# Using Internet and AI Engineering in Education: A Qualitative Study

Jean Fernandez, Alber Mendoza, Jay Morgan

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► To cite this version:

Jean Fernandez, Alber Mendoza, Jay Morgan. Using Internet and AI Engineering in Education: A Qualitative Study. International Journal of Advances in Engineering and Management (IJAEM), 2025, 2 (2), pp.450-53. 10.35629/5252-45122323. hal-05060382

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# Using Internet and AI Engineering in Education: A Qualitative Study

¹Jean Fernandez, ²Alber Mendoza, ³Jay Morgan

¹,²Southern Dilao College, Department of Engineering and Advanced Vocational Education, Dilao, Philippines

³Ladoke Akintola University of Technology, Engineering Department, Ogbomoso, Nigeria

Date of Submission: 04-20-2025

Date of Acceptance: 05-08-2025

**ABSTRACT:** The integration of Internet and Artificial Intelligence (AI) technologies in education is rapidly transforming teaching and learning paradigms. This qualitative research explores how educators and students perceive and utilize these technologies to enhance educational experiences. Data was collected through semi-structured interviews with ten educators and five education technology experts across various institutions. Thematic analysis revealed key areas of impact, including personalized learning, improved accessibility, data-driven instruction, and new pedagogical strategies. While promising, the study also highlights challenges such as digital divide, ethical concerns, and lack of proper training. This research contributes to understanding the practical implications of Internet and AI engineering in modern education.

**KEYWORDS:** Internet in education, AI in education, qualitative research, technology integration, digital pedagogy

## I. INTRODUCTION

The digital age has ushered in significant changes across all sectors, with education being one of the most affected. The use of Internet-based platforms for online learning and AI-powered tools for educational enhancement is increasingly prevalent (Luckin et al., 2016, Akmad, 2024). The COVID-19 pandemic further accelerated the adoption of these technologies, pushing both institutions and educators to adapt swiftly (Dhawan, 2020). AI engineering in particular has enabled adaptive learning systems, automated grading, intelligent tutoring systems, and predictive analytics for student performance (Holmes et al., 2019).

Despite the rapid growth in the deployment of these technologies, the understanding of their qualitative impact from the perspective of educators remains limited. Most studies have focused on quantitative outcomes such as performance metrics and user statistics. There is a need to investigate how these technologies influence teaching practices, student engagement, and the broader educational ecosystem from an experiential point of view.

The purpose of this study is to explore how Internet and AI engineering technologies are being used in educational settings and to understand the perceptions of educators and experts regarding their benefits and challenges. The study focuses on:

1. How teachers and education technology professionals perceive the integration of Internet and AI in education.

2. The ways in which these technologies impact pedagogy, student engagement, and learning outcomes.

3. The perceived challenges and barriers to effective integration.

The scope of this study is limited to qualitative data collected through interviews with participants from higher education and secondary education institutions.
## II. RESULT AND DISCUSSION

### Thematic Findings

Using thematic analysis of interview transcripts, the following key themes were identified:

**a. Personalized and Adaptive Learning**

Participants reported that AI tools such as adaptive learning platforms (e.g., Knewton, DreamBox) have allowed for more individualized learning experiences. Teachers noted that students who struggled with certain topics benefited from targeted content delivery, enhancing their understanding.

*"The AI system identifies weaknesses early and gives me insights I wouldn't see on my own,"* said one high school teacher.

**b. Enhanced Accessibility and Flexibility**

Internet-based platforms such as Google Classroom and Zoom have made learning more accessible, especially for students in remote areas or with disabilities. Educators emphasized the flexibility of asynchronous learning as a major advantage.

**c. Data-Driven Decision Making**

AI tools were reported to provide data analytics that help instructors make informed decisions. Dashboards indicating student engagement, comprehension levels, and assignment completion were seen as valuable for instructional planning.

**d. Ethical and Technical Challenges**

Despite the benefits, concerns were raised about student privacy, data security, and algorithmic bias. Several participants also pointed out the lack of institutional support and adequate training.

*"We're using powerful tools but don't fully understand how they work or what risks they carry,"* noted an educational technologist.

**e. Digital Divide and Equity**

Access to stable internet and advanced devices remains uneven, especially in rural or underserved communities. This gap creates inequality in educational outcomes.

The findings of this study resonate with existing literature on the transformative potential of AI in education (Zawacki-Richter et al., 2019) and underscore the nuanced human experiences behind technological adoption. Teachers perceive AI and Internet tools as supportive rather than replacement technologies, reinforcing the idea that human facilitation remains central to effective learning. The study reaffirms the importance of digital literacy and the ethical deployment of AI systems in educational contexts.

*Infrastructural Challenges and the Digital Divide*
A significant barrier to the effective integration of Internet and AI technologies in education is the issue of unstable internet connectivity. Akmad and Abatayo (2024) conducted a qualitative study focusing on tertiary students in Palimbang, Philippines, revealing that erratic internet connections led to delays in task submissions, reduced access to online resources, and hindered collaborative learning efforts. Students reported that poor connectivity not only consumed more time but also diminished their enthusiasm and ability to participate in group projects. Conversely, stable internet connections facilitated quicker access to resources, more efficient research, and timely submission of academic tasks.

Further, Akmad (2024) explored the resilience of students facing internet challenges, highlighting the emotional toll and the coping mechanisms employed to navigate these obstacles. The study emphasized the need for institutional support to address these infrastructural issues and to foster environments conducive to learning despite technological limitations.

## Personalized Learning and Intelligent Tutoring Systems

The advent of AI has introduced personalized learning experiences through Intelligent Tutoring Systems (ITS). Maity and Deroy (2024) discuss how generative AI, particularly large language models like GPT-4, enables dynamic content generation and real-time feedback, tailoring educational experiences to individual learner needs. These systems can adapt to various learning styles and paces, offering customized support that traditional teaching methods may not provide.

## Career Guidance through Machine Learning

AI's role in career guidance is another area of significant impact. Trujillo et al. (2025) conducted a systematic literature review highlighting how machine learning algorithms, such as Random Forest and Support Vector Machines, are employed to predict suitable career paths for students based on academic performance, interests, and demographic data. This approach aids in aligning students' strengths and preferences with potential career opportunities, enhancing decision-making processes in education.

## Ethical Considerations and Data Privacy

While AI offers numerous benefits, it also raises ethical concerns, particularly regarding data privacy and algorithmic bias. Yerbabuena Torres et al. (2024) emphasize the importance of addressing these challenges to ensure equitable access to AI technologies in education. They advocate for transparency in AI algorithms and the implementation of policies that protect student data, ensuring that AI integration does not inadvertently perpetuate existing inequalities.

## Global Initiatives and Policy Implications

Globally, countries are recognizing the potential of AI in education and are initiating reforms to integrate these technologies. For instance, China has announced a significant educational reform initiative that involves integrating AI into all levels of its educational system, including teaching methods, textbooks, and school curricula. This move is part of China's broader strategy to enhance innovation and sustain economic growth. According to a statement from the Ministry of Education, the integration of AI aims to develop essential skills among teachers and students—such as independent thinking, problem-solving, communication, and collaboration—and to elevate classroom experiences with innovative and challenging content.
### III. CONCLUSION

This qualitative study reveals that Internet and AI engineering technologies offer significant benefits in terms of personalization, access, and instructional efficiency in education. However, these benefits are tempered by challenges including ethical concerns, training gaps, and infrastructural inequalities. To maximize the potential of these tools, stakeholders must invest in professional development, infrastructure, and ethical frameworks for responsible AI use. Further longitudinal and mixed-methods studies are recommended to deepen insights into long-term impacts. The integration of Internet and AI technologies in education holds immense promise for enhancing learning experiences, personalizing education, and preparing students for future careers. However, to fully realize these benefits, it is imperative to address infrastructural challenges, ethical considerations, and ensure equitable access. Collaborative efforts among educators, policymakers, and technologists are essential to navigate the complexities of AI integration and to foster an educational environment that is inclusive, ethical, and effective.

### REFERENCES

Akmad, A. P. (2024). Academic Resilience in the Face of Internet Challenges: A Narrative Analysis of Tertiary Students' Experiences. Journal of Interdisciplinary Perspectives, 2(8), 551-563. https://doi.org/10.69569/jip.2024.0255

Akmad, A. P., & Abatayo, A. V. L. (2024). Caught in the slow lane: Effects of unstable internet connectivity on accessing academic resources and collaborative learning. Journal of Interdisciplinary Perspectives, 2(9), 278-286. https://doi.org/10.69569/jip.2024.0370

Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22. https://doi.org/10.1177/0047239520934018

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education. https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf

Maity, S., & Deroy, A. (2024). Generative AI and Its Impact on Personalized Intelligent Tutoring Systems. arXiv preprint arXiv:2410.10650. https://arxiv.org/abs/2410.10650

Trujillo, F., Pozo, M., & Suntaxi, G. (2025). Artificial intelligence in education: A systematic literature review of machine learning approaches in student career prediction. Journal of Technology and Science Education, 15(1), 162-185. https://doi.org/10.3926/jotse.3124

Yerbabuena Torres, C. F., Villagomez Cabezas, A. V., Yerbabuena Torres, A. R., & Mendoza Torres, N. A. (2024). Artificial Intelligence Tools Applied to Education: A Systematic Literature Review. International Journal of Interactive Mobile Technologies (iJIM), 18(24), 155-174. https://doi.org/10.3991/ijim.v18i24.50055

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0