Intelligence In Higher Education Research Articles

Computer intelligence in higher education

The role of artificial intelligence (AI) systems is constantly increasing in the creation and production of this knowledge. Software and hardware complexes of universal humanoid intelligence and artificial superintelligence are being created with maximum intensity. Progress in this field in the last 15 years is reflected precisely in the realization that the human intellect does not arise simply from a few methods and techniques for solving problems, schemes, and reasoning mechanisms, but requires the use of specific knowledge depending on the specific problem area. Education, thanks to the application of modern technologies, is no longer a privilege, but a basic human right.

Adoption of AI Chat Bot like Chat GPT in Higher Education in India: a SEM Analysis Approach

Applications of artificial intelligence have grown to be one of the most important and well-known targets for nations in the modern era, particularly in the education sector. This is because these technologies have the potential to boost productivity and help the sector develop quickly by presenting scientific information to students in an appealing manner. To explore the link between latent variables, structural equation modeling is done using the partial least square technique structural equation model (PLS-SEM) with the help of Smart PLS. The intent of this investigation is to offer empirical support and explain the variables that may influence the adoption of artificial intelligence in higher education. The finding suggests that the hedonic, gamification, and motivational factors, as well as the convenience and efficiency factors, all have a significant impact on the adoption of artificial intelligence in India, like Chat GPT.

Optimising the Interface between Artificial Intelligence and Human Intelligence in Higher Education

Optimising the Interface between Artificial Intelligence and Human Intelligence in Higher Education

Review on A big data-based innovative knowledge teaching evaluation system in universities

With the widespread use of digital technologies such as big data, cloud computing and artificial intelligence in higher education, how to establish a scientific and systematic evaluation system to turn the traditional classroom with the one-way transmission of knowledge into an interactive space for exchanging ideas and inspiring wisdom has become an essential task for human resource management in universities, and a key to improving teaching quality. However, due to the debate between scientism and humanism in teaching evaluation, studies related to teaching performance have been isolated from human resource management, resulting in the lack of a systematic vision and framework for such studies. Relevant studies are still limited to the evaluation contents of different evaluation subjects. Evaluations also tend to focus only on the teaching process, ignoring the objectives of talent training, making it difficult for evaluations to play a goal-oriented role and hindering the further development of relevant studies. Therefore, this paper draws on human resource management methodologies and analyzes knowledge teaching evaluation system characteristics in colleges and universities in a big data context to construct a “multiple evaluations, trinity and four-step closed-loop” big data-based knowledge teaching evaluation system. “Trinity” represents evaluation from three performance dimensions: teaching effect, teaching behavior and teaching ability. “Multiple evaluations” represents the design of teaching performance indicators based on teaching data, breaking the barriers between different evaluation subjects. “Four-step closed-loop” draws on performance management theory to standardize the teaching performance management process from four aspects: planning, implementation, evaluation, and feedback. This evaluation system provides a systematic methodology for unifying the theory and practice of innovative knowledge teaching evaluation system in universities in a big data context.

Integration of Design Thinking with Cultural Intelligence in Higher Education for a Socially Complex Environment

AbstractThis article focuses on international design education and the benefits of combining design thinking with cross‐cultural empathy, in addressing ‘wicked’ global problems in our society. There is a lack of cross‐cultural design thinking‐specific approaches in international education literature. This study aims to cover the main contributors to the development of design thinking, Cultural Intelligence (CQ), and explore their different views. It discusses the metric of CQ, its importance in the world today and how to improve CQ, through cross‐cultural design workshops and units that deal with ‘wicked’ global problems and scenarios. The approach involves a combination of design thinking methods with an empathy framework, to seek meaningful participation in the entire design process for a socially complex environment. Furthermore, the results reveal that, co‐design with cross‐cultural empathy relates to the increasing development of CQ. The findings are discussed and related to the implications for the use of design thinking in international higher education.

Artificial Intelligence in Higher Education and Changing roles of Educators

Artificial intelligence has created many imagination among movie makers, cartoonists and science fiction authors they imagined how AI will change our life. Even though it has not changed our life as they imagined but it has entered in every aspect of our life. This paper discusses about the basic things about artificial intelligence. Artificial intelligence mainly uses computer science, robust data base, and our structure of our brain, nervous system and learning theory from psychology. Artificial intelligence has wide applications in higher education especially in teaching and learning process. It can be used in institutional, student support and pedagogical process. This paper discusses about application of artificial intelligence in higher education and changing roles of educators.

Artificial Intelligence Implementation In Omani Higher Education; Logistics Studies, Imcoi As A Case Study

Artificial intelligence (AI1) in education started over 30 years. Currently, artificial intelligence witnesses’ significant growth in the region to improve the productivity of learning quality. Previous studies revealed artificial intelligence in higher education in different aspects, majors, and countries. This study released the use of artificial intelligence in logistics studies in higher education institutions of Oman, where it took IMCO as a case study. The research paper thoroughly finds out the perfect use of artificial intelligence in logistics studies and the extent of its impact. The primary objectives of this study were to assess the use of AI in logistics study, the pros and challenges of using AI in logistics programs in higher education and suggest some recommendations to better use of AI in logistics study. The survey and interview were the principal methods used to collect primary data, while the secondary data were collected from the literature relevant to the study. The questionnaire was designed by using Office 365 form. The target population of this study was approximately 100 people that included teachers and students in International Maritime College Oman. The findings showed a slight variation in some of the responses, but most supported the previous studies that AI implementation in education is vital.

Artificial Intelligence in Higher Education: Challenges and Opportunities

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the emerging fields in education technology. Whilst it has been around for about thirty years, educators remain unclear as to how to take full pedagogical advantage of AI on a broader scale and how it could actually have a meaningful impact on teaching and learning in higher education. This paper aims to evaluate Artificial Intelligence within Higher Education, focussing on the opportunities and challenges it presents. It also investigates the educational implications of emerging technologies on the way students learn and how institutions teach and evolve. The paper gathers some examples of the introduction of AI in education in a bid to establish equitable, quality education for all. Firstly, the paper analyses how AI can be used to improve learning outcomes, presenting examples of how AI technology can help education systems use data to improve equity and quality in Higher Education. The paper also addresses the benefits and challenges of introducing AI in educational settings, as well as the potential risks of such an endeavour. Finally, we put forward some recommendations for AI in education, with a focus on establishing discussions around the uses, possibilities and risks of AI in education for sustainable development.

Artificial Intelligence in Higher Education: Perspicacity Relation between Educators and Students

Artificial intelligence is a system with the nature of human intelligence that can automatically provide knowledge and information to create intelligent applications to make it easier to solve problems such as problem-solving, speech recognition, and learning. This research was investigated to find out and understand the relationship of intelligence between educators and students in applying artificial intelligence in universities. The method used is a phenomenological type of qualitative method. This research was conducted at Nurul Jadid University (UNUJA), with the subject of research being PAI lecturers and students in semester V. This research phase was carried out by preliminary studies, observation, data collection (interviews), data verification, and drawing conclusions. As for the results of this study, in its application, artificial intelligence in universities has a positive and negative impact on the relationship of intelligence to learning when it is carried out outside conventional learning. The intelligence relationship between educators and students, which includes Intelligence Quotient (IQ), Emotional Quotient (EQ), and Spiritual Quotient (SQ) contained in it, is also a positive thing that coexists and is directed.

Applied Innovation: Artificial Intelligence in Higher Education

Applied Innovation: Artificial Intelligence in Higher Education

The Effect of Service-Learning on Intrapersonal Intelligence in Higher Education

이 연구의 목적은 대학교육에서 교수-학습방법인 서비스-러닝이 대학생들의 자기성찰 지능에 미치는 영향을 탐색하는 것이다. 자기성찰 지능은 다중지능 중 하나로 다른 일반 지능들의 기초이자 활성화의 동인이 되기 때문에, 학습과 자아성취에 필수적이다. 연구문제는 1) 서비스-러닝의 참여는 자기성찰 지능에 어떤 영향을 미치는가? 2) 서비스-러닝의 참여동기는 자기성찰 지능에 어떤 영향을 미치는가? 이다. A대학교에서 서비스-러닝에 참여한 학생 303명을 대상으로 사전-사후설문을 실시한 결과, 서비스-러닝에 참여함으로써 학생들은 자기성찰 지능이 유의미하게 향상되었으며, 1학년 학생들이 가장 큰 변화를 보였고, 4학년 학생들이 가장 적은 변화를 나타냈다. 참여동기 유형 중 봉사에 대한 내적 참여동기가 높은 학생이 자기성찰 지능에 가장 큰 향상을 보여주었다. 이 연구는 대학교육에서 ‘성찰’을 중심으로 한 서비스-러닝의 교육적 가능성을 검증하고, 서비스-러닝으로 계발된 자기성찰 지능을 바탕으로 다양한 지능과 역량들을 키울 수 있는 근거를 마련했다는 데에 의의가 있다.

A Common Framework for Artificial Intelligence in Higher Education (AAI-HE Model)

This research aims to 1) Develop a common framework for artificial intelligence in higher education (AAI-HE model) and 2) Assess the AAI-HE model. The research process is divided into two stages: 1) Develop an AAI-HE model, and 2) Assessment the model. The sample consists of five experts chosen through purposive sampling. The data is analyzed by means and standardized deviations statistically. The research result shows that 1) the AAI-HE model consists of seven key components which are 1.1) User Interactive Components and Technology of AI, 1.2) Components and Technology of AI, 1.3) Roles for Artificial Intelligence in Education 1.4) Machine Learning and Deep Learning 1.5) DSS Modules 1.6) Applications of Artificial Intelligence in Education, and 1.7) AI to enhance campus efficiencies, and 2) The result of the assessment of the AAI-HE model is rated as absolutely appropriate overall.

Artificial Intelligence in Higher Education: A Bibliometrics Analysis

With the coming of the era of intelligence, the integration and innovation of artificial intelligence and education has become an essential trend for future educational reform. In this paper, a bibliometrics analysis of 288 academic publications between 2000 and 2020 was carried out to provide a systemic and objective overview of research in this field by visualizing collaboration network and performing keyword analysis. The results show that the number of publications in this field has increased rapidly since 2016. The most productive countries are the United States, China and some European countries like England and Spain. However, a complete and close collaboration network of institutions or authors has not yet been formed. Three themes are synthesized by analyzing the related keywords: (1). diverse artificial intelligence technologies; (2). intelligent tutoring system to support student learning; and (3). educational data mining and learning analytics. The findings also show that research on intelligent tutoring systems to support student learning has long been a focus of this field, whereas data science is a relatively new research trend.

Developing psychological capital and emotional intelligence in higher education: A field experiment with economics and management students

This study examines the effectiveness of an educational intervention built upon the experiential learning model and positive psychology aimed at increasing management students' psychological capital (PsyCap) and emotional intelligence (EI) during higher education (HE). It also analyses how EI influences PsyCap and how both affect students' academic performance measured by self-reported grade point average (GPA). A field experiment was used employing a quasi-experimental design with a pre-test-post-test and a control group. Results confirm the effectiveness of the educational intervention in increasing management students' PsyCap and further influencing academic performance; however, the intervention was less effective in increasing students' overall EI. The findings of this paper contribute to the literature by examining the role of management education to develop students' psychological resources, which have key implications for graduates' academic success and future employability and institutions’ engagement in responsible management education.

Emergent Role of Artificial Intelligence in Higher Education

Artificial Intelligence (AI) has been seamlessly integrated into our lives. In 2016, Barack Obama reported, "the walls between humans and AI systems are slowly beginning to erode, with AI systems augmenting and enhancing human capabilities. Fundamental research is needed to develop effective methods for human-AI interaction and collaboration [1]." As institutions of higher education continue to grapple with the impacts of COVID-19, some are turning to existing technologies to make educational experiences safer, more efficient, and more adaptable to changing environments. AI in higher education (AIEd) enables institutions to have meaningful impacts on teaching and learning environments. When using the term AI in relation to higher education, it can be described as an information system that acts as if it were intelligent by perceiving and acting upon its environment. The bandwidth of teaching professionals has been grossly misidentified, and as remote learning methods become more common, there will be deeper discussions surrounding the role of AI in supporting these professionals. It will benefit both teachers and their students if instructors have more time to focus on curriculum contents and prolonged engagement, while AI applications take over administrative functions that are currently expected of teaching professionals.

The Potential of Artificial Intelligence in Higher Education

Artificial Intelligence (AI) is seeping into many aspects of our everyday lives, with common internet applications, smartphones and even household appliances. Within education, AI is a rapidly emerging field and there is a strong potential for AI to greatly extend and enhance teaching and learning in higher education (Crompton et al., 2020). AI is defined as “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks” (Popenici & Kerr, 2017). In the Horizon Report 2020 report (Brown et al., 2020), AI is listed as one of six technologies with the potential for high impact in higher education. The Horizon Report (2020), is an annual publication that examines the major trends in educational technology that are shaping global higher education. This paper will highlight some of the ways AI is supporting both students and faculty members including bespoke learning, intelligent tutoring systems, facilitating collaboration, and automated grading. This is followed by a section on ethical implications.

Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling

Emergence of the use and application of Artificial Intelligence (AI) in higher education in India has opened new possibilities and challenges. Use of AI in will bring in effective change of governance in the entire internal architecture of Indian Institutes of higher education. The prospect of use of AI includes investigation of educational implications as to how teachers would enrich them, how students would learn, and how accurate and prompt decisions can be taken in the institutes of higher education. This is important since the workload has been multiplied due to massification of higher education. Such being the scenario, help of AI is highly essential. The question of adoption of AI in higher education is an important issue in this perspective. The purpose of this study is to explore how the stakeholders would be able to adopt it. For this, we have taken help of many adoption theories and models including ‘Unified Theory of Acceptance and Use of Technology’ (UTAUT) model. We have developed hypotheses and a conceptual model and got it validated through survey with the help of feedbacks from useable 329 respondents. It has been found that the model can help the authorities to facilitate adoption of AI in higher education.

Перспективы внедрения технологий искусственного интеллекта в сфере высшего образования

At present, comprehensive research from various points of view (social, economic, ethical, and legal) on the implementation of artificial intelligence technologies in various spheres of society’s life is of particular relevance. It is important to identify opportunities and threats, to determine the limits of the use of artificial intelligence in practice. The article is devoted to the introduction of artificial intelligence technologies in higher education. Prospective directions of using artificial intelligence in higher education are considered and analyzed. The authors conclude that artificial intelligence is not a competitor to the teaching staff, either in teaching or in evaluating students’ knowledge. Artificial intelligence is an auxiliary, but valuable tool that can perform and improve a large number of different operations performed at the University, help in organizing an effective educational process and building the necessary communications. Effective use of AI technologies in higher education will allow you to select the most optimal learning strategy adapted to the individual abilities and needs of students and the needs of the labor market.

Emotionally intelligent students are more engaged and successful: examining the role of emotional intelligence in higher education

The role of emotions in student engagement has been examined in many studies. However, little is known about how emotional intelligence (EI) is related to engagement and other key learning outcomes in higher education. To fill this gap, this study examined how EI is associated with student engagement and how EI and engagement jointly predict key learning outcomes in higher education, including the students’ GPA, generic outcomes, and satisfaction with the university experience. The study adopted a prospective longitudinal design involving 560 first-year students from 10 faculties of a university in Hong Kong. The data were collected at two-time points, namely before the start and after the end of the students’ first year in the university. Structural equation modeling was employed to test the measurement and hypothesized models. Results indicated that EI positively predicted all dimensions of student engagement and promoted key learning outcomes (including GPA, generic learning outcomes, and students’ satisfaction with the university) via the different dimensions of student engagement. The model also explained 16%, 44%, and 38% of the students’ GPA, generic learning outcomes, and satisfaction with their university experience, respectively. This study provides empirical evidence on the positive effect of EI on the students’ optimal functioning in the higher education context. Implications of the findings are discussed.

Artificial Intelligence in Higher Education: New Wine in Old Wineskins?

Artificial intelligence applications are becoming common in every segment of American life; higher education will not be immune. Attempts to fit artificial intelligence into the existing syllabus o...