Setting an agenda for urban AI adaptivity in urban planning and architecture e-learning

Joint Authors

Hindawi, Minnah Allah
Kayid, Sumayyah
Ghuz, Lamya
al-Buhayri, Farah
Ghunaym, Ahmad

Source

Journal of Engineering Research

Publisher

Tanta University Faculty of Engineering

Publication Date

2022-10-31

Country of Publication

Egypt

No. of Pages

9

Main Subjects

Telecommunications Engineering
Architecture Engineering

Topics

• Higher education
• Artificial intelligence
• Electronic learning
• Urban planning
• Egypt
• Architecture

English Abstract

The rapid spread of technology and learning systems have altered the viewpoint about the lack of e-learning to the human element.

the intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education.

however, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields.

building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development massive open online courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making.

therefore, the aim is to explore how educators from the architecture and urban field e-learning stakeholders perceive AI in the creation of urban MOOCs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI.

in an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning ? to achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges.

this overview informed and guided the formulation of the survey questions.

then we conducted a survey on educators in architecture and urban fields from universities in Egypt.

the unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators' AI adaptivity in online learning and MOOCs.

the study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners.

Data Type

Conference Papers

Record ID

BIM-1454572

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