The Evolution of AI-Driven Educational Systems during the COVID-19 Pandemic

Pantelimon, Florin-Valeriu; Bologa, Răzvan; Toma, Andrei; Posedaru, Bogdan-Ștefan

doi:10.3390/su132313501

Cited by 32 publications

(20 citation statements)

References 17 publications

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“…This rapid implementation of digitized learning has directed more attention toward AI-driven education such as adaptive intelligent tutoring systems, virtual reality (VR), augmented reality (AR), holograms and robotics [ 37 ]. It is worth mentioning that in the pre-COVID-19 era, AI applications in education were merely used in experimental projects or to detect plagiarism [ 39 ].…”

Section: Lessons Learnt During the Covid-19 Pandemicmentioning

confidence: 99%

“…During COVID-19 pandemic, AI-assisted learning has been proven to positively impact different aspects of the education process, including tutoring, e.g., adaptive intelligent tutoring systems, learners’ enrollment, assessment and administration tasks [ 39 , 40 ]. It has been shown to improve learners’ engagement, their knowledge retention and enabled them to gain and nurture new skills and aspects of human intelligence [ 39 , 41 ]. Currently, there is a noticeable increased interest in the adoption of AI-driven educational systems in both highly and less developed countries [ 39 ].…”

Section: Lessons Learnt During the Covid-19 Pandemicmentioning

confidence: 99%

“…It has been shown to improve learners’ engagement, their knowledge retention and enabled them to gain and nurture new skills and aspects of human intelligence [ 39 , 41 ]. Currently, there is a noticeable increased interest in the adoption of AI-driven educational systems in both highly and less developed countries [ 39 ]. Scientists are predicting that, post-COVID-19, the use of digitized learning will continue and will be blended with in-person education due to the multi-level benefits it provided during the pandemics [ 42 ].…”

Section: Lessons Learnt During the Covid-19 Pandemicmentioning

confidence: 99%

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Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

Abdellatif

Mushaiqri

Albalushi

et al. 2022

IJERPH

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Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.

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Section: Lessons Learnt During the Covid-19 Pandemicmentioning

confidence: 99%

Section: Lessons Learnt During the Covid-19 Pandemicmentioning

confidence: 99%

Section: Lessons Learnt During the Covid-19 Pandemicmentioning

confidence: 99%

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Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

Abdellatif

Mushaiqri

Albalushi

et al. 2022

IJERPH

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“…Education researchers have studied AI applications in education (AIEd) for decades to enrich learning and teaching activities (Luckin et al, 2016 ). Given the shift from in-person to remote schooling brought about by the COVID-19 pandemic (Pantelimon et al, 2021 ), scholars have focused on creating various AIEd applications such as virtual assistants for teaching course content, automated grading and feedback, and learner-tracking systems (Dignum, 2021 ; Zawacki-Richter et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Development research on an AI English learning support system to facilitate learner-generated-context-based learning

Lee¹,

Kim²,

Sung³

2022

Education Tech Research Dev

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For decades, AI applications in education (AIEd) have shown how AI can contribute to education. However, a challenge remains: how AIEd, guided by educational knowledge, can be made to meet specific needs in education, specifically in supporting learners’ autonomous learning. To address this challenge, we demonstrate the process of developing an AI-applied system that can assist learners in studying autonomously. Guided by a Learner-Generated Context (LGC) framework and development research methodology (Richey and Klein in J Comput High Educ 16(2):23–38, https://doi.org/10.1007/BF02961473, 2005), we define a form of learning called “LGC-based learning,” setting specific study objectives in the design, development, and testing of an AI-based system that can facilitate Korean students’ LGC-based English language learning experience. The new system is developed based on three design principles derived from the literature review. We then recruit three Korean secondary-school students with different educational backgrounds and illustrate and analyze their English learning experiences using the system. Following this analysis, we discuss how the AI-based system facilitates LGC-based learning and further issues to be considered for future research.

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“…During the COVID-19 pandemic, online education proved to be important to move the gears of education around the world [Cao et al 2021, Penteado andFornazin 2021]. Although this type of educational approach have presented difficulties and pedagogical problems [Santos et al 2020], the number of Online Intelligent Tutoring System (ITS) users increased during the period of social distancing due to the pandemic [Pantelimon et al 2021, Akyuz 2020. Such an increase in the amount of users led to an increase in the amount of data generated by these students, thus boosting existing research on Artificial Intelligence (AI) and Big Data applied to education [Liu et al 2021].…”

Section: Introductionmentioning

confidence: 99%

Towards Interpretability of Attention-Based Knowledge Tracing Models

Souza²,

Bernardino

et al. 2022

Anais Do XXXIII Simpósio Brasileiro De Informática Na Educação (SBIE 2022)

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Knowledge Tracing (KT) models based on attention mechanisms have demonstrated in literature the capability to predict student performance more accurately than previous models in some datasets. However, they fail to directly infer student knowledge. In this paper, we apply a proposed extension already seen in KT literature in order to infer latent knowledge to these models. We apply the extension to four different attention-based KT models, to investigate whether these models can better infer the knowledge outside the learning system than previous models. We find that attention-based models can generate better knowledge estimate correlations with student’s scores than the previous models.

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The Evolution of AI-Driven Educational Systems during the COVID-19 Pandemic

Cited by 32 publications

References 17 publications

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

Development research on an AI English learning support system to facilitate learner-generated-context-based learning

Towards Interpretability of Attention-Based Knowledge Tracing Models

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