AI-Based Education for Sustainability and the Promotion of Lifestyle and Healthy Diet

AI-Based Education for Sustainability and the Promotion of Lifestyle and Healthy Diet

Sami Fattouch, Fethi Ben Slama, Henda Jamoussi, Luana Bontempo
DOI: 10.4018/979-8-3693-1638-2.ch006
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The literature is prolific in studies about the artificial intelligence (AI) applications, particularly to support formal education and develop adaptive lifelong learning environments by means of an array of flexible, inclusive, and interactive tools. AI-based education is assumed to be determinant for the achievement of the Sustainable Development Goals (SDGs), targeted for 2030, and supported by all 193 member states of the United Nations. This intelligent technology is expected to play a key role to raising awareness about the management of food discards and byproducts for a circular economy, thus optimizing the resources' efficiency and slowing down their economic, social, and environmental impacts. This chapter provides an overview of the AI applications and challenges to promote sustainable food habits and to monitor and manage local food byproducts suitable for human consumption to develop nutritional and healthy added-value outcomes.
Chapter Preview
Top

Introduction

During the last decades, the development of digital tools and computer-based means has metamorphosed the education to shape an up-to-date and innovative learning system in both virtual and real spaces by means of online and face-to-face workplaces. The recent advances in the artificial intelligence (AI) field boosted substantially these progresses to cross-over interdisciplinary learning sciences (informatics, statistics, pedagogy, linguistics, neuropsychology, sociology, …) and targeting as much as possible a personalized education to a greater extent focused on adaptive learning environments and using a panoply of flexible and inclusive means (Pai et al., 2021). While the classic education models were more built on the teachers’ pedagogical knowledge skills, the cognitive load, and the formative return, the increasingly available amounts of diversified data related to education with the advent of Big Data significantly facilitated and accelerated the emergence of theoretical and practical research works on the issues of the collection, assessment, and analysis of the accumulated data (Hamal et al., 2022). Presently, pedagogic experts are incessantly seeking state-of-the-art educational techniques that could be ever more attractive for digital teaching, e-coaching, and e-learning. Besides, the learning analytics have motivated the educational institutions to analyse and predict the feedback of their learners while being continuously challenged by a greater pressure at all levels of education to meet the high-ranking accreditation standards (Hamal et al., 2022). Simultaneously, the imminent ecological issues are growingly raising public awareness about the pessimistic predicated scenarios because of the global warming of the planet, thus necessitating to engage both AI and education to actively contribute to the sustainable management of these matters and help developing preventive measurements (Onyeaka et al. 2023). The impact of these adversities will undeniably affect different facets of life, in particular provoking a remarkable declining growth in global agricultural productivity which will not be able to cover the need of a constantly expanding population predicted to exceed nine billions by 2050 (Béné et al., 2015). Filho et al. (2022) also evoked the involvement of conflicts and wars in food deprivation, especially i.e., the recent unforeseen war in Ukraine which has severely damaged its wheat reserves and dropped down their export, thus aggravating the extension of food insecurity, particularly in the poorer.

Key Terms in this Chapter

IT: Information Technology.

NGOs: Non-Governmental Organizations.

LA: Learning Analytics.

ITSs: Intelligent Tutoring Systems.

ML: Machine Learning.

DL: Deep Learning.

SDGs: Sustainable Development Goals.

DRL: Deep reinforcement learning.

SNS: Social Network Site.

VLEs: Virtual Learning Environments.

UN: United Nations.

FAO: Food and Agriculture Organization.

MOOCs: Massive Open Online Courses.

EDM: Educational Data Mining.

Complete Chapter List

Search this Book:
Reset