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All Journal International Transactions on Education Technology (ITEE) Journal of Computer Science and Technology Application
Fletcher, Eamon
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Computer Science & IT Control & Systems Engineering Social Sciences Other

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Harnessing Artificial Intelligence in Higher Education: Balancing Innovation and Ethical Challenges Aprianto, Ronal; Lestari, Etty Puji; Sadan; Fletcher, Eamon
International Transactions on Education Technology (ITEE) Vol. 3 No. 1 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i1.680

Abstract

The development of Artificial Intelligence (AI) in higher education has created new opportunities while presenting major challenges. This research aims to explore the impact of AI on higher education, both in terms of benefits and risks that may arise in the future. AI has opened up opportunities to personalize learning experiences, automate administrative processes, and support innovation in curriculum development, potentially improving educational effectiveness. However, there are also concerns regarding the digital divide, data privacy, ethical considerations, and the readiness of educators and institutions to deal with these technological changes. This research uses a literature review approach by analyzing current research on AI implementation in higher education institutions. It also compares case studies from several developed and developing countries to gain a broader picture of the global influence of AI in the education sector. The results show that while AI can have a positive impact in terms of more efficient learning and more effective operations, challenges in terms of equitable access and transparency must be addressed. The novelty of this research lies in the comprehensive analysis of the long-term implications of AI on higher education, as well as the strategies that institutions need to implement to maximize the benefits of AI and minimize the risks. This research makes an important contribution to education stakeholders in understanding the importance of responsible AI adoption to create an inclusive and sustainable educational environment.
Artificial Intelligence in Predictive Cybersecurity: Developing Adaptive Algorithms to Combat Emerging Threats Sudaryono, Sudaryono; Pratomo, Rusdi; Ramadan, Ahmad; Ahsanitaqwim, Ridhuan; Fletcher, Eamon
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.55

Abstract

The exponential growth of digital systems has introduced significant cybersecurity challenges, exposing vulnerabilities to increasingly sophisticated threats. Traditional security measures, which rely on static and signature-based methods, often fail to adapt to the dynamic nature of cyberattacks, highlighting the need for innovative solutions. This study aims to develop and evaluate adaptive algorithms in predictive cybersecurity, leveraging Artificial Intelligence (AI) to combat emerging threats such as zero-day exploits and advanced persistent threats (APTs). A simulation-based research design was employed, integrating reinforcement learning frameworks like Deep Q-Learning and utilizing datasets such as CICIDS2017 and synthetic data for zero-day threat simulations. The results show that adaptive algorithms achieved 94.8% detection accuracy, reduced false positives by 54.5%, and improved response times by 53.1%, significantly outper forming static models. Additionally, the adaptive systems demonstrated superiorcapacity to identify novel threats in simulated attack scenarios. These findings underscore the potential of adaptive AI algorithms to revolutionize predictive cybersecurity by offering dynamic, real-time responses to evolving threats. Despite their computational demands posing challenges for smaller organizations, integrating techniques such as adversarial training and robust anomaly detection can enhance resilience. That adaptive algorithms can enhance the resilience and reliability of cybersecurity systems, advocating for future integration with technologies like blockchain and edge computing to address scalability and latency issues. These advancements pave the way for more robust and proactive cybersecurity defenses in an increasingly interconnected digital landscape.
Co-Authors Ahsanitaqwim, Ridhuan Etty Puji Lestari Pratomo, Rusdi Ramadan, Ahmad Ronal Aprianto Sadan Sudaryono Sudaryono