Guaranteeing Correctness of Machine Learning Based Decision Making at Higher Educational Institutions

Nauman, Muhammad; Akhtar, Nadeem; Alhudhaif, Adi; Alothaim, Abdulrahman

doi:10.1109/access.2021.3088901

Cited by 17 publications

(11 citation statements)

References 49 publications

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“…Table 4 summarizes the papers analysed, including information about the AI applied, who used the AI, and what main benefits it provided. In brief, the results revealed that the main advantages of AI applications in higher education were to improve educational outcomes [3,7,12,16,18,22,27,29,30,32], to enhance institutional decision-making [1,8,14,21,26], and to advance institutional systems [2,10,23]. Moreover, the end users benefiting from the AI applications mainly were higher education institutions (n=11), instructors (n=9), and college students (n=6).…”

Section: Advantages and Disadvantages Of Using Aimentioning

confidence: 99%

A systematic review of research on artificial intelligence in higher education: Practice, gaps, and future directions in the GCC

Fadlelmula,

Qadhi

2024

JUTLP

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Acknowledging its potential on diversifying economy and attaining sustainable development, the Gulf Cooperation Council (GCC) countries, comprising of Bahrain, Kuwait, Oman, Qatar, Kingdom of Saudi Arabia, and United Arab Emirates, have been investing heavily on digital transformation and keeping pace with technological advancements. In particular, over the last years, with the unified efforts on transitioning to a knowledge society and enhancing educational outcomes, GCC countries have been demonstrating a strong dedication on integrating artificial intelligence in education (AIED). This systematic review investigates characteristics of artificial intelligence (AI) research in the region, identifying advantages and disadvantages of AI utilization in higher education, and exploring main issues accompanied with possible directions for the future. In the Scopus database, 32 studies were analyzed, all open access documents affiliated to a GCC country, having artificial intelligence and higher education, or related terminologies as keywords. Results revealed that AI applications were beneficial for institutions to improve educational outcomes, assist in decision-making, and advance institutional systems. No study reported negativity resulting from AI practices. However, important barriers were identified that hinder the full deployment of AI in higher education, including poor technology skills, inadequate technology infrastructure, resistance in leveraging traditional approaches in education, and challenges related to structural complexity of Arabic language. Future directions are proposed, offering opportunities for practitioners and research potential for scholars.

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Section: Advantages and Disadvantages Of Using Aimentioning

confidence: 99%

A systematic review of research on artificial intelligence in higher education: Practice, gaps, and future directions in the GCC

Fadlelmula,

Qadhi

2024

JUTLP

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“…Computational algorithms and automated decision support systems (DSS) may enhance this manual process. DSSs have shown tremendous success in automating processes and supporting human decisions in different domains, such as in higher education, health care [ 22 ], education [ 23 ], agriculture [ 24 , 25 ], and finance [ 26 ]. These DSSs benefit from artificial intelligence and machine learning techniques that can identify patterns and use them in their decision-making process.…”

Section: Discussionmentioning

confidence: 99%

Systems that evaluate international equivalency in health-related professions: a scoping review with a focus on Canada

Lafave,

Amannejad,

Mammadova

et al. 2023

Hum Resour Health

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Health workforce planning has become a significant global problem considering there are estimates of an 18 million healthcare provider shortfall by 2030. There are two mechanisms to address healthcare worker shortages: (1) domestic education of those professions and (2) integration of internationally educated health professionals. Integration of internationally educated health professionals into the Canadian healthcare system requires: (1) reductions in systemic and administrative barriers and (2) development, testing, and implementation of credential equivalency recognition systems. The goal of this scoping review was to identify systems that are employed to determine credential equivalency, with a focus on Canada. The scoping review was carried by employing: (1) a systematic literature search (9) and (2) a website and grey literature Google search of professional governing bodies from a selection of medical/allied healthcare professions, but also other non-medical professions, such as law, engineering and accounting. Seven databases were searched to identify relevant sources: MEDLINE, CINAHL Plus with Full Text, PsycINFO, SPORT Discus, Academic Search Complete, Business Source Complete, and SCOPUS. The search strategy combined keyword, text terms, and medical subject headings (MeSH) and was carried out with the help of a health sciences librarian. Seven articles were included in the final manuscript review from the following professions: nursing; psychology; engineering; pharmacy; and multiple health professions. Twenty-four health-related professional governing body websites were hand searched to determine systems to evaluate international equivalency. There were many systems employed to determine equivalency, but there were no systems that were automated or that employed machine-learning or artificial intelligence to guide the evaluation process.

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“…The simulated scenarios demonstrate the effectiveness of the strategy. Nauman et al (2021) asserts that organizations may gather and store vast volumes of student data using cutting-edge technologies. This quantity of knowledge might aid in making a decision.…”

Section: Detailed Review Of Existing Decision Recommendation Modelsmentioning

confidence: 99%

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Gonnade,

Ridhorkarb

2024

Multidiscip. Rev.

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Decision recommendation models allow researchers and process designers to identify and implement high-efficiency processes in ambiguous situations. These models perform multipara metric analysis on the given process sets to recommend high-quality decisions that assist in improving process-based efficiency levels. A wide variety of models have been proposed by researchers for the implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scopes. Thus, it is difficult for researchers and process designers to identify optimal models for functionality-specific use. Therefore, they tend to validate multiple process models, which increase deployment time, cost and complexity levels. To overcome this ambiguity, a detailed survey of different decision process recommendation models is discussed in this text. Fuzzy logic, analytical hierarchical processing (AHP), the technique for order performance by similarity to ideal solution (TOPSIS), and their variants are highly useful for the recommendation of efficient decisions. Based on this survey, readers will be able to identify recently proposed decision recommendation models and functionality-specific models for their deployments. To further assist in the model selection process, this paper compares the reviewed models in terms of their computational complexity, recommendation efficiency, delay needed for recommendation, scalability and contextual accuracy. Based on this comparison, readers will be able to identify performance-specific models for their deployments. This paper also evaluates a novel decision recommendation rank metric (DRRM), which combines these parameters, to identify models that can optimally perform w.r.t. multiple process metrics. Referring to this parameter comparison, readers will be able to identify optimal recommendation models for enhancing the performance of their decision recommendations under real-time scenarios.

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Guaranteeing Correctness of Machine Learning Based Decision Making at Higher Educational Institutions

Cited by 17 publications

References 49 publications

A systematic review of research on artificial intelligence in higher education: Practice, gaps, and future directions in the GCC

A systematic review of research on artificial intelligence in higher education: Practice, gaps, and future directions in the GCC

Systems that evaluate international equivalency in health-related professions: a scoping review with a focus on Canada

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

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