The integration of Artificial Intelligence (AI) holds immense potential for revolutionizing education; especially, in contexts where multimodal learning experiences are designed. This paper investigated the potential benefits of Generative Artificial Intelligence (AI) in education, concentrating on the design and evaluation of an AI Educational Video Assistant tailored for multimodal learning experiences. The tool, utilizing the principles of the Cognitive Theory of Multimedia Learning (CTML), comprises three modules: Transcription, Engagement, and Reinforcement, each focusing on distinct aspects of the learning process. It Integraties Automatic Speech Recognition (ASR) using OpenAI's Whisper and Google's Large Language Model (LLM) Bard. Our twofold objective includes both the development of this AI assistant tool and the assessment of its effect on improving the learning experiences. For the evaluation, a mixed methods approach was adopted, combining human evaluation by nine educational experts with automatic metrics. Participants provided their perceptions on the tool's effectiveness in terms of engagement, content organization, clarity, and usability. Additionally, automatic metrics including Content Distinctiveness and Readability scores were computed. The results from the human evaluation suggest positive impacts across all assessed domains. The automatic metrics further proved the tool's ability in content generation and readability. Collectively, these preliminary results highlight the tool's potential to revolutionize educational design and provide personalized and engaging learning experiences.

The structure and topology of chemical compounds can be determined using chemical graph theory. Using topological indices, we may uncover much about connectivity, complexity, and other important aspects of molecules. Numerous research investigations have been conducted on the K-Banhatti indices and entropy measurements in various fields, including the study of natural polymers, nanotubes, and catalysts. At the same time, the Shannon entropy of a graph is widely used in network science. It is employed in evaluating several networks, including social networks, neural networks, and transportation systems. The Shannon entropy enables the analysis of a network’s topology and structure, facilitating the identification of significant nodes or structures that substantially impact network operation and stability. In the past decade, there has been a considerable focus on investigating a range of nanostructures, such as nanosheets and nanoparticles, in both experimental and theoretical domains. As a very effective catalyst and inert substrate, the $MgO$ nanostructure has received a lot of interest. The primary objective of this research is to study different indices and employ them to look at entropy measures of magnesium oxide(111) nanosheets over a wide range of p values, including $p=1,2,3,\dots ,j$. Additionally, we conducted a linear regression analysis to establish the correlation between indices and entropies.

The aim of this paper is to demonstrate the degree of teaching staff’s and Students’ satisfaction with the teaching and learning experience via the Blackboard â„¢ Learning Management System (LMS), recently acquired by King Abdulaziz University (KAU) as an online solution supporting distance education programs. Teaching staff’s and students’ perceptions about the e-courses offered via the LMS were collected in four areas: 1) E-course Content, 2) LMS Ease of Use and Performance, 3) Communication Facilitation, and 4) Delivery Methods. The respondents’ observations on these topics were gathered via two short, Likert-scale questionnaires which were distributed during the Summer Semester of 2014. The researchers sought, through this preliminary investigation, to identify issues which might impact the implementation of the Blackboard LMS in the Distance Education programs in KAU. Generally, the results indicate a relatively high satisfaction rating of the LMS from both groups of users.

A vertex subset D of G is a dominating set if every vertex in $V\left(G\right)\backslash D$ is adjacent to a vertex in D. A dominating set D is independent if $G\left[D\right]$, the subgraph of G induced by D, contains no edge. The domination number $\gamma \left(G\right)$ of a graph G is the minimum cardinality of a dominating set of G, and the independent domination number $i\left(G\right)$ of G is the minimum cardinality of an independent dominating set of G. A classical work related to the relationship between $\gamma \left(G\right)$ and $i\left(G\right)$ of a graph G was established in 1978 by Allan and Laskar. They proved that every $K_{1,3}$-free graph G satisfies $\gamma \left(G\right)=i\left(H\right)$. Hexagonal systems (2 connected planar graphs whose interior faces are all hexagons) have been extensively studied as they are used to present bezenoid hydrocarbon structures which play an important role in organic chemistry. The domination numbers of hexagonal systems have been studied continuously since 2018 when Hutchinson et al. posted conjectures, generated from a computer program called Conjecturing, related to the domination numbers of hexagonal systems. Very recently in 2021, Bermudo et al. answered all of these conjectures. In this paper, we extend these studies by considering the relationship between the domination number and the independent domination number of hexagonal systems. Although every hexagonal system H with at least two hexagons contains $K_{1,3}$ as an induced subgraph, we find many classes of hexagonal systems whose domination number is equal to an independent domination number. However, we establish the existence of a hexagonal system H such that $\gamma \left(H\right)<i\left(H\right)$ with the prescribed number of hexagons.

In the context of smart classrooms, monitoring students’ engagement is crucial for ensuring sustainable motivation, successful attainment of learning outcomes, ultimately, better academic achievement regardless of learning mode or environment. The use of IoT sensors in such contexts becomes a very important source of multi-modal data that can be used in the prediction of students’ engagement levels in real-time, subsequently, the provision of proper interventions. Thus, our study developed a framework for real-time students’ engagement prediction that utilizes camera, audio, physiological and environmental sensors to collect data from 36 female students enrolled in a master program. After combining the data and pre-processing the resultant dataset with additional steps of feature engineering and augmentation using WGAN, we trained a Bi-LSTM with Self-Attention and evaluated it on the augmented dataset and compared the results to four baseline models to output an accuracy per engagement level. The BiLSTM outperformed the baselines with an accuracy of 91.8%. The research reports also on the implementation of this pre-trained model in a Real-Time Student Engagement Monitoring Dashboard that provides a real-time view of individual and class engagement levels and is enhanced with an LLM’s (ChatGPT) generative capabilities to generate faculty targeted feedback that will assist in re-engaging the students.