AI technologies for education: Recent research &amp; future directions

Zhang, Ke; Aslan, Ayşe

doi:10.1016/j.caeai.2021.100025

Cited by 366 publications

(171 citation statements)

References 67 publications

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“…These results are consistent with the findings in the current literature where various authors [45][46][47][48][49][50][51] found that AI tools can be used to attain sustainability by improving student grades, identifying learning styles, and recommending personalized learning styles to students based on their performance in particular subjects, etc. On the other hand, two participants mentioned that, since AI has not yet been fully adapted in various sectors, human interaction is always beneficial.…”

Section: Attitudes and Beliefssupporting

confidence: 91%

Teacher Views on Teaching Sustainability in Higher Education Institutes in Australia

et al. 2022

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Higher education for sustainable development (HESD) plays a key role in achieving the United Nations’ 17 Sustainable Development Goals (SDGs). This research study specifically examined HESD in terms of eLearning initiatives in Australian private higher education providers from the perspective of teachers. A qualitative structured interview method was adopted wherein 10 teachers were interviewed in order to gain an understanding of their general knowledge of HESD, their attitudes and experience towards HESD, their teaching practices related to HESD, and their understandings of strategy as well as planning initiatives for their institution. The main findings suggest that (1) teachers in private higher education providers tend to have a limited knowledge of sustainability concepts and limited experience in teaching sustainability; (2) eLearning can be a valuable approach in teaching sustainability, but this approach presents teachers with challenges such as student engagement; and (3) private higher education providers require proper resources and governance frameworks in order for any sustainability initiative to be successful. This research highlights the resourcing aspect of private higher education providers in training staff, developing learning materials, and developing practical guidelines to achieve the SDGs by 2030.

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Section: Attitudes and Beliefssupporting

confidence: 91%

Teacher Views on Teaching Sustainability in Higher Education Institutes in Australia

et al. 2022

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“…In recent years, deep learning using deep artificial neural networks has gained increasing attention for its image recognition abilities. As a result, artificial intelligence (AI) has been widely applied to handle problems directly related to humans, such as intelligent education [7], medical [8,9], network security [10], users' behavior and the environment [11,12]. Thus, their application has improved our society.…”

Section: Related Workmentioning

confidence: 99%

Eye Strain Detection During Online Learning

Thao¹,

Cuong²,

Hung³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The recent outbreak of the coronavirus disease of 2019 (Covid-19) has been causing many disruptions among the education systems worldwide, most of them due to the abrupt transition to online learning. The sudden upsurge in digital electronic devices usage, namely personal computers, laptops, tablets and smartphones is unprecedented, which leads to a new wave of both mental and physical health problems among students, for example eye-related illnesses. The overexposure to electronic devices, extended screen time usage and lack of outdoor sunlight have put a consequential strain on the student's ophthalmic health because of their young age and a relative lack of responsibility on their own health. Failure to take appropriate external measures to mitigate the negative effects of this process could lead to common ophthalmic illnesses such as myopia or more serious conditions. To remedy this situation, we propose a software solution that is able to track and capture images of its users' eyes to detect symptoms of eye illnesses while simultaneously giving them warnings and even offering treatments. To meet the requirements of a small and light model that is operable on low-end devices without information loss, we optimized the original MobileNetV2 model with depth-wise separable convolutions by altering the parameters in the last layers with an aim to minimize the resizing of the input image and obtained a new model which we call EyeNet. Combined with applying the knowledge distillation technique and ResNet-18 as a teacher model to train the student model, we have successfully increased the accuracy of the EyeNet model up to 87.16% and support the development of a model compatible with embedded systems with limited computing power, accessible to all students.

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“…The use of AI technologies to enhance students' learning experiences has gained increasing popularity, especially in the last decade (Chassignol et al, 2018;Aslan et al, 2019;Jia et al, 2020;Baker, 2021;Zhai et al, 2021;Zhang and Aslan, 2021). Intelligent gamebased learning systems (Lester et al, 2013;Richey et al, 2021) present significant benefits for practicing math concepts in smart spaces (Pires et al, 2019;Sun et al, 2021), specifically for early childhood education (Skene et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

NLU for Game-based Learning in Real: Initial Evaluations

Okur¹,

Sahay²,

Nachman³

2022

Preprint

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Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment of such conversational agents, improving the Natural Language Understanding (NLU) module of the goal-oriented SDS pipeline is crucial, especially with limited task-specific datasets. This study explores the potential benefits of a recently proposed transformer-based multi-task NLU architecture, mainly to perform Intent Recognition on small-size domain-specific educational game datasets. The evaluation datasets were collected from children practicing basic math concepts via play-based interactions in game-based learning settings. We investigate the NLU performances on the initial proof-of-concept game datasets versus the real-world deployment datasets and observe anticipated performance drops in-the-wild. We have shown that compared to the more straightforward baseline approaches, Dual Intent and Entity Transformer (DIET) architecture (Bunk et al., 2020) is robust enough to handle real-world data to a large extent for the Intent Recognition task on these domain-specific in-the-wild game datasets.

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AI technologies for education: Recent research & future directions

Cited by 366 publications

References 67 publications

Teacher Views on Teaching Sustainability in Higher Education Institutes in Australia

Teacher Views on Teaching Sustainability in Higher Education Institutes in Australia

Eye Strain Detection During Online Learning

NLU for Game-based Learning in Real: Initial Evaluations

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