NJLVol. 1, Iss. 1
10/18/2026Lucky Oghenetega URHIEWHU, Uche Mokobia
This study investigates the strategic role of university management in fostering collaboration between embedded librarians and faculty staff to enhance accreditation outcomes in Nigerian universities. With the growing emphasis on digital infrastructure and quality assurance, embedded librarianship—where librarians integrate directly into academic departments—has emerged as a vital approach to supporting teaching, research, and curriculum development. Despite its potential, integration remains limited due to institutional barriers, inadequate policy frameworks, and insufficient managerial support. Using an exploratory descriptive survey design, data were collected from 65 librarians across Nigerian universities through stratified random sampling, supported by faculty questionnaires and in-depth interviews with university management. Quantitative data were analyzed using descriptive statistics and inferential tests, while qualitative responses were thematically examined. Findings reveal that Embedded Librarians significantly contribute to accreditation processes by preparing documentation, ensuring collection standards, and collaborating with faculty. Management support, particularly through resource allocation and structured interaction, was moderately perceived. However, significant challenges—including poor funding, lack of training, weak institutional policies, and regional gender disparities—continue to undermine collaboration. Communication is primarily through Whatsapp/Institutional Email, with limited use of online collaborative platforms, suggesting a gap in digital integration. The study concludes that while embedded librarianship holds promise for strengthening institutional accreditation, its success hinges on proactive managerial strategies, digital infrastructure investment, and continuous professional development. It recommends that universities implement policy reforms, promote gender equity, and enhance digital literacy to maximize the strategic contribution of librarians in quality assurance and accreditation success.
DOI: 10.5555/necr.2026.003 J. Sci. Technol. AIVol. 1, Iss. 2
10/17/2026Chuks Emmanuel Okolo
This research on how artificial intelligence is being used in modern-day marketing focuses understanding the current landscape, including recent trends and statistics related to AI adoption in the field of marketing. I plan to investigate the specific ways AI is being applied in marketing. This includes areas like creating personalized marketing experiences, enhancing advertising efforts, and developing AI-powered tools for content creation. I will also look into how AI is transforming customer relationship management (CRM) and customer service within marketing. This involves understanding the role of AI in managing customer data and providing support. Furthermore, I intend to explore how AI is used for marketing analytics, including its ability to analyze data, predict what customers might do next, and help optimize marketing strategies for better results. A key part of my research will be to identify the main benefits that AI brings to marketing, such as making processes more efficient, improving how precisely marketing efforts are targeted, and leading to better customer engagement. It's also important to understand the challenges and potential downsides of using AI in marketing. This includes looking at ethical considerations and limitations that need to be addressed. To illustrate the practical application of AI in marketing, I will be searching for case studies of companies that have successfully implemented AI strategies. Finally, I will aim to understand the future trends in AI for marketing and what kind of impact these advancements are likely to have on the field of marketing.
DOI: 10.5555/jstai.2026.005 Interdiscip. Health Life Sci.Vol. 1, Iss. 1
10/16/2026Chuks Emmanuel Okolo
Artificial intelligence (AI) is rapidly emerging as a transformative force within the healthcare industry, promising to reshape medical practices in unprecedented ways. Its capacity to analyze intricate datasets, discern hidden patterns, and generate actionable insights positions AI as a pivotal technology capable of addressing some of the most pressing challenges in modern medicine. This research article provides a comprehensive exploration of AI's multifaceted impact on healthcare, encompassing its current applications, the advantages and limitations of its implementation, the evolving roles of healthcare professionals, anticipated future trends, illustrative case studies, and potential risks. The overarching aim is to offer healthcare administrators and policymakers a nuanced understanding of AI's strategic implications for the future of healthcare.
DOI: 10.5555/ihls.2026.003 J. Sci. Technol. AIVol. 1, Iss. 2
10/16/2026Chuks, Emmanuel Okolo
Generative AI tools have become part of daily study routines faster than most universities have written rules for them. This article reports a survey of students at Lagos Business School (LBS), the graduate management school of Pan-Atlantic University, about how they use AI in six everyday learning activities and how they feel about it. A questionnaire was shared with a target of 100 students and about 97 responded. The picture is one of heavy, habitual use: 88.8% of those answering said they always use AI to prepare for case discussions, 94.2% always use it for writing, and 86.7% always use it for revision. Every respondent who answered agreed that AI makes them less likely to do the full reading, and only 41.3% said they always form their own view before consulting a tool, even though all 63 who answered said their confidence in their own analysis had grown. At the same time, 91.5% said they understand the school's rules on AI, yet 77.6% said those rules are not applied fairly, and all 93 who answered the open question asked the institution for a better policy. Respondents were unanimous that access, cost and power supply create unequal advantages. The article sets out six structured problems drawn from these results and a matching set of recommendations. Because the sample was self-selected and the data are self-reported, the findings describe attitudes and stated habits, not measured learning outcomes.
J. Sci. Technol. AIPeer-ReviewedOpen AccessCase Study
DOI: 10.5555/jstai.2026.004 