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An Experimental Study of The Efficacy of Prompting Strategies In Guiding ChatGPT for A Computer Programming Task Mnguni, Nompilo Makhosi; Nkomo, Nkululeko; Maguraushe, Kudakwashe; Mutanga, Murimo Bethel
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.783

In the rapidly advancing artificial intelligence (AI) era, optimizing language models such as Chatbot Generative Pretrained Transformer (ChatGPT) for specialised tasks like computer programming remains a mystery. There are numerous inconsistencies in the quality and correctness of code generated by ChatGPT in programming. This study aims to analyse how the different prompting strategies; text-to-code and code-to-code, impact the output of ChatGPT's responses in programming tasks. The study adopted an experimental design that presented ChatGPT with a diverse set of programming tasks and prompts, spanning various programming languages, difficulty levels, and problem domains. The generated outputs were rigorously tested and evaluated for accuracy, latency, and qualitative aspects. The findings indicated that code-to-code prompting significantly improved accuracy, achieving a 93.55% success rate compared to 29.03% for text-to-code. Code-to-code prompts were particularly effective across all difficulty levels, while text-to-code struggled, especially with harder tasks. Based on these findings, computer programming students need to appreciate and comprehend that ChatGPT prompting is essential for getting the desired output. Using optimised prompting methods, students can achieve more accurate and efficient code generation, enhancing the quality of their code. Future research should explore the balance between prompt specificity and code efficiency, investigate additional prompting strategies, and develop best practices for prompt design to optimize the use of AI in software development.

Artificial Intelligence Chatbots in Education: Academics Beliefs, Concerns and Pathways for Integration Ndlovu, Belinda; Ndlovu, Sharmaine; Dube, Sibusisiwe; Maguraushe, Kudakwashe
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10805

Although globally there are mixed perceptions regarding the academic integrity of chatbots, existing research has mainly focused on developed nations, neglecting the unique perspectives of academics in developing countries, with different contextual, environmental, and technological settings. This study presents lecturers’ perceptions of using Artificial Intelligence (AI) chatbots in education. Guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research collected quantitative and qualitative data from 140 lecturers and three administrators from a STEM-based Zimbabwean university. The research confirmed that performance expectancy (belief in improved efficiency and personalised learning) and perceived value and social influence drive adoption. Contrary to previous studies, there was no significant link between effort expectancy (reduced workload) and chatbot use. Demographics like gender, age, and qualifications did not impact chatbot use. Academics were cautiously optimistic, recognising benefits like personalised learning and routine task management but concerned about ease of use, technical expertise, and ethical considerations. To effectively integrate AI chatbots into higher education processes, there is a need for funding, technical support, training, strengthening IT infrastructure and establishing frameworks for responsible use. Emphasising efficiency, personalisation, and robust support will help overcome barriers and maximise AI chatbots’ potential in education.

Managing Diabetes Using Machine Learning and Digital Twins Hadebe, Sanele; Ndlovu, Belinda; Maguraushe, Kudakwashe
Indonesian Journal of Innovation and Applied Sciences (IJIAS) Vol. 5 No. 2 (2025): June-September
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijias.v5i2.1981

Diabetes is a major public health problem worldwide, and early diagnosis will remain pivotal for intervention and management. This Systematic Literature Review (SLR), therefore, attempts to explore the prospects of integrating Machine Learning (ML) and Digital Twins (DT) to enable diabetes treatment through prediction and patient-specific modeling. This SLR contributes to the body of literature by examining how ML and DTs are being applied in diabetes treatment, identifying the opportunities and challenges that exist, and determining which algorithms are most commonly used. In contrast to SLRs that have been reviewed previously, this study considers Digital Twin-based technological perspectives, along with algorithmic evaluations of ML models, to provide an overall view of the potential for combined use in diabetes care. Following PRISMA guidelines, 11 relevant studies were selected from major academic databases. The study identified Random Forests, Gradient-Boosted Decision Trees, K-Nearest Neighbors, Time Series and Structured Analysis, Regression-based algorithms, and Artificial Neural Networks as machine learning algorithms commonly used to predict diabetes risk. The integration of ML and DT for diabetes management enables the personalization of patient management through virtual representations, real-time monitoring of an individual's glucose levels, simulation of disease progression, and prediction of subsequent treatment steps for proactive and immediate decision-making. Through this collaboration, simulations of various situations are performed, and the interventions are optimized to correspond with unique human physiological profiles for better patient outcomes. Based on the results, policymakers must balance data quality and patient privacy.

Machine Learning and Explainable AI for Parkinson’s Disease Prediction: A Systematic Review Ndlovu, Belinda; Maguraushe, Kudakwashe; Mabikwa, Otis
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4837

Parkinson's disease is a movement disorder within the nervous system that impacts millions of people across the world. The standard diagnostic methods usually miss early subtle signs of disease which has driven research into Machine Learning (ML) and Explainable Artificial Intelligence (XAI) to develop better predictive models. Following PRISMA guidelines we analyzed 13 studies found in IEEE Xplore, PubMed and ACM concerning different ML methodologies for Parkinson’s disease prediction. Deep learning models using vocal and motor data achieve good accuracy but require more clinical trust and transparency due to their opaque "black-box" nature. SHAP and LIME act as XAI solutions that address transparency issues in model predictions by delivering understandable information regarding model outputs to all users. Current solutions show progress. However, there are multiple complications, including limited and unbalanced datasets alongside accuracy-explainability trade-offs which underline the need for extensive datasets, multidisciplinary teamwork and practical validation.

Framework for Enhancing Interoperability, Data Exchange, and Security in Healthcare through Blockchain Technology Muderere, Vimbai Alice; Ndlovu, Belinda; Maguraushe, Kudakwashe
The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i4.4950

The healthcare sector is changing, such as fragmentation issues, the sharing of data, and the security of protected health information. Traditional systems tend to work independently or in silos, resulting in disjointed patient records and system inefficiency. With more trusted healthcare providers, patients relying more on digital solutions than ever, the urgency for a consistent data management solution has never been greater. This systematic literature review (SLR) aims to investigate the existing framework, factors, opportunities and challenges of blockchain technology in healthcare systems. The integrative approach was done according to the PRISMA guidelines. A literature search was carried out on various electronic databases, including PubMed, IEE Xplore, and ACM Digital Library, which gave a total of 832 articles, to begin with. Based on set scale criteria, 18 studies were deemed relevant for analysis. The findings indicate that blockchain technology holds promise due to its ability to facilitate secure and easy data sharing through immutability, cryptographic methods, and the removal of centralized authorities. However, there is a challenge of interoperability, data exchange and security within the healthcare systems and other technologies. This study contributes to the body of knowledge by developing a conceptual framework that helps policymakers, researchers, and practitioners that act as guide to effectively implement blockchain technology in healthcare. The framework addresses key considerations of traditional systems, such as scalability, interoperability, security, and regulatory compliance, and offers a structured approach to resolving current challenges.

A Comparative Analysis of Machine Learning Techniques and Explainable AI on Voice Biomarkers for Effective Parkinson’s Disease Prediction Ndlovu, Belinda; Maguraushe, Kudakwashe; Mabikwa, Otis
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1172

Parkinson's disease (PD) is a neurological movement disorder that remains difficult to diagnose, although it affects millions globally. Early diagnosis can lead to more effective and improved patient outcomes. Diagnosis through traditional methods is subjective and often lacks transparency, raising concerns about reliability. In this study, the CRISP-DM framework was applied to compare eight ML algorithms, including Random Forest and Support Vector Machine (SVM). Recursive Feature Elimination (RFE) was used to preprocess, balance, refine the data and find the eight most predictive vocal features. With 195 recordings coming from the UCI Parkinson’s Speech Dataset, which contains voice measurements from 31 individuals (23 with PD and 8 healthy controls), Random Forest (Entropy) had the best performance (F₁ = 96.6%, ROC AUC = 0.98). Explainable AI tools (SHAP and LIME) were integrated, allowing both global and instance-level understanding of model predictions thereby identifying measures of pitch variability (MDVP: RAP, spread1, PPE) as key predictors of PD. This research contributes to the practical deployment of reliable, transparent PD prediction tools in real-world medical settings, supporting early diagnosis and improved patient care. This raises the issue of the urgent need to detect PD early among Africa's aging populations to help protect the cultural heritage contained in the voices of the elders. this research contributes to the practical deployment of reliable, transparent PD prediction tools in real-world medical settings, supporting early diagnosis and improved patient care.Future work should embark on validating these findings over much more varied cohorts, integrating additional data modalities (e.g., gait, imaging), and enhancing model robustness. Real-time speech analysis-based tools, in the end, will allow remote screening, early intervention, and tailored care.

Artificial Intelligence Chatbots in Education: Academics Beliefs, Concerns and Pathways for Integration Ndlovu, Belinda; Ndlovu, Sharmaine; Dube, Sibusisiwe; Maguraushe, Kudakwashe
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10805

Although globally there are mixed perceptions regarding the academic integrity of chatbots, existing research has mainly focused on developed nations, neglecting the unique perspectives of academics in developing countries, with different contextual, environmental, and technological settings. This study presents lecturers’ perceptions of using Artificial Intelligence (AI) chatbots in education. Guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research collected quantitative and qualitative data from 140 lecturers and three administrators from a STEM-based Zimbabwean university. The research confirmed that performance expectancy (belief in improved efficiency and personalised learning) and perceived value and social influence drive adoption. Contrary to previous studies, there was no significant link between effort expectancy (reduced workload) and chatbot use. Demographics like gender, age, and qualifications did not impact chatbot use. Academics were cautiously optimistic, recognising benefits like personalised learning and routine task management but concerned about ease of use, technical expertise, and ethical considerations. To effectively integrate AI chatbots into higher education processes, there is a need for funding, technical support, training, strengthening IT infrastructure and establishing frameworks for responsible use. Emphasising efficiency, personalisation, and robust support will help overcome barriers and maximise AI chatbots’ potential in education.

Blockchain Adoption in Healthcare: Enhancing Interoperability, Security and Data Exchange Muderere, Vimbai Alice; Ndlovu, Belinda; Maguraushe, Kudakwashe
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1267

Fragmented data across the healthcare industry increasingly impedes interoperability, compromises data security, and ultimately interferes with safe and quality patient care delivery. This research introduces a framework that uses blockchain technology to enhance interoperability and data exchange in healthcare environments. Leveraging qualitative methods,semi-structured interviews were held with fifteen health care practitioners at various facilities who gave their insights and perceptions of data sharing and blockchain technology. The findings were thematic and conceptualized through the Technology Acceptance Model, focusing on perceived ease of use and perceived usefulness, and the Technology-Organization-Environment framework that examined organizational support and regulatory compliance. Thematic analysis identified four main themes, including (i) factors influencing adoption: ease of use with four participants, usefulness with three participants, organizational support with two participants, regulatory compliance with two participants, and technical infrastructure with two participants. (ii)Application areas included patient data management, billing and payment, and remote patient monitoring; (iii) benefits such as a more effective decentralized system, safer storage of data, and patient empowerment. (iv)Challenges included privacy concerns, the costs of implementation and system failure, and patients' knowledge and stakeholders' digital literacy. The findings suggested that stakeholders knew the potential disruption to any blockchain system. However, major issues needed to be addressed before implementation. This research expands the conversation about innovative solutions to health care interoperability. It exposes potential ways to address the challenges to adoption. Recommendations for future research include examining the scalability and integration of blockchain technology across different healthcare environments and addressing the pressing need for empirical evidence regarding its real-world applications and impacts.

Advancing Inclusive Educational VR: A Bibliometric Study of Interface Design Maguraushe, Kudakwashe; Masimba, Fine; Chimbo, Bester
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1271

While virtual reality (VR) has shown transformative potential in education, its accessibility and inclusivity for learners with disabilities remain insufficiently explored. This study offers the first bibliometric mapping of educational VR interface design for inclusivity, analysing 4,735 documents from 1,714 sources (2020-2025) using Biblioshiny and VOSviewer. The analysis reveals a 13.22% annual publication growth rate, an average of 10 citations per document, and an international co-authorship rate of 25.85%, reflecting both rapid expansion and increasing collaboration. Dominant research themes include user experience, usability, and the metaverse, while underexplored areas such as cognitive accessibility and neurodiverse learners highlight emerging opportunities. The findings demonstrate a concentration of scholarly activity in North America and Asia, with limited representation from the Global South. Practically, the study informs developers on designing adaptive interfaces, guides educators in implementing inclusive VR pedagogies, and provides policymakers with evidence for promoting equitable digital learning ecosystems. By identifying trends, gaps, and collaboration patterns, this research advances the discourse on inclusive educational VR and underscores the need for interdisciplinary, AI-driven accessibility strategies that ensure equitable participation for all learners.

Integrating Human-Centered AI into the Technology Acceptance Model: Understanding AI-Chatbot Adoption in Higher Education Masimba, Fine; Maguraushe, Kudakwashe; Chimbo, Bester
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1316

Artificial intelligence (AI) is transforming education by enhancing assessments, personalizing learning, and improving administrative efficiency. However, the adoption of AI-powered chatbots in higher education remains limited, primarily due to concerns about trust, transparency, explainability, perceived control, and alignment with human values. While the Technology Acceptance Model (TAM) is commonly used to explain technology adoption, it does not fully address the challenges posed by AI systems, which require human-centered safeguards. To address this gap, this study extends TAM by incorporating Human-Centered AI (HCAI) principles—explainability, transparency, trust, and perceived control—resulting in the HCAI-TAM framework. An empirical study with 300 respondents was conducted using a structured English questionnaire, and regression analysis was applied to assess the relationships among variables. The model explained 65% (R² = 0.65) of the variance in behavioral intention and 55% (R² = 0.55) in usage behavior. The findings highlight that integrating HCAI principles into TAM enhances user adoption of AI chatbots in higher education, contributing both theoretically and practically.

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