Educational Intelligence Research Articles

The Double-Edged Sword of Artificial Intelligence (AI) in Education

Objective: The purpose of this paper is to conduct a thorough review and investigation on the function and importance of AI in education. The development of technology in the sector, and the emergence of raising interest to apply this AI technologies with a view to enable disruptive efficiencies across all aspects of education. Methodology: The study uses a systematic review of literature for exploring the existing literatures and research findings about AI implementation in Education. For learning it tells you good & bad about AI of bunchy resources, And changes because of the whether educational practices. Results: The results suggest that AI can greatly improve the personalization of learning, being able to provide tailored activities beyond traditional strategies. But the review also highlights worries over inequalities that may arise if only a specialist few can call upon top-notch AI tools possibly deepening of the achievement gap. Furthermore, a sanguine reliance on automated solutions could compromise the core human element in education and stymie social-emotional development for our students. Originality/value: Given the tendency for increased transparency, accountability and potential bias of AI systems in this critical domain leading to possible discrimination practices (e.g. admissions, grading etc) it is felt that the paper has great value contribution. There needs to be a spirit among policymakers, educators and developers working together to ensure that AI in education is implemented following certain principles.

TEACHERS’ AND STUDENTS’ PERCEPTIONS TOWARDS THE USE OF CHATGPT TO IMPROVE WRITING IN THE MALAYSIAN SECONDARY SCHOOL CONTEXT

The use of artificial intelligence in education has gained traction among teachers as well as students in recent years. Artificial intelligence (AI)-powered language models such as ChatGPT have been widely used for various purposes, including to produce improved writing. This study sought to investigate the perceptions of secondary school students and English teachers in Malaysian public schools on the use of ChatGPT to enhance students’ English writing. Alongside that, this study also investigated how ChatGPT was used by both students and teachers and factors that influenced their use of ChatGPT. This study employed a qualitative research design. The participants in this study hail from six schools around Sabah: SMK Tansau, Penampang; SM St. Michael, Penampang; SMK Shan Tao, Kota Kinabalu; SMK Bongawan II; SMK Umas-Umas, Tawau; and SMK Desa Kencana, Lahad Datu. A total of 20 participants participated in this study. 12 of them were students, and the rest were English teachers. Data for this study was collected using an open-ended questionnaire designed based on the Technology Acceptance Model (TAM). The data were analyzed thematically. The findings of the study showed that both students and teachers generally believe ChatGPT to be an effective tool to enhance students’ English writing proficiency. Among the features of ChatGPT that were lauded by the participants were its ability to provide personalized feedback and perform tasks that are useful for writing, such as proofreading and idea generation. Several challenges were also pointed out, such as difficulties using ChatGPT due to lack of resources and digital literacy. The participants also expressed their concern about the possibility of overdependency on ChatGPT, which can hamper the development of the students ability to write independently. The findings of this study contributed to the current knowledge of the perceptions of the use of ChatGPT among secondary school students and English teachers in Malaysian public secondary schools. This knowledge can guide educators and policymakers on matters related to the integration of AI into English writing instruction.

Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks

Cognitive diagnosis is one of the essential components in intelligent education and aims to diagnose student’s skill or knowledge mastery based on their responses. Recently, with the development of artificial intelligence, some researchers have applied neural network methods to cognitive diagnosis. Although they achieved some success, they seemed to lack a certain basis for designing network structures and could not obtain a unified method for designing network structures. We propose a neural network method for cognitive diagnosis based on Q-matrix constraints, introducing the Q-matrix from traditional cognitive diagnosis to enhance the reliability and interpretability of the network structure. Specifically, our method is highly consistent with generalized deterministic inputs, the noisy “and” gate model (GDINA), and the network structure reflects the direct contribution of skills to answering questions correctly, as well as the indirect contribution of interactions between skills to answering questions correctly. Finally, extensive experiments on both simulated and real datasets demonstrated that our method achieved high accuracy and reliability, with a particularly notable performance on low-quality datasets. As the number of questions and skills increased, our approach exhibited greater robustness compared to the classical methods, highlighting its potential for broad applicability in cognitive diagnosis tasks.

Generative Artificial Intelligence (AI) Education Policies of UK Universities

Generative Artificial Intelligence (AI) Education Policies of UK Universities

Exploring the Educational Transformations: A Systematic Literature Review on the Influence of the Internet of Things in Higher Education

The rapid growth of the Internet of Things innovations stimulates higher educational institutions to invest in and adopt these technologies to support and enhance their learning and teaching strategies. This study aims to investigate the influence of the Internet of Things on teaching and learning in higher education, performing a systematic literature review. Therefore, this research focuses on the following research questions: R-Q1: What are the benefits of the Internet of Things for teaching and learning in higher education? R-Q2: What are the limitations of the Internet of Things for teaching and learning in higher education? The systematic literature review, including the search strategy, the inclusion and exclusion criteria, and the review of the titles, keywords, and abstracts, identified a total of 31 results, mainly journal and conference articles. The findings from the extracted articles in this review were grouped into eleven themes: adoption, personalized learning, learning efficiency, intelligent teaching, collaboration and connectivity, creativity, health and safety monitoring, latency time, security and privacy, quality and ethics, and financing issues. The findings suggest that the Internet of Things can enhance the learning quality, improve the gained knowledge, and reduce costs in higher education. Therefore, adopting a consistent Internet of Things implementation strategy is essential to address identified limitations in higher education.

Transformative Assessment Review

The rapid advancement of generative artificial intelligence (AI) in education and the workforce necessitates that students acquire critical competencies, including digital literacy, data integrity, and ethical AI use, to navigate an increasingly AI-driven world. Equally, academic staff must be upskilled to effectively guide students in the ethical and practical applications of AI. This project examines how a private higher education (HE) institute has modified its Quality Assessment Framework (QAF) to incorporate an AI competence dimension, improve HE assessment and foster students’ competencies. This new dimension complements the existing QAF elements—Intellectual Quality, Significance, and Student Support to ensure a holistic approach in preparing students for AI-integrated academic and professional environments. This project’s framework and innovative tools, such as the enhanced QAF and an AI-driven Generative Pre-trained Transformer (GPT) assessment review tool, support educators in adapting curriculum and assessment design, and empower them to integrate AI competencies seamlessly into the curriculum. The development of these tools drew upon established frameworks, particularly the QAF (Gore et al., 2009) and the AI Assessment Scale (AIAS) by Perkins et al. (2024), which categorises AI usage across five levels, guides assessment reviews and ensures consistent standards for AI integration in student work. A trial, conducted across selected disciplines at the institute, involved academic staff as assessment reviewers and utilised the GPT tool to streamline the assessment review process by aiding in feedback provision, coding, and suggestions for improvement. Evaluation of the tool includes qualitative and quantitative methodologies, gathering academic feedback on usability, clarity, and effectiveness, as well as comparative studies to assess review time and quality before and after GPT integration. Usability testing evaluates workflow compatibility, while academic performance data provide insights into the tool’s impact on student outcomes. Initial results on the tool’s success in enhancing assessment alignment with institutional goals and fostering a comprehensive understanding of AI competence were also examined. Developing and implementing these tools has presented challenges, including the complexity of aligning them with diverse curriculum needs across disciplines, the time investment required for staff training, and the necessity of continuous updates to keep pace with AI technology. Addressing these challenges has been crucial to ensuring the effectiveness and sustainability of these solutions. This digital poster provides insights into the project, covering its development process, trial outcomes, challenges encountered, and future directions for integrating AI competence in higher education assessments.

Factors Influencing AI Learning Motivation and Personalisation Among Pre-service Teachers in Higher Education

This study examines the integration of artificial intelligence (AI) in education, focusing on motivating pre-service teachers to utilise AI technologies. The research assesses factors influencing their motivation for AI learning and personalisation in higher education. Over 14 weeks, 180 pre-service teachers participated in a qualitative case study with quantitative content analysis. Results indicate a positive attitude towards AI use among these future educators. The study concludes that AI-enhanced learning can significantly improve teacher-student interactions through personalised feedback, learning guidance and collaborative experiences across various platforms. Findings suggest AI has the potential to enhance personalised learning by tailoring content to individual student needs, preferences and learning styles. Educational policies should encourage a balanced approach to AI implementation, recognising its benefits whilst maintaining human interaction. Although AI may reduce face-to-face engagement, striking a balance where it supports rather than replaces human interaction can strengthen teacher-student relationships. By providing detailed insights into students’ progress and challenges, AI can help teachers offer more targeted support and encouragement.

Making Human Learning Visible in a World of Invisible Generative AI

The growth of Generative Artificial Intelligence (GenAI) in Higher Education (HE) challenges us to think differently and creatively about learning and assessment in order to prepare graduates for their futures (Dawson, 2023) This presentation will argue that the very strength of GenAI is also its Achilles’ heel. Anyone can access, garner and generate data rapidly, but human beings have never had a greater need for scrutiny and evaluation of this output. Our graduates will need to have a transparent understanding of how they are learning in terms of the critical choices they make, the information or knowledge they engage with, and where machine learning ends and theirs begins (Okolie et al, 2022). The future of assessment in HE must go beyond simply measuring knowledge acquisition and instead focus on capturing and evidencing students’ capacity for critical thinking. We will highlight some preliminary findings and early impacts of work underway at the University of Bath (UK) and University of Stellenbosch (South Africa), who are working collaboratively to ensure students develop holistically, excel in critical thinking, and gain the confidence and skills to use GenAI effectively to advance their ideas and learning (Osborne, 2024). This will be structured in three parts: Part one (Reviewing): We will outline work during academic year 23/24 by academic staff to review all their assessments against a three-fold categorisation model of permitted GenAI usage. From September 2024, all assessments clearly signal to students the extent to which GenAI is permitted in each assignment. Part two (Guidance): We will showcase resources that support staff to rethink assessment design that embeds GenAI in ways that are authentic, valid and robust (Upsher et al, 2024). We will also present resources that support students to consider how they might use GenAI ethically, and to develop their AI literacy skills and critical thinking. Part three (Examples): We will present initial findings from an internal project that is engaging with national and international employers, in-house world-leading AI researchers and our own students that highlight creative approaches to harnessing GenAI as a tool to reinforce and support (rather than replace) human learning. We conclude by suggesting that HE has always promoted critical thinking and the rigorous investigation and examination of the production of knowledge. We need graduates who can critically engage with their learning, and assessments that capture and evidence this. The only way to do this effectively is to put people at the start, middle and end of our assessment journey.

Students' perceptions of using artificial intelligence in tertiary education

Artificial Intelligence (AI) is increasingly influencing various aspects of teaching and learning in tertiary educational institutes. This concise paper presents the preliminary findings of an ongoing research that aims at exploring university students' perceptions of the use of AI in higher education settings. Phenomenographic framework has been adapted as the methodological guide for this research. Semi-structured interviews were conducted among students of an international university to capture students' perceptions. Findings revealed that university students possess diverse perspectives on the use of AI in higher education settings. Four qualitatively distinct categories of perceptions of AI emerged from the interview data. It is found that university students perceive the use of AI as a) an essential academic aid, b) a facilitator of personalized learning, c) an inhibitor to critical thinking, and d) an ethical challenger. Findings have pedagogical, administrative, and ethical implications for developing AI-driven technology-focused policy that facilitates diverse student needs.

The Ethics of AI in the Administration of Higher Education

The rising presence of artificial intelligence (AI) in higher education carries great promise and peril. This paper will examine the ethics of using AI for the operation of these institutions. Key areas of focus include data privacy, bias in decision making, accountability, and the impact on academic integrity. So is the importance of transparency, stakeholder involvement, and the need for ethical guidelines. The paper closes with suggestions for the responsible adoption of AI systems in higher education.

Examination of Research Conducted on the Use of Artificial Intelligence in Science Education

The advancement of artificial intelligence (AI) has been significantly driven by developments in machine learning and neural networks. As AI becomes increasingly pervasive, its applications are diversifying, with notable penetration in sectors such as health, education, social media, robotics, and entertainment. One area in which AI is being deployed is science education. The objective of this study is to examine the research that incorporates AI within the field of science education. By analysing trends in the reviewed studies, this research identifies the countries, institutions, journals and scholars that are the most prominent contributors to this field of enquiry. The findings suggest that the incorporation of artificial intelligence into science education is still in its infancy, with a paucity of widespread implementation. However, there is a discernible increase in the quantity of published works, with an emerging emphasis on the assessment of learning outcomes and the enhancement of academic performance. The findings indicate that the United States is the leading country in terms of publications related to AI in science education, accounting for 38% of the total contributions. Additionally, Türkiye has emerged as a notable contributor in this field, demonstrating a growing presence. The Journal of Science Education and Technology was identified as the preeminent journal publishing research on AI. Furthermore, the findings revealed that GPT was the most frequently utilised tool in this context. In light of these findings, it is recommended that future investigations into the application of artificial intelligence (AI) in science education employ a range of AI tools and explore the development of higher-order thinking skills.

Artificial Intelligence in Education

Artificial Intelligence in Education

Artificial intelligence in physical education: comprehensive review and future teacher training strategies

Artificial intelligence (AI) technology is deeply changing our lives and provides impetus for improving production and living efficiency as an important emerging tool. Digitalization and intelligent development have also become the development direction of the sports industry, bringing new requirements to the transformation of physical education (PE) and the improvement of the quality of PE teachers. PE is an important part of the public health system, and AI can deeply participate in the formulation of teaching strategies, the tracking of teaching processes and the evaluation of teaching results, effectively improving the quality of teaching. Research on the application of AI technology in PE has been carried out. This paper comprehensively reviews the existing research and conducts a comprehensive analysis of the research progress and status. The potential application areas of AI in PE are discussed to better promote the intelligent and digital upgrading of PE. We found that the research on the application of AI in PE is still in its early stages, and the research content needs to be strengthened in terms of breadth and depth. Furthermore, this paper analyzes the challenges faced by PE teacher development and training in the context of educational transformation in the era of AI, and explores the necessary skills and knowledge related to AI technology that future PE teachers should master in order to effectively achieve the improvement of teaching level and the sustainable development of public health system. The review of this paper provides valuable guidance for educators and policymakers to formulate high-quality teacher development and training mechanisms, and provides a new reference for the application and development of AI in sports.

AI MyData: Fostering Middle School Students’ Engagement with Machine Learning through an Ethics-Infused AI Curriculum

As initiatives on artificial intelligence (AI) education in K-12 learning contexts continues to evolve, researchers have developed curricula among other resources to promote AI across grade levels. Yet, there is a need for more effort regarding curriculum, tools, and pedagogy, as well as assessment techniques to popularize AI at the middle school level. Drawing on prior work, we created original curriculum activities with innovative use of existing technology, a new computational teaching tool, and a series of approaches and assessments to evaluate students’ engagement with the learning resources. Our curriculum called AI MyData comprises elements of ML and data science infused with ethical orientation. In this paper, we describe the novel AI curriculum and further discuss how we engaged students in learning and critiquing AI ethical dilemmas. We gathered data from two pilot studies conducted in the Northeast United States, one Artificial Intelligence Afterschool (AIA) program, and one virtual AI summer camp. The AIA program was carried out in a local public school with four middle school students aged 12 to 13; the program consisted of eleven two-hour sessions. The summer camp consisted of two-hour sessions over four consecutive days, with eighteen students aged 12 to 15. We facilitated both pilot programs with hands-on plugged and unplugged activities. The method of capturing data included artifact collection, structured interviews, written assessments, and a pre- to post-questionnaire tapping participants’ dispositions about AI and its societal implication. Participant artifacts, written assessments, survey, observation, and analysis of tasks completed revealed that the children improved in their knowledge of AI. In addition, the AI curriculum units and accompanying approaches developed for this study successfully engaged the participants, even without prior knowledge of related concepts. We also found an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens. Our study results also indicate that lessons establishing links with students’ personal lives (e.g., letting students choose personally meaningful datasets) and societal implications using unplugged activities and interactive tools were particularly valuable for promoting AI and the integration of AI in middle school education across the subject domains and settings. Based on these results, we discuss our findings, identify their limitations, and propose future work.

Application Of Chatgpt in The Field of Preschool Education from The Perspective of Personalized Learning

With the rapid change in science and technology, ChatGPT has become a topic of concern in all walks of life, and ChatGPT's development in education has also received extensive attention. This paper explores the application of ChatGPT in the field of preschool education against the background of John Dewey's child-centered theory in educational practice. ChatGPT's education model is of great significance to the current way of children's education, which is conducive to further optimizing the comprehensive reform of the kindergarten curriculum. Through the conclusions, it is concluded that ChatGPT can better promote the innovation and progress of education in preschool education. Based on this, this paper puts forward the following suggestions: firstly, the new artificial intelligence should be applied in preschool education from many aspects. Secondly, the use of new artificial intelligence in home, school, and social education can better realize the reform and innovation of education. Finally, the use of new learning methods can promote the development of children's abilities and provide a more personalized learning experience.

The promotion strategy of artificial intelligence on students ' creativity and critical thinking in college art education

With the rapid development of artificial intelligence, its application in art education is becoming increasingly widespread, providing students with new ways to enhance creativity and critical thinking. This paper analyses the specific application of artificial intelligence in art education and explores how it fosters students' creativity through innovative teaching methods, personalized learning, and thinking guidance in artistic creation. At the same time, the study also reveals the positive role of artificial intelligence in helping students to screen and criticize information, analyze and evaluate art works, and encourage innovative thinking and critical thinking. The research demonstrates that artificial intelligence significantly enhances students' independent thinking and critical appreciation of art, improving their artistic literacy and innovation while supporting the modernization of art education.

Artificial intelligence in management education: transformative potential and challenges

Purpose The purpose of this paper is to define artificial intelligence (AI) and examine its history, positive and negative impacts, ethical and social implications and implementation within management education. This paper offers various suggestions for the use of AI, as well as context surrounding the current AI landscape. Design/methodology/approach The paper uses a narrative review (Sylvester et al., 2013). Findings This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution. In addition, there are significant concerns regarding the lack of ethical guidelines with current AI. Practical implications Practical implications include the ability to immediately use certain AI tools to enhance lesson plans as well as enhance student work using AI as a tool. Originality/value This paper was originally created as a conference presentation and presented at the society for advancement of management (SAM) International Business Conference before being reworked to be submitted to the journal. All content in this paper is original in their creation.

Artificial Intelligence and the Development of “Specialized, Refined, Unique, and Innovative” Small‐ and Medium‐Sized Enterprises

ABSTRACT“Specialized, Refined, Unique, and Innovative” (SRUI) enterprises are a crucial part of the national innovation system and play a significant role in enhancing national core competitiveness by leveraging strengths and addressing weaknesses. Given the limited quantitative analysis on the productivity of SRUI enterprises in the academic field, this paper constructs an enterprise‐level artificial intelligence index based on text analysis and machine learning. Using panel data from 2018 to 2022, we investigate the impact of artificial intelligence on the productivity of SRUI small‐ and medium‐sized enterprises (SMEs). The research findings indicate that (1) artificial intelligence significantly enhances the productivity of SRUI SMEs, and for each one standard deviation increase in artificial intelligence, the productivity level of SRUI enterprises will increase by 6.82%. (2) Mechanism tests reveal that artificial intelligence improves productivity by optimizing labor structure, improving labor resource allocation, stimulating endogenous motivation and innovation vitality within enterprises, and enhancing management level and investment efficiency. (3) Heterogeneity analysis indicates that at the regional level, artificial intelligence significantly boosts the productivity of enterprises with well‐developed network infrastructure and robust intellectual property protection systems. At the industry level, AI has a more pronounced effect on the productivity of technology‐intensive and competitive industries. Additionally, we also find that the impact of AI on productivity is more significant in enterprises with younger executive teams and executives with digital and intelligent education backgrounds. This study expands the research scope of productivity and provides valuable insights for the government to optimize digital economy policies and for enterprises to formulate digital innovation strategies.

Assessment of lecturers' awareness of artificial intelligence for education in a Nigerian university

Assessment of lecturers' awareness of artificial intelligence for education in a Nigerian university

Attitudes of Instructors Toward the Use and Implications of Artificial Intelligence in Online Higher Education

The purpose of this study was to assess the attitudes and perceptions of instructors toward the use and implication of artificial intelligence (AI) in online higher education spanning bachelor’s through doctorate level classroom instruction. 104 online higher education instructors were recruited through LinkedIn and the Colorado Technical University Teaching & Learning Center to complete a survey. Nonparametric statistical analysis was used to assess close-ended question responses and open-ended question responses were categorized. A significant difference in instructors’ concern for being displaced by AI in the next 2 years compared to the next 5 to 10 years was found at the bachelor’s level (p=0.04846). At the doctorate level, support for the use of AI in higher education was moderately correlated with concerns for being displaced by AI (rho=0.493, p=0.01058). Support for the use of AI in higher education by the instructor was moderately correlated with support for use of AI in the classroom by students (rho= 0.58199, p<0.001). Instructor support for allowing students to use AI in the classroom was not found to be dependent on the perception of the university employer providing instructors with the policies, procedures, and training to appropriately and ethically adapt to AI in the higher education classroom (p=0.7336). However, most participants recommended that the university develop strong policies regarding the use of AI. The findings indicate a positive preference toward the use of AI by both faculty and students, with some significant exceptions. Support and research toward adopting AI in the classroom are recommended.