tools interfered with mastering challenging material (Ole et al., 2024). Yet some literature points to benefits for revision and practice. Platforms support quiz and flashcard generation, scaffolding reflective study, and helping fill knowledge gaps. Jisc research documents widespread student use of AI to create self-quizzes, practice exams, and revision aids— tools that many describe as time-savers and mindset-boosters (Attewell, 2025). However, the overall takeaway: when AI acts as a substitute for internal processing, students may lose out in high-stakes assessment contexts. 3. Student Perceptions and Experiences with AI Tools Beyond performance metrics, student perceptions reveal clear benefits—but express nuanced concerns on the use of AI tools. A May 2025 study of over 260 undergraduates surfaced themes: many valued instant feedback, help with writing, idea generation, and study support. Equally noted were worries: risks to academic integrity, erosion of independent problem-solving, inaccuracy of AI content, and privacy or bias issues. Students recommended that institutions develop clear policies and AI literacy curricula Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 62 (Criddle & Jack, 2025; Lin & Chen, 2024). Similarly, a 2025 mixed-methods evaluation in engineering revealed users valued convenience, ease of use, and access to explanations. Still, ethical uncertainty and fear of unintentional misconduct limited some engagement (Sajja et al., 2025). Other research in diverse national contexts shows most learners perceive AI as time-saving and useful for structuring knowledge and generating ideas, especially for language, coding, and initial concept formulation. Yet a minority voice concerns that AI might stifle creativity or weaken critical thinking over time (Alsaeed Alshamy et al., 2025; Fošner, 2024; Yan et al., 2025). One paper emphasizes that students often overestimate what AI can reliably do, and misunderstand its limitations. It recommends adopting explainable AI (XAI) designs and involving students in tool development so they build realistic expectations and trust (Marrone et al., 2024). Collectively, students tend to welcome AI as a helpful collaborator—so long as its use is transparent, governed by clear rules, and combined with human teaching. Research gap While global studies have explored AI-assisted learning, there remains a paucity of empirical evidence on how Nigerian engineering students utilize AI for individualized exam preparation and how such use influences academic confidence and preparedness. Theoretical Framework Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) This study is based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is a continuation of the first UTAUT model created by Venkatesh et al. (2012) to describe the intentions and behavior of users to adopt technology in consumer environments (Marikyan and Papagiannidis, 2021). Some of the major constructs adopted in UTAUT2 include performance expectancy (perceived usefulness), expectancy of ease of use (ease of use), social influence, facilitating conditions, hedonic motivation, price value, and habit, which are highly predictive of behavioral intention and actual use. UTAUT2 would be especially applicable to the current topic of AI tools to prepare the exam because it takes into consideration the potential perception of engineering students in terms of the importance of AI in improving academic performance (e.g., simplifying concepts and increasing confidence) and overcoming certain barrier factors, such as ethical concerns or overdependence. Performance expectancy is connected to the student perception of AI effectiveness in individualized learning, whereas the effort expectancy is connected to the availability of the tools in time when the student is under stressful exams. This framework informs on analyzing the patterns of use, perceptions, as well as the connection between adoption of AI and exam preparedness, which has been present in previous usages of educational technology (Yakubu et al., 2025). 3.0 Methodology This study employed a descriptive cross-sectional survey design to investigate how undergraduate engineering students at a private university in southwestern Nigeria use artificial intelligence (AI) tools during exam preparation. Participants were drawn from six disciplines: Electrical, Computer, Mechanical, Mechatronics, Civil, and Biomedical Engineering, with the population shown in Table 1. Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 63 Table 1. Population breakdown of engineering students by discipline S/N Engineering Discipline Number of Students Percentage (%) 1 Electrical Engineering 41 14.1 2 Computer Engineering 67 23.1 3 Mechanical Engineering 21 7.2 4 Mechatronics Engineering 93 32.0 5 Civil Engineering 28 9.6 6 Biomedical Engineering 49 16.9 Total 299 100 A proportionate random sampling method was used to ensure departmental representation (Rahman & Sarker, 2022). Data were collected with a structured questionnaire based on UTAUT2, covering demographics, frequency and purpose of AI use, perceived usefulness, ethical considerations, and discipline-specific experiences. The instrument was validated by experts, piloted with 10 students, and refined accordingly. Questionnaires were distributed electronically via departmental WhatsApp forums. Participation was voluntary, with respondents free to withdraw at any time. Data were analysed using Stastify.App. Descriptive statistics summarised AI use, while Chisquare tested associations across disciplines. Linear regression examined the link between AI effectiveness and exam preparedness. Analyses were conducted at a 95% confidence level (p < 0.05), with assumptions of normality and linearity satisfied. 4.0 Results and Discussion Presentation of Results 1. Demographic Information a. Gender Table 2: Gender Distribution of Respondents Category N Observed Probability Gender Female 12 10.26% Male 105 89.74% Valid Total 117 100% Fig 1: Frequency and percentage distribution of respondents by gender Table 2 and Figure 1 show a striking gender imbalance among the 117 engineering students surveyed. Males dominate, making up 89.7% (105 respondents), while females account for just 10.3% (12 respondents). This lopsided distribution reflects the broader trend in engineering fields, especially in a Nigerian university setting, where male participation tends to overshadow female involvement. Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 64 b. Age Table 3 Age Distribution of Respondents Category N Observed Probability Age 18–21 74 62.71% 26–30 5 4.24% Under 18 20 16.95% 22–25 16 13.56% Above 30 3 2.54% Valid Total 118 100% Fig 2 Frequency and percentage distribution of respondents by age group. Table 3 and Figure 2 break down the age distribution of the 118 engineering students surveyed, painting a picture of a youthful group. The majority, 62.7% (74 students), are 18–21 years old, which makes sense for undergraduates in their prime college years. A smaller chunk, 17% (20 students), are under 18, likely fresh entrants, while 13.6% (16 students) fall between 22–25. Only a handful, 4.2% (5 students) and 2.5% (3 students), are 26– 30 or over 30, respectively. c. Level of Study in each Department Table 4 Cross-tabulation of respondents’ level of study by engineering department. Department Level of Study 300 Level 200 Level 100 Level 500 Level 400 Level Total Biomedical Engineering 13 1 5 3 1 23 Civil Engineering 2 8 18 0 1 29 Mechatronics Engineering 10 9 0 0 7 26 Computer Engineering 6 2 6 4 4 22 Electrical/Electronic Engineering 1 4 0 1 3 9 Mechanical Engineering 7 1 0 0 1 9 Total 39 25 29 8 17 118 Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 65 Fig 3 Respondents’ level of study by engineering department. Table 4 and Figure 3 map out the distribution of 118 engineering students across different levels of study and departments, revealing a varied academic landscape. Civil Engineering has the largest group at 29 students, with a heavy concentration at the 100 level (18 students), suggesting a strong influx of beginners. Mechatronics follows with 26 students, spread across 100 to 400 levels, while Biomedical Engineering has 23, mostly at the 300 level (13 students). Computer Engineering counts 22 students, with a balanced mix across levels, whereas Electrical and Mechanical Engineering each have only 9 students, skewed toward higher levels like 300 and 400. 2. Patterns of AI Tool Usage Table 5: Respondents’ frequency of AI tool use for academic purposes. Have you ever used Artificial Intelligence tools for academic purposes? Category N Observed Probability Yes 106 90.6% Maybe 3 2.56% No 8 6.84% Valid Total 117 100% Fig 4: Respondents’ frequency of AI tool use for academic purposes Table 5 and Figure 4 dive into how often 117 engineering students turn to AI tools for academic work, and the numbers tell a clear story. A whopping 90.6% (106 students) say they’ve used these tools, showing just how deeply integrated tech has become in their studies. Only a tiny sliver, 6.8% (8 students), claim they’ve never touched AI, while 2.6% (3 students) sit on the fence with a “maybe.” Table 6: Responses showing the Use of AI Tools During Exam Preparation Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 66 . I use AI tools regularly when preparing for my exams Category N Observed Probability Strongly Agree 20 16.95% Agree 66 55.93% Neutral 25 21.19% Strongly Disagree 3 2.54% Disagree 4 3.39% Valid Total 118 100% Fig 5 Likert-scale responses to AI tool usage during exam preparation. Table 6 and Figure 5 zoom in on how often 118 engineering students lean on AI tools specifically for exam prep, and it’s eye-opening. A solid 72.9% (86 students) agree or strongly agree they regularly use AI, with 55.9% (66 students) in the “Agree” camp and 16.9% (20 students) firmly at “Strongly Agree.” Meanwhile, 21.2% (25 students) are neutral, and just 5.9% (7 students) disagree or strongly disagree. Table 7 Students’ agreement with AI tools simplifying complex concepts. I use AI tools to simplify or clarify complex concepts during my revision. Category N Observed Probability Agree 65 55.08% Strongly Agree 44 37.29% Neutral 6 5.08% Disagree 1 0.85% Strongly Disagree 2 1.69% Valid Total 118 100% Fig 6 Students’ agreement with AI tools simplifying complex concepts. Table 7 and Figure 6 capture how 118 engineering students feel about AI tools simplifying tough concepts during study sessions, and the response is overwhelmingly positive. A striking 92.4% (109 students) either agree (55.1%, 65 students) or strongly agree (37.3%, 44 students) that AI helps clarify complex ideas. Just 5.1% (6 students) are neutral, and a mere 2.5% (3 students) disagree or strongly disagree. 3. Perceived Usefulness and Exam Preparedness The study evaluated students’ perceptions of the usefulness of AI tools in aiding their academic performance, exam confidence, and revision efficiency. Table 8: Perceived Usefulness of AI Tools Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 73 just using these tools for novelty—they are relying on them to streamline information, manage their time, and create structure during high-pressure periods. This mirrors findings from Bravo & Cruz-Bohorquez, (2024), who reported a 65% increase in students’ self-rated exam confidence following the integration of chatbot-based revision tools. Inter-Departmental Variation: A Curriculum-Tuned Effect The cross-departmental comparisons illustrate a more nuanced picture. AI’s perceived effectiveness is not uniform—it fluctuates according to curricular design, technical demand, and perhaps even departmental culture. For instance, students in Biomedical and Computer Engineering reported both high usage and high confidence, with mean exam confidence scores above 4.2 on a 5-point scale. In contrast, students in Mechanical and Electrical Engineering reported mean confidence scores below 4.0, coupled with less frequent tool usage. There are several plausible interpretations. First, Biomedical Engineering benefits from AI simulations and virtual labs that align well with conceptual learning, as noted in Essien et al. (2024) and Salman et al. (2025). Second, Computer Engineering students may possess greater digital fluency, making them more likely to explore and persist with AI applications even when initial outputs fall short. By contrast, students in more quantitative fields may have encountered the limitations of AI tools firsthand—particularly the unreliability of answers in circuit design or differential equations. AI-generated solutions in electrical engineering might often lack the structural logic required for technical soundness, making them difficult to trust without substantial modification. This likely explains why Electrical Engineering students in this study were less inclined to rate AI tools as useful or confidence-boosting (Chew, 2023). Interpreting the Hypothesis Test: Implications and Boundaries The positive, statistically significant relationship between AI use and perceived preparedness offers strong support for the alternative hypothesis. This aligns with international research documenting the supportive role of AI in reducing academic uncertainty and improving self-efficacy (Joseph et al., 2024; Sajja et al., 2025). However, we must be cautious in interpreting this result. Confidence is not a proxy for competence, and the study did not assess actual exam scores. The link between AI usage and perceived preparedness may reflect psychological reassurance rather than measurable gains in performance. Students may feel more in control simply because AI platforms provide immediate responses and organized summaries. Whether this translates into more effective problem-solving remains an open question. Still, confidence is not an empty metric. It influences help-seeking behaviour, persistence, and even test anxiety. From an instructional standpoint, tools that enhance student confidence are valuable— provided they are embedded within broader frameworks that promote accuracy and accountability. Implications for Curriculum, Policy, and Practice The findings suggest a need to rethink the role of AI tools in engineering education—not as optional extras but as structured components of academic development. First, faculties should be supported in integrating AI into teaching practices, not just as content generators but as pedagogical scaffolds. Second, departmental policies should account for the varying utility of AI tools across disciplines. Where AI lacks precision (e.g., Electrical Engineering), tool-specific training or hybrid solutions involving human review might be necessary. Moreover, ethical literacy should be paired with technical instruction. As students become more reliant on AI for exam preparation, questions arise about originality, verification, and critical engagement. Universities need clear Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 74 guidelines not just on what tools may be used, but how they should be evaluated in academic settings. 5.0 Conclusion The study underscores the transformative role of artificial intelligence tools in enhancing individualized exam preparation among engineering undergraduates. AI improves students’ conceptual understanding, confidence, and revision efficiency, especially in disciplines with high digital exposure. However, disparities in adoption across fields highlight the need for tailored training and ethical guidance. Recommendations 1. Integrate AI literacy modules into engineering curricula. 2. Develop institutional policies on ethical AI use. 3. Encourage faculty to design AI-assisted formative assessments. 4. Conduct longitudinal studies linking AI use to actual exam scores. References Adewale, M. D., Azeta, A., Abayomi-Alli, A., & Sambo-Magaji, A. (2024). Impact of artificial intelligence adoption on students’ academic performance in open and distance learning: A systematic literature review. Heliyon, 10(22), e40025. https://doi.org/10.1016/j.heliyon.2024.e40025 Adoghe, O. B., Ikharo, B. A., Amhenrior, H. E., Abiodun, J., & Ebede, B. C. (2024). Data collection module for a centralized electronic health records system. Journal of Engineering Research, Innovation, and Scientific Development, 2(1), 11–19. https://doi.org/10.61448/jerisd21242 Alli, S., & Adoghe, O. B. (2024). Advancing image processing systems through embedded FPGA technology: Improving performance, efficiency, and real-time applications. Pakistan Advances in Engineering Research. https://pakadvances.com/index.php/PAERJ/article/view/5 Almasri, F. (2024). Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research. Research in Science Education, 54(1). https://doi.org/10.1007/s11165-024-10176-3 Alsaeed Alshamy, Salim, A., & Abdullah, S. (2025). Perceptions of Generative AI Tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University. Education Sciences, 15(4), 501–501. https://doi.org/10.3390/educsci15040501 Anthropic. (2023, March 14). Introducing Claude. Www.anthropic.com. https://www.anthropic.com/news/introducing-claude Attewell, S. (2025, May 22). Student perceptions of AI 2025 - Jisc. Jisc. https://www.jisc.ac.uk/reports/student-perceptions-of-ai-2025 Axios. (2024). Axios. https://www.axios.com/newsletters/axios-ai-plus-07e5c670-5a4f-11ef8dfc-71ff6e4cb7a4 Bauer, E., Greiff, S., Graesser, A. C., Scheiter, K., & Sailer, M. (2025). Looking Beyond the Hype: Understanding the Effects of AI on Learning. Educational Psychology Review,37(2).https://doi.org/10.1007/s10648-025-10020-8 Benke, E., & Szőke, A. (2024). Academic Integrity in the Time of Artificial Intelligence: Exploring Student Attitudes. Italian Journal of Sociology of Education, 16(2), 91– 108.https://doi.org/10.14658/PUPJ-IJSE-2024-2-5 Bravo, F. A., & Cruz-Bohorquez, J. M. (2024). Engineering Education in the Age of AI: Analysis of the Impact of Chatbots on Learning in Engineering. Education Sciences, 14(5), 484. https://doi.org/10.3390/educsci14050484 Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 75 Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 1–25. https://doi.org/10.1186/s41239-023-00408-3 Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8 Chen, C. (2023, March 9). AI Will Transform Teaching and Learning. Let’s Get It Right. Stanford University. https://hai.stanford.edu/news/ai-will-transform-teaching-andlearning-lets-get-it-right Chew, P. (2023, October 13). Pioneering Tomorrow’s AI System Through Electrical Engineering. An Empirical Study Of The Peter Chew Rule For Overcoming Error In Chat GPT. Ssrn.com. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4601107 Criddle, C., & Jack, A. (2025, July 18). Chatbots in the classroom: how AI is reshaping higher education. @FinancialTimes; Financial Times. https://www.ft.com/content/adb559da1bdf-4645-aa3b-e179962171a1 Crompton, H., & Burke, D. (2023). Artificial Intelligence in Higher education: the State of the Field. International Journal of Educational Technology in Higher Education,20(1), 1– 22.https://doi.org/10.1186/s41239-023-00392-8 Dempere, J. M., Modugu, K. P., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1206936 Ebede, B. C., Adoghe, O. B., & Imoukhuede, P. O. (2023). Utilization of social media as a digital learning platform for secondary schools in Egor Local Government Area of Edo State. Nigerian Journal of Educational Management, 7(2), 42–53. https://www.researchgate.net/publication/394194208_Utilization_of_social_media_as _a_Digital_Learning_platform_for_Secondary_Schools_in_Egor_Local_Government _Area_of_Edo_State Essien, A., Salami, A., Ajala, O., Bamidele Adebisi, Adesina Shodiya, & Essien, G. (2024). Exploring socio-cultural influences on generative AI engagement in Nigerian higher education: an activity theory analysis. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00352-3 Fošner, A. (2024). University students’ attitudes and perceptions towards AI tools: Implications for sustainable educational practices. Sustainability, 16(19), 8668–8668. https://doi.org/10.3390/su16198668 Grájeda, A., Burgos, J., Olivera, P. C., & Sanjinés, A. (2023). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1). https://doi.org/10.1080/2331186x.2023.2287917 Granjeiro, J. M., Cury, A. A. D. B., Cury, J. A., Bueno, M., Sousa-Neto, M. D., & Estrela, C. (2025). The Future of Scientific Writing: AI Tools, Benefits, and Ethical Implications. Brazilian Dental Journal, 36. https://doi.org/10.1590/0103-644020256471 Imran, M., & Almusharraf, N. (2024). Google Gemini as a next generation AI educational tool: a review of emerging educational technology. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00310-z Joseph, G. V., P, A., Thomas M, A., Jose, D., V Roy, T., & Prasad, M. P. (2024). Impact of Digital Literacy, Use of AI tools and Peer Collaboration on AI Assisted LearningPerceptions of the University students. Digital Education Review, 45, 43–49. https://doi.org/10.1344/der.2024.45.43-49 Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 76 Kamalov, F., Calonge, D. S., & Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability, 15(16), 12451. mdpi. https://doi.org/10.3390/su151612451 Kaputa, V., Loučanová, E., & Tejerina-Gaite, F. A. (2022). Digital Transformation in Higher Education Institutions as a Driver of Social Oriented Innovations. Innovation, Technology, and Knowledge Management, 61–85. https://doi.org/10.1007/978-3-03084044-0_4 Kerimbayev, N., Adamova, K., Shadiev, R., & Altinay, Z. (2025). Intelligent educational technologies in individual learning: a systematic literature review. Smart Learning Environments, 12(1). https://doi.org/10.1186/s40561-024-00360-3 Khlaif, Z. N., Mousa, A., Hattab, M. K., Itmazi, J., Hassan, A. A., Sanmugam, M., & Ayyoub, A. (2023). The Potential and Concerns of Using AI in Scientific Research: ChatGPT Performance Evaluation. JMIR Medical Education, 9(1), e47049. https://doi.org/10.2196/47049 Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI Chatbots in education: Systematic Literature Review. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00426-1 Létourneau, A., Martineau, D., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. Npj Science of Learning, 10(1), 1–13. https://doi.org/10.1038/s41539-025-00320-7 Lin, C.-C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00260-y Lin, H., & Chen, Q. (2024). Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: students and teachers’ perceptions and attitudes. PubMed, 12(1), 487–487. https://doi.org/10.1186/s40359-024-01979-0 Machucho, R., & Ortiz, D. (2025). The Impacts of Artificial Intelligence on Business Innovation: A Comprehensive Review of Applications, Organizational Challenges, and Ethical Considerations. Systems, 13(4), 264. https://doi.org/10.3390/systems13040264 Makanju, T. D., Kibuebu, F. E., Adoghe, O. B., Omojoyegbe, M. O., & Famoriji, O. J. (2025). IoT-enabled smart lighting control system with motion detection using passive infrared sensor technology. In New horizons in science, technology, and computing (Vol. 4, pp. 1–14). British Publishing International. https://doi.org/10.9734/bpi/nhstc/v4/6069 Marikyan, D., & Papagiannidis, S. (2021). Unified Theory of Acceptance and Use of Technology: A review. Open.ncl.ac.uk. https://open.ncl.ac.uk/theories/2/unifiedtheory-of-acceptance-and-use-of-technology/ Marrone, R., Zamecnik, A., Srecko Joksimovic, Johnson, J., & Maarten De Laat. (2024). Understanding Student Perceptions of Artificial Intelligence as a Teammate. Technology Knowledge and Learning. https://doi.org/10.1007/s10758-024-09780-z Martínez, E. (2024). Re-evaluating GPT-4’s bar exam performance. Artificial Intelligence and Law. https://doi.org/10.1007/s10506-024-09396-9 Mat Yusoff, S., Mohamad Marzaini, A. F., Hao, L., Zainuddin, Z., & Basal, M. H. (2025). Understanding the role of AI in Malaysian higher education curricula: an analysis of student perceptions. Discover Computing, 28(1). https://doi.org/10.1007/s10791-02509567-5 Mesko, B. (2023). The ChatGPT (Generative Artificial Intelligence) Revolution Has Made Artificial Intelligence Approachable for Medical Professionals. Journal of Medical Internet Research, 25(1), e48392. https://doi.org/10.2196/48392 Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 77 Mhlanga, D. (2023). The Value of Open AI and Chat GPT for the Current Learning Environments and the Potential Future Uses. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4439267 Mittal, A. (2025, July 22). ChatGPT handles 2.5 billion prompts daily: Here’s how students are turning it into their ultimate AI tutor. The Times of India; The Times Of India. https://timesofindia.indiatimes.com/education/news/chatgpt-handles-2-5-billionprompts-daily-heres-how-students-are-turning-it-into-their-ultimate-aitutor/articleshow/122831017.cms Murillo, & Weigang, L. (2025). Grok, Gemini, ChatGPT and DeepSeek: Comparison and Applications in Conversational Artificial Intelligence. 1(1). https://doi.org/10.5281/zenodo.14885243 Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Petar Jandrić, Zhao, J., Salha, S., Xu, L., Panda, S., None Kinshuk, Sonsoles López-Pernas, & Saqr, M. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00350-5 Odey, B. E., Arubami Aghogho Joushua, & AKOR, G. B. (2025). Influence of AI on the Academic Performance of Students of Uni CRS, Calabar. Vol. 2, 23–29. https://www.researchgate.net/publication/393587603_Influence_of_AI_on_the_Acad emic_Performance_of_Students_of_Uni_CRS_Calabar Ole, W. J., Voshaar, J., Plate, B. J., & Zimmermann, J. (2024). Generative AI Usage and Exam Performance. ArXiv.org. https://arxiv.org/abs/2404.19699 OpenAI. (2023a, March 14). GPT-4. Openai.com. https://openai.com/index/gpt-4-research/ OpenAI. (2023b). GPT-4 Technical Report. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2303.08774 Oseghale, J. (2024, October 28). The Future of Education in Nigeria: E-Learning, AI & Virtual Reality. Nile University of Nigeria. https://nileuniversity.edu.ng/the-future-ofeducation-in-nigeria-embracing-online-learning-ai-and-virtual-reality-in-nigeria/ Sajja, R., Sermet, Y., Fodale, B., & Demir, I. (2025). Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications. ArXiv.org. https://arxiv.org/abs/2506.05699 Salman, I. M., Ameer, O. Z., Khanfar, M. A., & Hsieh, Y.-H. (2025). Artificial intelligence in healthcare education: evaluating the accuracy of ChatGPT, Copilot, and Google Gemini in cardiovascular pharmacology. Frontiers in Medicine, 12. https://doi.org/10.3389/fmed.2025.1495378 UNESCO. (2023). Artificial intelligence in education. UNESCO. https://www.unesco.org/en/digital-education/artificial-intelligence Vieriu, A. M., & Petrea, G. (2025). The Impact of Artificial Intelligence (AI) on Students’ Academic Development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343 Wang, X., Xu, X., Zhang, Y., Hao, S., & Jie, W. (2024). Exploring the impact of artificial intelligence application in personalized learning environments: thematic analysis of undergraduates’ perceptions in China. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-04168-x xAI. (2024). Grok. X.ai. https://x.ai/grok Yakubu, M. N., David, N., & Abubakar, N. H. (2025). Students’ behavioural intention to use content generative AI for learning and research: A UTAUT theoretical perspective. Education and Information Technologies. https://doi.org/10.1007/s10639-025-134418 Journal of Education, Communication, and Digital Humanities-Vol.2, No.2, Sept. 2025 pg. 78 Yan, Y., Wu, B., Pi, J., & Zhang, X. (2025). Perceptions of AI in Higher Education: Insights from Students at a Top-Tier Chinese University. Education Sciences, 15(6), 735. https://doi.org/10.3390/educsci15060735 Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13299-2