Artificial Intelligence In Education Research Articles

Intelligent Tutoring Systems in Mathematics Education: A Systematic Literature Review Using the Substitution, Augmentation, Modification, Redefinition Model

Scholars have claimed that artificial intelligence can be used in education to transform learning. However, there is insufficient evidence on whether intelligent tutoring systems (ITSs), a representative form of artificial intelligence in education, has transformed the teaching and learning of mathematics. To fill this gap, this systematic review was conducted to examine empirical studies from 2003 to 2023 that used ITSs in mathematics education. Technology integration was coded using the substitution, augmentation, modification, redefinition (SAMR) model, which was extended to suit ITSs in a mathematics education context. How different contexts and teacher roles are intertwined with SAMR levels were examined. The results show that while ITSs in mathematics education primarily augmented existing learning, recent ITS studies have transformed students’ learning experiences. ITSs were most commonly applied at the elementary school level, and most ITS studies focused on the areas of number and arithmetic, algebra, and geometry. The level of SAMR varied depending on the research purpose, and ITS studies in mathematics education were mainly conducted in a way that minimized teacher intervention. The results of this study suggest that the affordance of an ITS, the educational context, and the teacher’s role should be considered simultaneously to demonstrate the transformative power of ITSs in mathematics education.

How can we improve AI competencies for tomorrow's leaders: Insights from multi-stakeholders’ interaction

This study investigates factors influencing artificial intelligence (AI) competencies in higher education for sustainable development using learning theories. Utilizing a mixed-method approach, it analyzes responses from 525 students and interviews with 35 faculty members from five Indian universities. The study employs confirmatory factor analysis (CFA) and structural equation modeling (CB-SEM) to explore relationships between various learning approaches and AI competencies. Findings indicate that collaborative learning, problem-solving, and cognitive competence significantly contribute to AI proficiency. Human-tool collaboration and self-learning also enhance students' understanding of AI concepts, fostering strategic decision-making in business contexts. Additionally, thematic analysis from qualitative interviews identifies critical themes for developing an AI curriculum framework, including curriculum design, pedagogical strategies, ethical considerations, and global perspectives. The research provides an integrated theoretical model and offers practical recommendations for enhancing AI education, emphasizing the importance of interdisciplinary collaboration and ethical AI usage in management education.

Affect of Manufactured Insights on Human Misfortune in Choice Making, Sluggishness and Security in Instruction

The impact of artificial intelligence on decision-making, laziness, and privacy concerns among university students in Pakistan and China is examined in this study. Like other sectors, education uses artificial intelligence to address modern-day challenges. The investment in artificial intelligence will grow to more than $250 million over the course of the next decade. Researchers and institutions across the globe are praising the positive role of Artificial Intelligence, but ignoring its concerns. The data analysis of this study is based on qualitative methodology. The data was collected from students in different universities. The sample was drawn using the purposive sampling technique. The data analysis shows that artificial intelligence makes humans lazy. It impacts both security and privacy. The impact of artificial intelligence in Pakistan and Chinese society has been found to be a factor in the loss of decision-making. Humans are the most affected by the effects of artificial intelligence. The study argues that preventative measures are necessary before using artificial intelligence in education. It would be like putting the devils in charge of the major human concerns. In order to address the issue, it is recommended to cent rate on justified designing and deployment.

The effect of artificial intelligence tools on EFL learners' engagement, enjoyment, and motivation

The use of Artificial Intelligence (AI) tools has become increasingly prevalent within higher education and numerous studies have indicated the efficacy of AI applications within the English as a foreign language (EFL) context. Enhancing student engagement, foreign language enjoyment (FLE), and motivation in the field of AI education are significant in promoting effective learning outcomes. The rise of AI in educational settings has led to a paucity of research exploring its impact on various educational constructs. This quasi-experimental research examined the role of utilizing an AI tool, specifically Duolingo, on the engagement of FLE, and motivation of Chinese EFL learners. The present study involved 383 out of 412 Chinese university EFL students, who were assigned to either the experimental or control group. Both groups participated in pre- and post-intervention assessments prior to and following a 12-session intervention. Running a One-way Analysis of Covariance (ANCOVA) revealed statistically significant improvements in FLE, engagement, and motivation within the experimental group. On the contrary, the control group demonstrated a minor alteration in the aforementioned variables. AI tools possess the capability to captivate the attention of students and consequently enhance their motivation to actively participate in the educational experience. The results of the study designate that there are significant implications for both teachers and teacher educators concerning the incorporation of AI tools in the EFL classroom environment.

Visualizing the knowledge mapping of artificial intelligence in education: A systematic review

Visualizing the knowledge mapping of artificial intelligence in education: A systematic review

Quantifying the use and potential benefits of artificial intelligence in scientific research.

The rapid advancement of artificial intelligence (AI) is poised to reshape almost every line of work. Despite enormous efforts devoted to understanding AI's economic impacts, we lack a systematic understanding of the benefits to scientific research associated with the use of AI. Here we develop a measurement framework to estimate the direct use of AI and associated benefits in science. We find that the use and benefits of AI appear widespread throughout the sciences, growing especially rapidly since 2015. However, there is a substantial gap between AI education and its application in research, highlighting a misalignment between AI expertise supply and demand. Our analysis also reveals demographic disparities, with disciplines with higher proportions of women or Black scientists reaping fewer benefits from AI, potentially exacerbating existing inequalities in science. These findings have implications for the equity and sustainability of the research enterprise, especially as the integration of AI with science continues to deepen.

Towards a Literature Review Methodology: A Practical Guide in the Context of Using Artificial Intelligence in Education

Towards a Literature Review Methodology: A Practical Guide in the Context of Using Artificial Intelligence in Education

Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022).

Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers. The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

Virtual Laboratory and Artificial Intelligence in Science Education: Bibliometric Analysis Based on Scopus Source

This study examines the development of research related to virtual laboratories and artificial intelligence in education. With the assistance of VOSviewer, this study aims to conduct computational mapping on bibliometric data of articles on the keywords: artificial intelligence, virtual laboratories, and education. The method used in this research is bibliometric analysis using VOSviewer. Article data obtained from Scopus-based data, article titles, and abstracts are used to guide the search process by referring to the keywords "laboratorium virtual" and "artificial intelligence in education". It found 359 allegedly relevant articles. The study period used as study material in this study was for the last 5.5 years, namely from 2018 to June 2023. The results of laboratorium virtual and artificial intelligence research in education can be separated into 3 terms, namely artificial intelligence, laboratorium virtual, and education. The term artificial intelligence has 304 links with a total link strength of 12065, the term Laboratorium virtual has 416 links with a total link strength of 5451 links with an accuracy of 212 terms, and the term education has 399 links with a total link strength of 3142 accuracies of 113. The analysis results indicate a significant increase in research related to virtual laboratories and artificial intelligence in education over the past four years. However, the relationship between virtual laboratories and artificial intelligence in education is still relatively rare. In fact, there is no existing relationship between virtual laboratories and artificial intelligence. Based on this information, the opportunity for developing research on virtual laboratories based on artificial intelligence in the field of education is substantial.

Regulatory impact of a governmental approach for artificial intelligence technology implementation in Vietnam

This study assesses Vietnam’s state-level implementation of artificial intelligence (AI) technology and analyses the government’s efforts to encourage AI implementation by focusing on the National Strategy on AI Development Program. This study emphasizes the possibility of implementing AI at the state level in Vietnam and the importance of conducting continuous reviews and enhancements to achieve sustainable and inclusive AI growth. Impact evaluations were conducted in public organizations alone, and implication evaluations were considered optional. AI impact assessments were constrained by societal norms that necessitated establishing relationships among findings. There is a lack of official information regarding the positive impact of Vietnam’s AI policy on the development of AI infrastructure, research, and talent pools. The study’s findings highlight the necessity of facilitating extensive AI legislation, and strengthening international cooperation. The study concludes with the following recommendations for improving Vietnam’s AI policy: implementing a strong AI governance structure and supporting AI education and awareness.

Empowering the Faculty of Education Students: Applying AI’s Potential for Motivating and Enhancing Learning

AbstractThis study aimed to investigate how AI impacts students’ intrinsic motivation and learning experiences in various cultural and academic settings. An analysis of the effects of AI-powered learning tools on intrinsic motivation and learning enhancement was conducted in early 2024. The study recruited 455 students (192 males and 263 females) from Egypt, Saudi Arabia, Spain, and Poland who were studying diverse majors in educational faculties. Using a 5-point Likert scale, the validated instrument included three dimensions with a total of 10 items: intrinsic motivation and AI, AI and enhancing learning, and overall perception of AI in education. Significant differences were found by nationality and major, with Polish students and education technology majors showing the highest motivation levels. As a result of enhancing learning experiences, autonomy, and critical thinking skills, AI tools were found to positively influence motivation. Motivation, however, was not significantly influenced by academic level. Despite ethical concerns, students held a moderately positive view of artificial intelligence in education. Further research on optimal AI implementation and ethical considerations in education is necessary based on these findings.

The Use of Artificial Intelligence in Teaching and Learning: Opportunities and Challenges. Students Vs Lecturers Perception

The use of AI in higher learning institutions has brought a dilemma as it was in the era of using google and Wikipedia causing a lot of academic dishonest. There are two paradigm on this, some sees as opportunity for effective learning and others sees as a challenge in the teaching and learning contributing to rote learning. The author wonders as to why should it be perceived as opportunity at the same time be seen as a challenge. The perceptions of students and lecturers towards this technological advancement is always taken as a shock at the beginning, yet can significantly influence the teaching and learning in institutions as it was in the error of the use of google search and Wikipedia. This paper explores the differing viewpoints of students and lecturers on the use of AI in higher education, by examining the opportunities, challenges, and potential impacts on teaching and learning experiences. Questionnaire and interview were used as data collection tools. The study realized that students knows the use of AI in searching information than their lecturers (72%:22%). Lecturers sees AI facilitates rote learning while students see that is an opportunity to them because the information is accessed in short time. Finally they all suggest that there should be clear policies on the use of AI in teaching and learning and having acceptable ratio of human and AI interaction in the teaching and learning. The study concludes by calling upon in-service training to lecturers and other education stakeholders to have proper use of AI for effective teaching and learning.

Development of an artificial intelligence curriculum design for children in Taiwan and its impact on learning outcomes

In the digital age, the application of Artificial Intelligence (AI) has become an irreversible trend, with its potential in the field of education being particularly noteworthy. However, there are currently few AI education programs for children in Taiwan, and there is a lack of systematic teaching resources and methods, which poses a major challenge to the promotion of AI education. To address this challenge, this study designed a tailor-made AI curriculum for children in Taiwan, aimed at enhancing their foundational knowledge in the AI field and skills in using generative AI. This study was conducted in Taiwan, involving 30 elementary school students from grades 3 and 4, employing a single-group pre-test and post-test research design. Data were collected and analyzed through pre-and post-tests on AI knowledge quizzes and AI knowledge self-assessment, as well as the expert consensual assessment technique and the pupils’ attitude toward technology survey questionnaire. Research shows that students who participated in the AI course significantly improved their test scores on AI knowledge before and after the course. The students AI knowledge increased by 62.75%, demonstrating the course’s effectiveness and showing a positive attitude towards AI technology. Additionally, the student’s project outcomes demonstrated a high level of creativity. The students exhibited an enhanced interest and positive attitude towards learning AI, expressing a willingness to participate in more AI educational courses. This work provides valuable experience and guidance for the future integration of AI technology in children’s education, offering practical guidelines for teachers and researchers on how to effectively teach AI knowledge, as well as serving as a robust reference for educational policy makers in formulating strategies to promote AI education.

Decoding AI ethics from Users' lens in education: A systematic review

In recent years, Artificial Intelligence (AI) has witnessed remarkable expansion, greatly benefiting the education sector. Nonetheless, this advancement brings forth several ethical dilemmas. The existing research on these ethical concerns within the educational framework is notably scarce, particularly when viewed from a user's standpoint. This research systematically reviewed 17 empirical articles from January 2018 to June 2023, sourced from peer-reviewed journals and conferences, to outlined existing ethical framework in Artificial Intelligence in Education (AIED), identify related concerns from user's perspectives, and construct Ethics Guideline for AIED. The finding revealed that certain ethical aspects, including the ethics of learning analytics and the ethics of algorithms in AIED, are often neglected in the existing ethical frameworks, principles, and standards for AIED. Based on the blank between existing ethical frameworks and ethic concerns from user's perspectives, the research proposes more inclusive and thoughtfully Ethics Guideline for AIED. The study also provides actionable recommendations for multiple stakeholders, emphasizing the need for guidelines that address user-centered concerns. In addition, How this Ethics Guideline for AIED could be developed is discussed, along with outlining potential avenues for future research.

Artificial intelligence in education: implications for academic integrity and the shift toward holistic assessment

This study examines the impact of Artificial Intelligence (AI) on the field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. This research fits within the specific setting of university students and faculty members in the Kingdom of Bahrain.MethodsA cross-sectional survey was designed to examine the impact Artificial Intelligence (AI) in field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. A total of 218 participants were randomly selected from 250 employed in this survey study.ResultsOut of 250 invited participants, 203 responded to the survey. This study evaluated the influence of Educational Impact (EI), Policy and Ethics (PE), and Pedagogical Implications (PI) on Academic Outcomes (AO). Results revealed a significant association between EI → AO with a beta of 0.490, t-value of 4.504, and p < 0.001. PI also showed a significant relationship (β = 0.454, t = 2.330, p = 0.010) with more variability. PE’s impact on AO was modest (β = 0.243, t = 1.977, p = 0.024). Overall, EI was the strongest AO predictor. The R2 value was approximately 39%, indicating a good fit.ConclusionThe research reveals a strong link between the Educational Impact (EI) of AI and academic success in Bahrain’s universities, with EI being the primary predictor. Both Policy and Ethics (PE) and Pedagogical Implications (PI) play crucial roles in this relationship.

LAMB: An Open-Source Software Framework to Create Artificial Intelligence Assistants Deployed and Integrated into Learning Management Systems

This paper presents LAMB (Learning Assistant Manager and Builder), an innovative open-source software framework designed to create AI-powered Learning Assistants tailored for integration into learning management systems. LAMB addresses critical gaps in existing educational AI solutions by providing a framework specifically designed for the unique requirements of the education sector. It introduces novel features, including a modular architecture for seamless integration of AI assistants into existing LMS platforms and an intuitive interface for educators to create custom AI assistants without coding skills. Unlike existing AI tools in education, LAMB provides a comprehensive framework that addresses privacy concerns, ensures alignment with institutional policies, and promotes using authoritative sources. LAMB leverages the capabilities of large language models and associated generative artificial intelligence technologies to create generative intelligent learning assistants that enhance educational experiences by providing personalized learning support based on clear directions and authoritative fonts of information. Key features of LAMB include its modular architecture, which supports prompt engineering, retrieval-augmented generation, and the creation of extensive knowledge bases from diverse educational content, including video sources. The development and deployment of LAMB were iteratively refined using a minimum viable product approach, exemplified by the learning assistant: “Macroeconomics Study Coach,” which effectively integrated lecture transcriptions and other course materials to support student inquiries. Initial validations in various educational settings demonstrate the potential that learning assistants created with LAMB have to enhance teaching methodologies, increase student engagement, and provide personalized learning experiences. The system's usability, scalability, security, and interoperability with existing LMS platforms make it a robust solution for integrating artificial intelligence into educational environments. LAMB's open-source nature encourages collaboration and innovation among educators, researchers, and developers, fostering a community dedicated to advancing the role of artificial intelligence in education. This paper outlines the system architecture, implementation details, use cases, and the significant benefits and challenges encountered, offering valuable insights for future developments in artificial intelligence assistants for any sector.

Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity

With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT domain. To address this challenge, this paper applies quantum computing (QC) technology to KT for the first time. It proposes two personalized KT models incorporating quantum mechanics (QM): quantum convolutional enhanced knowledge tracing (QCE-KT) and quantum variational enhanced knowledge tracing (QVE-KT). Through quantum superposition and entanglement properties, QCE-KT and QVE-KT effectively alleviate the data sparsity problem in the KT domain through quantum convolutional layers and variational quantum circuits, respectively, and significantly improve the quality of the representation and prediction accuracy of students’ knowledge states. Experiments on three datasets show that our models outperform ten benchmark models. On the most sparse dataset, QCE-KT and QVE-KT improve their performance by 16.44% and 14.78%, respectively, compared to DKT. Although QC is still in the developmental stage, this study reveals the great potential of QM in personalized KT, which provides new perspectives for solving personalized instruction problems and opens up new directions for applying QC in education.

Factors Influencing University Students' AI Use and Knowledge Acquisition

ABSTRACTDespite the growing emphasis on artificial intelligence (AI) education, there is relatively little research on the motivational factors that influence students' intention regarding AI knowledge acquisition and the utilization of AI applications. Understanding these factors helps educators and researchers to develop appropriate interventions to promote AI education. Guided by expectancy‐value theory and theory of planned behavior, we examined how university students' beliefs influenced their motivation to learn about and engage with AI technologies. Our findings demonstrated the significant role of expectancy‐value beliefs in shaping students' behavioral intention. Additionally, we identified gender differences, which can inform educators in designing gender‐specific interventions to enhance female students' motivation in AI learning.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

Aim/objectiveTo review the current AI applications in healthcare and explore the implications for nurse educators in innovative integration of this technology in nursing education and training programs. BackgroundThere are a variety of Artificial Intelligence (AI) applications currently supporting patient care in many healthcare settings. A nursing workforce that leverages healthcare technology to enhance efficiency and accuracy of patient health outcomes is necessary. Nurse educators must understand the various uses of AI applications in healthcare to equip themselves to effectively prepare students to use the applications. DesignQualitative synthesis and analysis of existing literature. MethodsGenerative AI (ChatGPT) was used to support the development of a list of the current AI applications in healthcare. Each application was evaluated for relevance and accuracy. A literature review to define and understand the use of each application in clinical practice was completed. The search terms “AI” and “Health Education” were used to review the literature for evidence on educational programs used for training learners. ResultsTen current applications of AI in healthcare were identified and explored. There is little evidence that outlines how to integrate AI education into educational training for nurses. ConclusionA comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

User Experience of ChatGPT: Insights from Undergraduate English Language and Literature Students in Bangladesh

ChatGPT, a large language model, has become increasingly popular, and students have begun to use it for the purpose of learning. The purpose of this research is to explore the experiences of undergraduate English language and literature students from Bangladesh who are using ChatGPT. Qualitative research methods were utilized for this study and the data were collected using semi-structured interviews. A thematic analysis technique was employed to analyze the data. The study reveals both positive and negative experiences associated with the use of ChatGPT by students from the English Language and Literature department. ChatGPT helps students to practice their reading and writing skills. It aids them to provide literary explanations of their texts and helps students to understand complex literary texts in lucid language. However, it sometimes provides confusing or wrong answers and provides wrong explanations regarding literary texts. As students possess some negative experiences along with many positive experiences, they are inclined to continue using it with heightened awareness of their use. However, users in this study believe that its use should be limited, and it should be used as a complement rather than a primary tool. The findings of this research may provide educators and policymakers with valuable insights that might help them integrate new aspects of artificial intelligence in education.