Abdelhak Aqqal

Artificial intelligence (AI) and its application across various domains have sparked significant interest, with each domain presenting distinct characteristics and challenges. In the renewable energies sector, accurate prediction of power output from photovoltaic (PV) panels using AI is crucial for meeting energy demand and facilitating energy management and storage. The field of data analysis has grown rapidly in recent years, with predictive models becoming increasingly popular for forecasting and prediction tasks. However, the accuracy and reliability of these models depend heavily on the quality of data, data preprocessing, model learning and evaluation. In this context, this paper aims to provide an in-depth review of previous research and recent progress in PV solar power forecasting and prediction by identifying and analyzing the most impacting factors. The findings of the literature review are then used to implement a benchmark for PV power prediction using deep learning models in different climates and PV panels. The aim of implementing this benchmark is to gain insights into the challenges and opportunities of PV power prediction and to improve the accuracy, reliability and explainability of predictive models in the future.

This article presents feedback on the implementation of an Artificial Intelligence-based adaptive learning Moodle plugin aimed at enhancing the engagement levels and academic performance of 102 Moroccan high school students. The primary objective of this study was to assess and compare the performance of students utilizing the adaptive learning system with those employing conventional learning methods. To guarantee the efficacy of this approach, a participant satisfaction survey and a comprehensive summative evaluation were conducted, revealing the positive impact of AI-based adaptive learning on the participants. The results of this study highlight the potential benefits of integrating AI-driven adaptive learning into high school computer science curricula, emphasizing how it may raise student engagement and academic performance. These results strengthen the determination to use this teaching methodology with students in future educational activities.

Introduction:E landscapes have been transformed by technological advancements, enabling adaptive and flexible learning through AI-based and decision-oriented adaptive learning systems. The increasing importance of this solutions is underscored by the pivotal role of the learner model, representing the core of the teaching-learning dynamic. This model, encompassing qualities, knowledge, abilities, behaviors, preferences, and unique distinctions, plays a crucial role in customizing the learning experience. It influences decisions related to learning materials, teaching strategies, and presentation styles. Objective: This study meets the need for applying AI-driven adaptive learning in education, implementing a novel method that uses self-esteem (ES), emotional intelligence (EQ), and demographic data to predict student performance and adjust the learning process. Methods: Our study involved collecting and processing data, constructing a predictive machine learning model, implementing it as an online solution, and conducting an experimental study with 146 high school students in computer science and French as foreign language. The aim was to tailor the teaching-learning process to the learners' performance. Results: significant correlations were observed between self-esteem, emotional intelligence, demographic data, and final grades. The predictive model demonstrated a 90 % accuracy rate. In the experimental group, the results indicated higher scores, with an average of 15,78/20 compared to the control group's 12,53/20 in computer science. Similarly, in French as a foreign language, the experimental group achieved an average of 13,78/20, surpassing the control group's 10,47/20. Conclusion: the achieved results motivate the creation of a multifactorial AI-driven adaptive learning platform. Recognizing the necessity for improvement, we aim to refine the predicted performance score through the incorporation of a diagnostic evaluation, ensuring an optimal grouping of learners

With the aid of technology advancement, the field of education has seen a noticeable transformation. The teaching-learning process is now more interactive and is no longer restricted to students' physical presence in the classroom but instead makes use of specialized online platforms. In recent years, solutions that offer learning routes customized to learners' needs have become more necessary. In this regard, artificial intelligence has served as an excellent answer, allowing for the building of educational systems that can accommodate a wide range of student needs. Through this paper, a systematic mapping of the literature on AI-based adaptive learning is presented. The examination of 93 articles published between 2000 and 2022 made it possible to draw several conclusions, including the number of adaptive learning environments based on AI, the types of AI algorithms used, the objectives targeted by these systems as well as factors related to adaptation. This study may serve as a springboard for further investigation into how to address the problems raised by the current state.

The climate change is hitting the whole world, and with different level of severity from one geographical zone to another. There are different sensible environments, where its impact is having dramatic consequences on the life, for human being as well for fauna and flora. One of this sensible environment is the oasis. For example, in Moroccan oases, the conditions are getting more dramatic, with harsh desertification, scarcity of the precipitations, and more dangerously is the diminished groundwater, caused by less rain, less snow on the mountains, and evaporation of few available water due to thigh temperature. Other factors come to worsen the situation, such diseases in palm trees, olive tree, more frequent fire even outside of the hot season, etc. In this paper, we list and discuss some of the major challenges and problems facing the oasis environment, and we propose some smart applications to hep this environment to overcome such problems/challenges from societal, economic as well as environmental aspects. These solutions are pointed as smart to, because they are based on some advanced Information and Communication Technologies (ICT), through data collection from sensors, data transmission, and then data analysis in order to make some intelligent decisions through algorithms to meet some optical decisions for resources utilisation. The term of “resources” covers energy, water, space, etc. In other works, we discuss “Internet-of-Things” (IoT) paradigm for modelling, synthesis and deployment of smart oasis.

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Open access peer-reviewed chapter