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All Journal Mikailalsys Journal of Advanced Engineering International
Dudari, Mafeng Jamima
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Promoting the Effective Use of AI in Learning: A Smart Student’s Perspective at Karl Kumm University, Vom Abba, Dadi Jonathan; Yarma, Adamu Ahmed; Dudari, Mafeng Jamima
Mikailalsys Journal of Advanced Engineering International Vol 3 No 1 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i1.8015

Abstract

Artificial intelligence (AI), with its capacity to support individualized learning, efficient research, and enhanced academic productivity, has become a disruptive force in higher education. However, limited understanding, low levels of digital literacy, and ethical concerns prevent many students from harnessing AI effectively. This study examines strategies for promoting the efficient and responsible use of AI in education from the perspective of “smart students” at Karl Kumm University, Vom. Using a mixed-methods design, data were collected from 200 undergraduate students through surveys and interviews to explore AI awareness, adoption patterns, perceived benefits, and perceived challenges. The findings indicate that students recognize AI’s potential to improve learning and engagement, yet its optimal use is constrained by inadequate technical skills, fears of over-reliance, and unresolved ethical issues. The study proposes practical interventions, including mentorship schemes, curriculum integration, structured training programs, and clear ethical use guidelines, to foster more responsible and effective adoption of AI in learning. Overall, the results provide actionable insights for higher education institutions seeking to leverage AI to improve academic outcomes and to cultivate an innovative, self-directed learning culture by enabling students to become discerning and competent AI users.
Artificial Intelligence in Early Disease Detection: Trends, Applications, and Challenges Abba, Dadi Jonathan; Dudari, Mafeng Jamima; Jakawa, Jimmy Nirat; Sani, Habibu Aminu; Yona, Kudyo Deborah
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9226

Abstract

Artificial intelligence (AI) is transforming healthcare by improving diagnostic precision, reducing clinician workload, and supporting early disease detection. Early diagnosis is essential for improving patient outcomes, reducing mortality, and lowering healthcare costs. This study examines current developments in AI-assisted diagnostics, with particular attention to applications in cancer, cardiology, neurology, infectious diseases, and personalized medicine. It discusses how AI, through machine learning, deep learning, and predictive analytics, can process large-scale medical datasets, analyze medical images, and support physicians in clinical decision-making. The findings indicate that AI offers substantial benefits for healthcare practice, including improved diagnostic accuracy, enhanced patient monitoring, reduced clinical errors, and more efficient decision support. However, major barriers remain, including algorithmic bias, high implementation costs, data privacy concerns, inadequate physician training, and unresolved ethical issues. The study concludes that the effective adoption of AI in early disease diagnosis requires collaborative research, robust policy frameworks, ethical governance, and practical integration strategies. These insights contribute to current discussions on AI-enabled healthcare by highlighting both its diagnostic potential and the institutional, technical, and ethical conditions needed to optimize its implementation in healthcare delivery.
Co-Authors Abba, Dadi Jonathan Jakawa, Jimmy Nirat Sani, Habibu Aminu Yarma, Adamu Ahmed Yona, Kudyo Deborah