Post-pandemic Education Strategy: Framework for Artificial Intelligence-Empowered Education in Engineering (AIEd-Eng) for Lifelong Learning

Megahed, Naglaa A.; Abdel-Kader, Rehab F.; Soliman, Heba Y.

doi:10.1007/978-3-031-03918-8_45

Cited by 18 publications

(15 citation statements)

References 43 publications

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“…By action of the AI, the personalization of learning paths produces tangible effects on students' learning processes, and this is by an almost revolutionary reconfiguration of the teaching strategies and methods implemented by teachers (Su and Yang, 2022). With the help of tools and techniques such as smart platforms, big data, cloud computing, machine learning, and natural language processing (Litman, 2016;Asgari and Antoniadis, 2021;Liu M. et al, 2022;Megahed et al, 2022;Dimitriadou and Lanitis, 2023)the teacher can implement comprehensive student monitoring. Thanks to these tools, the teacher is able to collect, represent and analyze in-depth data on students' learning behavior, their learning attitudes and styles, the educational needs of each student and the mutual differences between personal learning paths.…”

Section: Teaching Methods and Strategiesmentioning

confidence: 99%

“…Similarly, chatbots and robots can take over most of the interaction related to content and teaching materials and resources (Megahed et al, 2022;Timofeeva and Dorofeeva, 2022;Zhou, 2022). They can automatically answer the most frequent or repetitive questions, enhancing interactions with the teacher and allowing them to become more connected to students' learning strategies and individual needs.…”

Section: Teacher-student Interactionmentioning

confidence: 99%

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Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

et al. 2023

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Through a systematic analysis of the literature, this study analyzes the change in the teacher's role triggered by the integration of AI into educational systems. The picture offered by the systematic analysis of the literature conducted in this study reveals a less than total awareness of the urgency with which the challenges imposed by AI in the educational field must be addressed. We propose a manifesto to guide the evolution of the teachers' role according to the paradigm shift concept proposed by Kuhn in the scientific field.

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Section: Teaching Methods and Strategiesmentioning

confidence: 99%

Section: Teacher-student Interactionmentioning

confidence: 99%

Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

et al. 2023

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“…The Third Industrial Revolution significantly impacted the digital age, as production was mechanized, and information, and subsequently technology, became more widely used. Subsequently, 21 st -century advancements brought along the Fourth Industrial Revolution, and the focus has shifted to big data and AI applications (Abo El-Einen et al, 2015;Arfanuzzaman, 2021;Megahed, 2017;Megahed et al, 2022). As a multidisciplinary phenomenon, AI can be used for forecasting power demand and generation, optimizing the maintenance and use of energy assets, gaining a better understanding of energy usage patterns, and making systems more stable and efficient (Elzeni et al, 2021;Hassan et al, 2022;Williams, 1983).…”

Section: Ai Technology and Applicationsmentioning

confidence: 99%

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

Hassan

Megahed²

2022

A&J

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Introduction:Climate change and global warming are among the greatest challenges facing the world today. A newconcept, known as urban resilience, has been developed in response. There are various approaches to urban resilience. Among them, is the urban energy resilience (UER) approach, which poses a considerable challenge. Machine learning (ML), as an application of artificial intelligence (AI), provides powerful and affordable computing resources, large-scale datamining, advanced algorithms, and real-timemonitoring. However, veryfewstudies have investigated howsuch aspectscan be integrated into urban resilience in general, and UER in particular. Purpose of the study: The study develops an integrative framework that can improve UER, based on ML methods. Methodology: We carried out a bibliometric analysisand a systematic review of UER in accordance with AI concepts, models, and applications. Results:The findings of this study were used to create an integrative framework, based on three hierarchical phases, which effectively addressed the main capabilities of UER, identified its priorities, and shed light on how ML can benefit UER as a whole. Novelty:The framework developed in this study also offers insights in integrating ML methods into UER as strategically as possible, especially in the context of climate change and urban energy systems. This framework can serve as reference for specialistsand decision-makers aiming to expand AI and ML applications to optimize UER.

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“…This overlaps with the concept of inclusivity. (Gupta and Chen, 2022;Deho et al, 2022;Megahed, Abdel-Kader and Soliman, 2022;Casas-Roma and Conesa, 2021;Latham and Goltz, 2019;Tlili et al, 2019).…”

mentioning

confidence: 99%

“…Privacy is concerned about constant surveillance arising from AI use (Megahed, Abdel-Kader and Soliman, 2022).…”

mentioning

confidence: 99%

Artificial Intelligence in Today’s Education Landscape: Understanding and Managing Ethical Issues for Educational Assessment

Lim

Gottipati

Cheong

2023

Preprint

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In a societal institution as fundamental as education, when teaching practitioners or researchers apply artificial intelligence in academic processes such as assessments, it is important to study the divide between what may be ethically permissible and not permissible. This study applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped ten key ethical principles to five research archetypical domains, and reviewed the contribution and intensity of these ethical principles in each thematic domain. The study extended this review, by mapping out ethics programs and activities that can be applied in practice, alongside their relevant underpinning theories. This study provides a comprehensive treatment of this subject matter to date. We hope the findings of this research can provide researchers and practitioners the insights into the application methods of AI in assessments, and in particular, in terms of their intertwined ethical challenges and how these challenges may be addressed, for follow up studies.

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Post-pandemic Education Strategy: Framework for Artificial Intelligence-Empowered Education in Engineering (AIEd-Eng) for Lifelong Learning

Cited by 18 publications

References 43 publications

Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

Artificial Intelligence in Today’s Education Landscape: Understanding and Managing Ethical Issues for Educational Assessment

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