New Horizons of Science, Technology and Culture Vol. 11

The Role of Artificial Intelligence in Supporting Managerial Decision-Making

2026-06-06T07:52:40+00:00

Artificial Intelligence (AI) has emerged as a transformative force in organisational decision-making, fundamentally reshaping how managers process information, evaluate alternatives, and exercise judgment. While early discussions surrounding AI emphasised automation and potential job displacement, contemporary perspectives increasingly highlight its augmentative role in enhancing managerial capabilities. This study presents a comprehensive review of existing literature to examine how AI supports managerial decision-making across diverse organisational contexts. The findings suggest that AI enhances decision quality by improving analytical capabilities, enabling real-time data processing, and supporting complex judgments under uncertainty. However, the effectiveness of AI-assisted decision-making is contingent upon several factors, including managerial trust, perceptions of fairness, transparency, and organisational culture. Challenges such as algorithmic opacity, accountability concerns, and over-reliance on automated systems continue to influence adoption and effectiveness. By synthesising prior research, this paper contributes to a nuanced understanding of human–AI collaboration in managerial contexts and offers theoretical and practical implications for organisations seeking to leverage AI responsibly and effectively.

Machine Learning-Based Detection of Students at Academic Risk Due to Excessive AI Tool Usage

2026-06-06T07:56:09+00:00

The rapid adoption of artificial intelligence (AI) tools in education has significantly transformed learning practices, offering enhanced support for academic tasks such as writing, problem-solving, and research. However, excessive reliance on these tools may negatively impact students’ critical thinking, originality, and overall academic performance. This study proposes a machine learning-based framework to detect students at academic risk due to excessive AI tool usage by analysing behavioural and performance-related data. The research integrates multidimensional inputs, including AI usage frequency, session duration, assignment similarity scores, attendance, and GPA, to develop predictive models for early risk identification. A comprehensive data analysis process involving exploratory data analysis, feature engineering, and model training was conducted using multiple classifiers, including Logistic Regression, Decision Trees, Support Vector Machines, and Random Forest. Among these, the Random Forest classifier demonstrated superior performance, achieving an accuracy of 88%, a precision of 86%, a recall of 83%, an F1-score of 84%, and an AUC of 0.91. Feature importance analysis revealed that AI usage frequency and assignment similarity scores are the most significant predictors of academic risk, highlighting the critical role of behavioural patterns over traditional academic indicators. Correlation analysis further confirmed a negative relationship between AI usage and academic performance, alongside a positive association with content similarity, indicating potential overdependence on AI-generated outputs. This manuscript addresses a critical and emerging challenge in AI-enabled education by examining the impact of excessive AI tool usage on student performance. It contributes to the scientific community by proposing a novel machine learning-based framework that integrates behavioural analytics for early identification of at-risk students. The study provides empirical evidence that AI usage patterns are strong predictors of academic risk, offering new insights for educational data mining research. Additionally, it supports the development of proactive, data-driven interventions to enhance academic integrity and student success.

The findings emphasise the dual-edged nature of AI in education, where its benefits must be balanced with responsible usage. The proposed framework enables early detection of at-risk students and supports data-driven, proactive intervention strategies to enhance academic integrity and student success. This study contributes to the growing field of educational data mining by demonstrating the effectiveness of machine learning in monitoring emerging learning behaviours in AI-assisted environments.

A Critical Review of Artificial Intelligence and Its Influence on Organisational Work Practices and Culture

2026-06-06T07:58:31+00:00

Artificial intelligence (AI) is reshaping the landscape of organisational life with a speed and breadth unprecedented in the history of technological change. This critical narrative review synthesises peer-reviewed evidence published between 2018 and 2026 to examine the multidimensional ways in which AI influences organisational work practices and culture. Drawing on 35 verified scholarly sources, the article investigates four interrelated domains: the transformation of task structures and labour processes through automation and augmentation; the emergence of algorithmic management and its implications for worker autonomy and organisational control; the cultural shifts accompanying AI adoption, including changes to trust, learning, and leadership; and the ethical tensions arising from AI's deployment in human resource management and decision-making. The review reveals that AI does not operate as a neutral technology; rather, its effects are profoundly contingent on organisational context, governance choices, and the degree to which employees are meaningfully involved in implementation. Whereas AI creates measurable productivity gains and enables novel forms of human–machine collaboration, evidence equally points to deepening workplace inequalities, surveillance risks, and the erosion of meaningful work for certain categories of employee. Critically, organisations that attend solely to technical deployment while neglecting cultural readiness and ethical governance consistently fail to realise the anticipated value of AI investment. The article concludes by outlining an agenda for future research, highlighting the need for longitudinal, contextually sensitive, and worker-centred scholarship to inform both management practice and public policy.

Oxidative Stability and Quality Assessment of Artisanal Cottonseed Oils from Kossodo, Burkina Faso

2026-06-09T11:21:42+00:00

Artisanal cottonseed oil is widely consumed in Burkina Faso, yet its hygienic production conditions and product quality remain insufficiently controlled. This study assessed the sanitary conditions of three artisanal production units in the Kossodo industrial area of Ouagadougou and evaluated the main physicochemical indicators of oil quality using Codex Alimentarius and ISO reference methods. A descriptive cross-sectional design was used to examine 18 oil samples collected from the three units at different production times, together with structured inspections of raw material storage, equipment hygiene, workflow organisation, and worker practices. The results showed major shortcomings in storage conditions, cleaning practices, waste management, and personal hygiene, indicating weak application of good hygiene practices. Laboratory analyses showed that moisture and acid values remained within acceptable limits, while peroxide values and residual soap exceeded Codex thresholds in two of the three units, suggesting advanced oxidation and incomplete refining. Sensory evaluation also showed non-compliant appearance and odour in several samples, consistent with inadequate process control. Overall, the study indicates that improved hygiene management, process monitoring, and regulatory oversight could substantially enhance the safety and quality of artisanal cottonseed oil in Burkina Faso.

Exploration of Deliver Process for Higher Education with the Application of Artificial Intelligence Tools

2026-06-12T08:55:04+00:00

Technological advancement is driving significant transformation within the higher-education sector, which is already experiencing considerable change. Rising demand for personalized adaptive, and industry-responsive learning, coupled with technological advancements, is increasing the availability and acceptance of predictive analytics tools to support curriculum design. In today’s rapidly evolving higher education landscape, integrating Artificial Intelligence (AI) offers both opportunities and challenges for course design and delivery. This study examines the impact of AI tools, particularly ChatGPT, on student engagement and performance. By analysing exam results and usage patterns, it identifies key trends to support the development of AI-driven educational strategies. A comprehensive data analysis approach, including statistical methods and visualisations, is used to explore the relationship between AI usage and demographic factors such as age, role, and gender. The results reveal notable differences in perceived impact and efficiency across these groups, emphasising the need for customised educational approaches. Overall, the study provides practical recommendations for educators and institutions to effectively leverage AI for improved learning outcomes and personalised education. This study also examines the impact of online education on higher education using data analysis and qualitative methods. It analyses student performance, playtime, sleep, and online interactions, revealing key correlations. Findings highlight benefits such as accessibility, flexibility, and convenience, while also noting challenges. Overall, online education supports lifelong learning and enhances higher education quality. Future research should further investigate longitudinal patterns and more nuanced socio-demographic determinants influencing students’ utilisation of technology. Overall, the visual and statistical analyses undertaken provide a strong empirical basis for informing educational technology strategies and contributing to the advancement of scholarship in the domain of technology-enhanced learning.

Quantitative Analysis of Fourth-Finger Movement during Musical Expression in Early Childhood

2026-06-12T08:59:01+00:00

Musical expression in early childhood is an embodied process that involves the integration of perceptual systems in response to music. While previous research has examined overall body movement, the functional role of individual fingers remains insufficiently explored. This study aims to identify the functional characteristics of the fourth finger during musical expression in early childhood through quantitative movement analysis and simultaneous analysis of finger and eye movements. A total of 116 children aged 3–5 years participated in finger movement analysis using Meta Gloves connected to the MVN motion capture system. In addition, 58 children participated in simultaneous finger and eye movement analysis using an eye tracking system. Quantitative analyses were conducted for the fourth metacarpal, proximal, middle, and distal phalanges. A three-way non-repeated analysis of variance was conducted on the total moving distance, the moving average velocity, the moving average acceleration, and the moving smoothness calculated from the results of the movement analysis, with facility (3 levels), melody (2 levels), and age (3 levels) as factors. As a result, the total moving distance was larger for bright melodies than for dark melodies, and clear contrasts were observed between the moving average acceleration and the moving smoothness depending on melody type. The moving average acceleration was most characteristic in the fourth metacarpal and proximal phalanges, whereas the moving smoothness was most pronounced in the middle phalanges. These results were further validated by analysis using a linear mixed model to extract the characteristics of the fourth finger's movement through comparative analysis with other fingers.

Furthermore, simultaneous analyses of finger and eye movements revealed a close relationship between the two modalities during musical expression. These findings suggest that the extracted feature quantities reflect developmental processes in early childhood musical expression and contribute to improving the discrimination of developmental stages.

Comparison of Pineapple Enzyme Extract and Commercial Enzymes for Enhanced Coconut Oil Extraction

2026-06-16T09:04:22+00:00

Aqueous enzymatic oil extraction is undoubtedly an emerging technology in the fats and oils industry, as it offers many advantages over conventional extraction. For instance, it eliminates solvent consumption, which reportedly may also lower investment costs and energy requirements. Coconut oil is an edible oil extracted from the endosperm of the coconut (Cocos nucifera) using various methods. In this report, the coconut oil extraction from coconut meat rasp using pineapple enzymes was studied and compared with commercial enzymatic oil extraction. Pineapple enzymes were extracted from fresh pineapple fruit. The effectiveness of pineapple enzyme extract on coconut oil extraction from coconut meat rasp was studied at different enzyme concentrations of 0.5 to 2% (W/W) and pH of 4.5 to 7.5, with incubation times of 2 to 8 hr at 40 to 70°C, respectively. The commercial technical enzymes used in this study were from different companies (Valley enzyme®, Novozyme®, and AB-enzyme®). Valley enzyme® at 1% (w/w) enzyme concentration and 1:1 enzyme solution to coconut rasp ratio was found to be the most effective commercial enzyme in comparison with Novozyme® and AB-enzyme®. The optimum conditions for coconut oil extraction from coconut meat rasp using pineapple enzyme were at 1% (W/W) enzyme concentration, pH 6.5, 60°C, and 6 hr, while the optimum conditions for Valley enzyme were at pH 5.5, 60°C, and 6 hr. Oil yield obtained from Valley enzyme® and pineapple enzyme extract under optimum conditions were 21.89% and 20.50%, respectively. The oil yield obtained from fresh coconut rasp using pineapple enzyme extract was nearly comparable to that achieved with a commercial enzyme (Valley Enzyme) and was distinctly higher than that of the untreated control. Therefore, pineapple enzyme extract shows promise as a natural alternative to commercial enzymes for coconut oil extraction.

Use of Pulsed Electric Field (PEF) for Inactivation of Yeast S. cerevisiae in Apple Juice

2026-06-16T09:06:39+00:00

Although conventional thermal pasteurisation assures the safety of food products, it can also negatively affect sensorial acceptance. Therefore, alternative pasteurisation processes are intensively investigated. The application of electrical fields to biological cells induced electrical charges on the cell membrane. Pulsed electric field (PEF) is a non-thermal food preservation method as an alternative to the traditional thermal method. The effect of pulsed electric field (PEF) on the inactivation of yeast Saccharomyces cerevisiae (S. cerevisiae) in apple juice containing different sugar concentrations was studied. The results of this study have shown that, using PEF, the yeast could be inactivated at room temperature (20°C). The inactivation effect of PEF was dependent on pulse number, field strength, and pulse width as well as sugar concentration. Pulse numbers less than 20 pulses (at 12.6 kV/cm) had no or very little effect on yeast inactivation. Increasing the pulse number up to 400 pulses resulted in about 3 logs of yeast inactivation. The most important factor for yeast inactivation was the field strength (10 to 30 kV/cm). Whereas, at 10.2 kV/cm and 100 pulses, less than 2 logs yeast inactivation could be achieved, was the yeast inactivation at filed strength of 30kV/cm and 100 pulses about 5 logs. At a given field strength and pulse number, a longer pulse width (1 to 2.5 µF) affected the inactivation of microorganisms positively. In contrast, increasing the sugar concentration in apple juice to higher than 20% negatively affected the inactivation of yeast during PEF treatment at given treatment conditions. The study concluded that PEF treatment is a suitable non-thermal method for the inactivation of liquid foods.

The Impact of Diabetes Prevalence on Economic Growth: Evidence from a Nonlinear Analysis

2026-06-16T10:10:22+00:00

Diabetes, a non-communicable and chronic disease, occurs when the pancreas is unable to produce sufficient insulin in the body or when the body cannot effectively use the insulin it produces. The prevalence of diabetes has been increasing in both developed and developing nations in recent times, and many people remain ignorant of the disease and its management. This has led to an alarming mortality rate worldwide. In 2014, 8.5% of adults aged 18 years and older had diabetes. By 2019, diabetes was the direct cause of 1.5 million deaths, and 48% of all deaths due to diabetes occurred before the age of 70 years. Such a situation poses a great challenge globally, as high mortality rates resulting from diabetes lead to the loss of human capital that would have otherwise contributed to economic growth and development.

This study analyses the prevalence of diabetes and its indirect effect on economic growth, emphasising the need for effective management as a cornerstone for reducing its high mortality incidence worldwide. The study employed secondary data collected from reliable sources. The data were cross-sectional, drawn from randomly selected countries across the world. The methodology applied a non-linear model to examine the relationships among variables, while the methods of analysis included the Ordinary Least Squares (OLS) model and marginal effects estimation. The theoretical framework is grounded in the Endogenous Growth Theory, which posits that economic growth is driven by human capital accumulation, productivity improvements, and innovation.

The findings reveal that the estimated adult diabetes population (EADP) has a negative impact on economic growth (GDP per capita) when diabetes-related deaths (DDR) are at their mean and minimum values, and a positive impact when DDR is at its maximum value. This implies that at high diabetes-related mortality rates, economic growth appears to improve because the economic burden associated with diabetes decreases. Furthermore, EADP shows a positive relationship with GDP per capita when the cost per person with diabetes (CPPD) is at its mean and maximum values, and a negative relationship when CPPD is at its minimum value. This indicates that in higher-income settings, diabetes prevalence may increase alongside greater healthcare expenditure and lifestyle-related risk factors, whereas in lower-expenditure contexts, the burden of disease may be associated with weaker economic performance, potentially due to constrained healthcare financing and limited access to care. Overall, EADP is negatively associated with GDP per capita when DDR is at its mean and minimum levels and when CPPD is at its minimum level. These findings highlight the complex and context-dependent relationship between diabetes burden and economic performance.

In conclusion, the study underscores the multidimensional economic implications of diabetes. It recommends that governments adopt comprehensive public health strategies combining regulation of unhealthy diets and alcohol consumption, population-wide health education, and promotion of physical activity to reduce diabetes prevalence. Although this study relies on cross-sectional secondary data and employs OLS and marginal effects estimation, future research may consider panel data approaches, instrumental variable techniques, or alternative econometric frameworks to further validate and extend these findings.

Impact of Strategic Plan Implementation on Management and Production of Quality Health Data: Evidence from Gambia

2026-06-24T10:09:39+00:00

Background: Strategic plan implementation involves the execution of strategies and actions outlined in organisational strategic plans to achieve desired goals and objectives. It is expected to enhance the timeliness of health data collection and reporting processes.

Aims: This study assesses the impact of Strategic Plan Implementation on the management and production of quality Health Data in the Gambia. Specifically, it examines its effect on the timeliness of health data collection and reporting, as well as the reliability and accuracy of health data.

Methodology: The study focuses on health professionals engaged in health data management within the Gambia's health sector. A quantitative research design was adopted, and a purposive sampling technique was applied to determine sample size. The sample size consists of all 54 targeted individuals. The questionnaires were administered electronically to the selected participants using online Google survey forms. Descriptive analysis was applied to Quantitative data collected through a questionnaire using a statistical software called SPSS V27, while regression analysis was employed to examine relationships between independent and dependent variables.

Results: The research findings revealed considerable diversity in perceptions regarding the timeliness of health data collection and reporting, influenced by socio-demographic variables such as gender, age, professional role, and years of experience. Perceptions of improvements in data accuracy and consistency have emerged since the strategic plan's implementation, indicating that effective interventions resonate across diverse groups. However, a significant proportion of respondents reported challenges in implementing the strategic plan, including lack of resources (66.7%), inadequate support from leadership (44.4%), data quality issues (59.3%), and regulatory or compliance barriers (55.6%). Overall, the results revealed that the implementation of a well-structured strategic plan significantly impacts the management and production of quality health data.

Conclusion: The positive outcome of the study not only underscores the effectiveness of strategic planning in improving data practices but also highlights its potential to strengthen health systems in The Gambia, ultimately contributing to better healthcare delivery and informed decision-making. The results suggest further avenues for research and policy development aimed at optimising health data management practices in similar contexts.

Methane Mitigation in Ruminant Production Systems: Feed Additives, Rumen Microbiome Engineering, and Low-Emission Livestock Farming

2026-07-02T09:16:49+00:00

Enteric methane from cattle, sheep, and goats remains one of the most challenging agricultural emissions to reduce because it is produced by a normal microbial process that supports fibre digestion and hydrogen disposal in the rumen. At the same time, methane represents an energy loss to the animal and is a significant source of climate forcing from livestock systems. The review also explores how metagenomics and other omics technologies have shifted the view of methane from a simple output of methanogen abundance to an emergent property of microbial networks, offering new opportunities for selective breeding, ecological reprogramming, and precise intervention. Over the past decade, methane mitigation science has evolved from basic proof-of-concept strategies to a broader range of options, including targeted feed additives, manipulation of rumen microbial ecology, early-life programming, host–microbiome selection, and low-emission management at the farm level. This review was developed from a structured literature search conducted in Web of Science, Scopus, PubMed and Google Scholar. The principal search period covered January 2005 to March 2026, with earlier seminal papers screened only when necessary to clarify biological mechanisms. This review summarises the current understanding of three interconnected themes: feed additives, rumen microbiome engineering, and low-emission livestock farming. Special focus is given to 3-nitrooxypropanol, red macroalgae based on Asparagopsis, nitrate, tannins, essential oils, and probiotic approaches, with discussions on their effectiveness, durability, practical limitations, and safety. Beyond the rumen, sustained mitigation requires integrating animal productivity, forage quality, health, longevity, manure management, measurement systems, and economic pathways for adoption. The most credible route to low-emission ruminant production is therefore not reliant on a single technology but on a layered approach in which methane inhibitors, diet design, microbiome-aware management, and system efficiency work together. Ultimately, the review concludes that meaningful and lasting mitigation can already be achieved in many intensive systems, but broader adoption will require region-specific solutions, stronger evidence from long-term commercial studies, reliable monitoring systems, and careful management of trade-offs that affect animal performance, product quality, supply chains, and environmental outcomes.