2828 research outputs found
Sort by
Chatbot Adoption: A Multiperspective Systematic Review and Future Research Agenda
No full text—Studies on Chatbot adoption are gaining traction
across different fields. Previous studies have outlined several
drivers of Chatbot adoption through the lenses of various tech
nology adoption theories. However, these studies have not been
thoroughlyreviewedandsynthesized.Therefore,thisarticleaimsto
analyzethetechnologyadoptiontheories,antecedents,moderators,
domains, methodologies, and participants through a multiperspec
tive viewpoint. Out of 3942 studies collected, 219 studies were ana
lyzed. The main findings indicated that the technology acceptance
model, social presence theory, and computers are social actors
are the main dominant theories in explaining Chatbot adoption.
MoststudiesfocusedonexaminingtheusageintentionofChatbots,
with limited investigations on actual use and continuous intention.
Nearly 63% of the analyzed studies did not employ moderators,
andthose that did tend to do so mostfrequently focused on gender,
Chatbot/technical experience, andage.Thisarticle presents afresh
viewpoint that deepens our understanding of Chatbot adoption
and proposes several agendas for future research. The agenda
incorporates research directions for Chatbots adoption in general
and generative artificial intelligence in specific. It also offers sev
eral theoretical contributions and provides relevant information
to Chatbot developers, decision-makers, practitioners, IT vendors,
and policymakers
Investigating student acceptance of an academic advising chatbot in higher education institutions
No full textThe study explores factors affecting university students’ behavioural intentions in
adopting an academic advising chatbot. The study focuses on functional, socio emotional, and relational factors affecting students’ acceptance of an AI-driven aca demic advising chatbot. The research is based on a conceptual model derived from
several constructs of traditional technology acceptance models, TAM, UTAUT, the
latest AI-driven self-service technologies models, the Service Robot Acceptance
(sRAM) model, and the intrinsic motivation Self Determination Theory (SDT)
model. The proposed conceptual model has been tailored to an educational con text. A questionnaire Survey of Non-purposive sampling technique was applied to
collect data points from 207 university students from two major universities in the
UAE. Subsequently, PLS-SEM causal modelling was applied for hypothesis testing.
The results revealed that the functional elements, perceived ease of use and social
influence significantly affect behavioural intention for chatbots’ acceptance. How ever, perceived usefulness, autonomy, and trust did not show significant evidence
of influence on the acceptance of an advising chatbot. The study reviews chatbot
literature and presents recommendations for educational institutions to implement
AI-driven chatbots effectively for academic advising. It is one of the first studies
that assesses and examines factors that impact the willingness of higher education
students to accept AI-driven academic advising chatbots. This study presents sev eral theoretical contributions and practical implications for successful deployment
of service-oriented chatbots for academic advising in the educational sector
A Generative AI Chatbot in High School Advising: A Qualitative Analysis of Domain-Speci c Chatbot and ChatGPT
No full textDue to the variety of chatbot types and classi cations, students and advisers may experience confusion
when trying to select the right chatbot that can more trust it, however, the classi cation of chatbots
depends on different factors including, the complexity of the task, the response-based approach and the
type of the domain. Since selecting the most effective chatbot is crucial for high schools and students, a
semi-structured interviews in qualitative research were conducted with eight high school students in order
to investigate the students ‘perspectives on different seven responses of generative questions from the
domain-speci c chatbot named HSGAdviser, comparing it with the ChatGPT. All questions were related to
students’ advising interests including university applications, admission tests, majors and more. The
transcribed data were reviewed and examined by using the thematic analysis. However, the results reveal
that most students found that HSGAdviser chatbot is easier, shorter, faster and more concise compared
to ChatGPT, especially for Yes/No questions as students expect brief answers. However, some students
found that certain crucial questions that can have a signi cance impact on their future, they would prefer
the ChatGPT for more detailed information. The limitation of this study is the limited size of the
participants. Nevertheless, in the future research, other high school students from different regions will
participate in the study
The Role of Lexical Cohesion in Improving Twelfth Graders’ Essay Writing Quality
No full textThe current study was conducted to examine the role of lexical cohesion in
improving the quality of twelfth graders’ essay writing. The study specifically aimed
at examining the correlation between lexical cohesive devices (LCD) and the quality
of written texts as well as investigating the barriers of employing these devices for
twelfth graders. The context was a private American curriculum school in the UAE.
The present paper adopted the quantitative correlational and the quantitative survey
research approaches. Data were collected using document analysis of 30 twelfth
graders’ essays and an online survey attempted by 113 English teachers. Data were
analysed using correlational statistics, multiple linear regression, and exploratory
factor analysis. The results indicated that there was a significantly positive, moderate
association between cohesive ties and students’ essay scores. The results also demon
strated that there was a significantly linear relation between hyponyms and synonyms
and students’ writing scores although hyponyms had more effect on the writing score
than synonyms. The findings of exploratory factor analysis identified three factors as
major barriers encountered by learners while using lexical cohesion in their written
texts including (1) lack of resources and instructions, (2) impact of L1 interference
and (3) limited lexical awareness
Investigating different damages in a hybrid composite plate completely immersed in water using Ultrasonic waves
No full textThe present research investigates the ability of ultrasonic waves in
detecting and localizing different types of damage in a hybrid metal-composite
laminate fully immersed in water through the permanently attached piezoelectric
(PZT) transducers (PZT). Based on the wave-structure analysis and dispersion
diagrams obtained for the hybrid metal-composite laminate, a suitable wave
excitation frequency is selected for conducting numerical simulations. Accordingly, a
gaussian-windowed tone burst signal centered at 250 kHz is applied at the PZT to
generate ultrasonic waves in the test specimen. It is found that Scholte wave mode is
generated and propagates along the water-solid interface in addition to the anti symmetric guided wave modes. Further, mode conversion is observed during the wave
mode-damage interaction. Comparing the pristine and damaged specimens,
additional wave packets are seen to be propagating within the specimen that is
revealed by analyzing the time-domain waveforms. This information can be further
utilized in detecting as well as localizing the damage in the specimen. The location of
damage found is well in harmony with the physical locations of damage. Thus, the
proposed methodology is found to be capable of investigating the health status of
immersed hybrid laminates non-destructively using ultrasonic waves with the help
of simple and cost-effective PZT sensors
Arabic Educational Neural Network Chatbot
No full textChatbots (machine-based conversational systems) have grown in popularity in recent years. Chatbots powered by artificial
intelligence (AI) are sophisticated technologies that replicate human communication in a range of natural languages. A chatbot’s
primary purpose is to interpret user inquiries and give relevant, contextual responses. Chatbot success has been extensively reported in
a number of widely spoken languages; nonetheless, chatbots have not yet reached the predicted degree of success in Arabic. In recent
years, several academics have worked to solve the challenges of creating Arabic chatbots. Furthermore, the development of Arabic
chatbots is critical to our attempts to increase the use of the language in academic contexts. Our objective is to install and create an
Arabic chatbot that will help the Arabic language in the area of education. To begin implementing the chabot, we collected datasets
from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system
using a neural network model to create an Arabic neural network chabot. Furthermore, we found relevant research, conducted earlier
investigations, and compared their findings by searching Google scholar and looking through the linked references. Data was gathered
and saved in a json file. Finally, we programmed the chabot and the models in Python. As a consequence, an Arabic chatbot answers
all questions about educational regulations in the United Arab Emirates
Cyberbullying Detection Model for Arabic Text Using Deep Learning
No full textIn the new era of digital communications, cyberbullying is a significant concern for society.
Cyberbullying can negatively impact stakeholders and can vary from psychological to pathological,
such as self-isolation, depression and anxiety potentially leading to suicide. Hence, detecting any act
of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate
results from the victim’s perspective. Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. However, the meta-analysis shows that
ML approaches, particularly DL, have not been extensively studied for the Arabic text classification
of cyberbullying. Therefore, in this study, we conduct a performance evaluation and comparison for
various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM and LSTM-TCN) on
different datasets of Arabic cyberbullying to obtain more precise and dependable findings. As a result
of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the
baseline models CNN, BLSTM and GRU for identifying cyberbullying. The proposed hybrid model
improves the accuracy of all the studied datasets and can be integrated into different social media sites
to automatically detect cyberbullying from Arabic social datasets. It has the potential to significantly
reduce cyberbullying. The application of DL to cyberbullying detection problems within Arabic text
classification can be considered a novel approach due to the complexity of the problem and the tedious
process involved, besides the scarcity of relevant research studies
Impacts of Diverse Workforce on Cyber Security and Information Technology Management
No full textThe increasing dependence on digital technologies and the growing threat of cyberattacks made cybersecurity a critical concern for organizations worldwide. To address these challenges, organizations seek to develop diverse, skilled cybersecurity workforces that adapt to the constantly evolving threat landscape. However, little is known about the relationship between diversity in the cybersecurity workforce and performance, particularly in the context of the United Arab Emirates (UAE).
The aim of thisresearch is to investigate the impacts and relationship of diverse workforce on the cyber security and information technology management through evaluation of role of mediating factors as identified in this research. The research objective sinclude, developing an understanding of diverse workforce, its importance and impacts on the global business industry. Identify major influencing factors of diversity which has impacts on efficiency of cyber security and information technology management and challenges within the cyber security and information technology management domains. Suggesting recommendations for improvements within cyber security and information technology management in terms of results and diversity factors to overcome challenges
This study used a cross-sectional survey design to examine this relationship in the UAE. The sample included 51 organizations from three sectors (Government, Private, and Non-governmental) in the UAE.
Results of a linear regression analysis and an analysis of variance (ANOVA) indicated that diversity in the cybersecurity workforce significantly impacts information technology management (CSITM) in the UAE. The research findings showed that this relationship has proved the diverse workforce has a positive and significant impact on cybersecurity and information technology management. These findings suggest that other factors may be more influential in determining Cyper Security Information Technology Managment in the UAE and that diversity may not be a key predictor of performance in this context.
Despite these findings, organizations should continue to consider the potential benefits of diversity in the cybersecurity workforce, including improved decision-making, increased adaptability, enhanced creativity, greater representation, and improved team performance. To overcome the shortage of trained cybersecurity professionals and promote workforce diversity, organizations may need to adopt more flexible and inclusive work cultures and invest in training and development programs that support recruiting and retaining diverse team members. Further research is needed to more fully understand the relationship between diversity and performance in the context of cybersecurity and IT management in the UAE and other contexts
Successful Practices of Leadership on School Improvement: A Case Study in a Private School in Dubai
No full textThis open access book presents contributions on a wide range of scientific areas originating from the BUiD Doctoral Research Conference (BDRC 2022)This paper aimed to investigate successful practices of school leadership that lead to school improvement. To achieve this, the paper adopted the mixed-method approach and utilized two instruments: The first instrument is teachers’ and leadership members’ perceptions of school improvement questionnaire to collect quantitative data, the second instrument is a semi-structured interview with the school principal for the qualitative data. The analysis of both collected data led to a conclusion that school principal careful and professional practices have a significant impact on the overall school improvement. These practices include parents’ engagement, curriculum reform, teachers’ well-being, and professional development for both teachers and leadership members. This case study is an evidence-based guideline for educators and decision makers seeking quality education in their personalized- context learning community
The Impact Of Teachers’ Differentiated Initiations And Feedback On Students’ Responses: A Model Based On Audience Design And Bloom’s Taxonomy Of Cognitive Domains
No full textThe main objective of this study was to gain insights into students' verbal responses and, consequently, offer valuable information and recommendations to teachers and curriculum designers on ways to cater to individual students' learning needs and enhance the quality of students' responses. This study aimed to investigate the impact of incorporating the audience design model, Bloom's taxonomy, and the differentiated instruction approach on students' verbal responses.Data for this research was gathered from 5 observed classes and through semi-structured interviews with 12 high school teachers and 5 high school students. The research sought to address the following research questions: To what extent does integrating the audience design model in teachers’ differentiated initiations and feedback improve students’ verbal responses? To what extent does employing Bloom’s Taxonomy of Cognitive Domains when tailoring teachers’ initiations and feedback help improve students’ verbal responses? The study, conducted in a private school in Dubai, employed qualitative research methods to analyse and synthesise data into several themes related to differentiated instruction, classroom interactions, teacher talk time vs. student talk time, and audience design. These themes provided a comprehensive understanding of the data. The findings indicated that teachers' differentiated instruction and feedback, especially when considering the audience design model, significantly impacted both the quantity and quality of students' verbal responses. However, the study had faced some limitations, as a result, several recommendations were put forward to assist educators and curriculum designers in facilitating the implementation of differentiated instruction and feedback. These recommendations included promoting research, collaboration, and presentation skills. Furthermore, it highlighted the significant role of curriculum publishing companies in offering ready-made questions and activities to ease the burden on teachers in terms of time and effort