Detecting change and dealing with uncertainty in imperfect evolutionary environments

Mujtaba, Hasan; Kendall, Graham; Baig, Abdul Rauf; Özcan, Ender

doi:10.1016/j.ins.2014.12.053

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Behind these efforts is the notion that the analyses will better understand student learning progress, leading to interventions to improve teaching and learning [42]. In accordance with Mujtaba et al [43], the concept of imperfection in evolutionary settings is referred to as Imperfect Evolutionary Systems (IES) from now on. IESs are dynamic settings that are unpredictable and chaotic (changes can occur quickly at any time) and present several obstacles to the learning of the persons who live in them.…”

Section: Learning Outcomementioning

confidence: 95%

Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes

Alotaibi

Alshehri

2023

Sustainability

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Within the framework of the ongoing implementation of the 2030 Vision for Comprehensive Development of Higher Education in Saudi Arabia, the integration of artificial intelligence (AI) has emerged as a pivotal objective for the country’s numerous higher education institutions. This study aims to examine the opportunities and challenges that arise from the adoption of AI-based learning outcomes in Saudi Arabia’s higher education institutes. Moreover, the research also investigates the contribution of major higher education institutes in Saudi Arabia to the field of AI-based learning outcomes. To gather relevant literature, the Scopus and Web of Science databases were utilised, resulting in the selection of fifty-five studies for final analysis. The study employed the PRISMA statement 2020 for records filtration and utilised VOS viewer software to classify the literature on AI-based learning outcomes in Saudi Arabian universities. Through detailed analysis, three significant data streams were identified and examined. The findings indicate that AI is in a nascent stage within the realm of learning, and it has become an undeniable reality for higher education institutions. Embracing this transformative technology is crucial for meeting future learning challenges, and it is imperative that all students acquire the necessary technical skills to interact with and create artificial intelligence in the future. According to the findings, AI has the potential to address significant educational challenges, revolutionise teaching and learning methodologies, and accelerate progress toward the Saudi 2030 objectives. However, the study also highlights certain challenges associated with the implementation of AI-based learning in the higher education context of Saudi Arabia, emphasising the need for teachers to acquire new technological skills to effectively utilise AI pedagogically.

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Section: Learning Outcomementioning

confidence: 95%

Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes

Alotaibi

Alshehri

2023

Sustainability

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Integrated production and distribution scheduling in distributed hybrid flow shops

Qin

Teng

et al. 2021

Memetic Comp.

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Biasing the transition of Bayesian optimization algorithm between Markov chain states in dynamic environments

Kaedi

Ghasem-Aghaee

Ahn

2016

Information Sciences

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Detecting change and dealing with uncertainty in imperfect evolutionary environments

Abstract: (2015) Detecting change and dealing with uncertainty in imperfect evolutionary environments. Information Sciences, 302 . pp. 33-49.

Cited by 3 publications

References 32 publications

Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes

Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes

Integrated production and distribution scheduling in distributed hybrid flow shops

Biasing the transition of Bayesian optimization algorithm between Markov chain states in dynamic environments

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