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Predicting graduates' first destinations in advance is crucial for targeted career planning and strategic curriculum development. The relationship between first destinations and academic performance exhibits typical high-dimensional networks and varying degrees of correlation characteristics. To reveal this complex relationship, we developed a prediction and visual analysis method based on causal inference for predicting graduates' first destinations. First, we collaborated with university administrators to conceive the First Destination and Academic Performance (FDAP) dataset, which aims to define the available attributes related to academic performance and first destination. Second, we propose a key feature selection method based on the causal inference and random forest (CIRF), which both extracts key features in FDAP for training models and reveals causal relationships in the FDAP dataset. Third, we propose a novel prediction model, CIRF-MLP, which combines the CIRF method with a multilayer perceptron neural network that can predict students' first destination based on their current academic performance. When benchmarked against four other baseline prediction models on the real FDAP dataset, our model showcased exceptional performance, and the ablation experiment results demonstrate the necessity of each model component. Fourth, we developed CausalCareerVis, a visual analysis system built on the CIRF-MLP model, to analyze the causality and correlation between graduates' First Destination and Academic Performance, and to predict their first destinations based on academic performance. Three case studies highlighted the effectiveness and practicality of our work. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing the adoption of Generative-AI tools amongst Thai university students, employing the Technology Acceptance Model (TAM) as a theoretical framework. Data from 911 higher education students from 10 different Thai Universities Health Sciences, Sciences and Technology, Social Sciences and Humanities, and Vocational Fields were analysed via Structural Equation Modelling (SEM). The instrument used in collecting the data was a questionnaire. Results indicated that Expected Benefits, Perceived Usefulness, Attitude Toward Technology, and Behavioural Intention all significantly impacted student adoption of Generative AI. Intriguingly, Perceived Ease of Use was negatively correlated with Perceived Usefulness, challenging conventional TAM assumptions. This study underscores the need to address language barriers, foster a culture of innovation, and establish ethical guidelines to promote responsible AI use within education. Despite inherent limitations, this research contributes to our understanding of AI adoption in educational settings and helps inform strategies for equitable access and responsible innovation. The result demonstrated that the easier a tool was to use, the less value leaners seemed to see in it for their learning process. It can be implied that as Generative- AI get more intuitive, learners think they're less helpful. These finding challenges a few of those assumptions we usually make within the TAM model. It also points out the characteristic of learners which affects their learning preferences and expectation. Another finding showed the impact of language barrier on non-native English speaker that obstruct the user experience in AI services. Moreover, the role of universities in fostering both AI integration for learning for and the ethical implementation of Generative AI. By providing a supportive environment that encourages AI experimentation, redesign learning, empowering learners and faculty instructors to investigate how Generative AI can be applied across disciplines, and developing guidelines for ethical use, universities play a critical role in shaping the effective and responsible integration of AI into the next educational landscape. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) has received scholarly attention and is becoming a common trend in the field of education, while the cultivation of higher order thinking (HOT) skills still remains as one of the crucial educational objectives. Recent years have seen substantial amount of research exploring the influence of Artificial Intelligence in Education (AIEd) use on each HOT skills of students, which necessitated a systematic review to amalgamate the findings quantitatively. This meta-analysis study set out to explore the effect of AIEd on students’ separate HOT skills and identify the effect of potential moderator variables. The analysis included 29 articles with a total of 2.966 participants between the years of 2000 and 2023. This research confirmed the use of AIEd had a medium positive effect on skills classified as HOT skills. The findings suggested that instructional settings which incorporated AI technologies enhanced students’ HOT skills 9.3% higher. This review also found that the variables of country, year of publication and AI tool/agent moderated the magnitude of effect sizes significantly. The findings offer extensive examination of AI technologies in educational settings and contribute to understanding of how and to what extent they influence students’ HOT skills. [ABSTRACT FROM AUTHOR]

The evolution of artificial intelligence (AI) technology offers promising solutions to underserved populations in the United States. AI tools can reduce college access and academic barriers for low‐income students, first‐generation college students, racial and ethnic minorities, individuals with disabilities, and students living in rural areas. While AI tools offer support to college students from all demographics, they offer uniquely individualized assistance to underserved students to overcome the complex postsecondary schooling challenges they face. Examples of the nuanced, often complex, obstacles faced by these vulnerable students are discussed along with brief descriptions of AI tools that can help. AI tools leverage the power of high‐speed computing, expansive access to information, machine learning capabilities, and natural language processing. Utilizing the power of AI tools, vulnerable learners can increase their access to financial, academic, and social resources needed to attend and be successful in postsecondary education. AI tools also help close opportunity gaps as they provide means by which college students gain increased independence and control over their educational and career journeys. Note that the AI tools referenced in this article are included as examples only. We encourage ongoing discussion of AI‐powered tools and solutions to help underserved students gain more access to higher education and get individualized support for vulnerable learners. Practical Takeaways: Highlighting the potential of AI in addressing educational inequalities.Using AI in higher education settings for educators to better understand and respond to student needs.Implementing AI usage in higher education settings for underserved populations to reduce dropout rates.Leveraging technology to offer tailored guidance, bridging gaps, and fostering community among the underserved populations in higher education. [ABSTRACT FROM AUTHOR]

The growing use of artificial intelligence in education extends and intensifies technologies of governing, including datafication, performativity and accountability. In this article, we outline how the use of AI and data science has the disruptive potential to create new norms in education policy and governance. We report on an ethnographic investigation into an education department initiative that involved data scientists using machine learning to identify causal links between different school-based and non-school based factors, and educational outcomes. The article proposes that machine learning is being introduced into this education department as a set of minor experiments in education governance. We focus on issues of changing expertise and evidence and differences in method between new data science approaches and established statistical expertise in this education department, in order to highlight minor but important new governance practices. We conclude that the increasing use of AI in education, as part of new cognitive infrastructures, is quietly shifting the ways in which education systems operate. [ABSTRACT FROM AUTHOR]

As generative AI tools are increasingly popular in today’s teaching and learning process, challenges and opportunities occur at the same time. Self-directed learning has been regarded as a powerful learning ability that supports learners in informal learning contexts and its importance rises in salience when incorporating AI into learning. This study employed a mixed-method design to understand how people self-direct their online language learning through the utilization of ChatGPT. Analyzing survey data from 276 survey respondents and 11 one-to-one interviews with language learners in the United States, we found that learners are motivated to use generative AI for its high flexibility and personalization which enables learners to access learning materials that align with their knowledge levels, personal interests, and learning goals. We also found self-monitoring skills that are inherent to learners help them to use ChatGPT more effectively and achieve their learning goals efficiently. This study contributes to the emerging field of AI in education and explores possibilities to use ChatGPT to foster self-directed language learning and provide educators, instructional designers, and researchers with insights to design learning integrated with AI to best fulfill learners’ diverse needs and expand learning opportunities to more people. [ABSTRACT FROM AUTHOR]

The rapid advent of artificial intelligence (AI) is influencing education systems at all levels. Understanding the wide-ranging potential implications of AI, the Indian Central Board of Secondary Education (CBSE) has produced an AI integration manual consisting of details, strategies and approaches to successfully integrate AI in teaching and learning processes in its affiliated schools. Using thematic analysis, this study attempts to explore the promises and potentialities of AI in K–12 education with reference to the initiatives taken by the CBSE in its school system. Results show that the CBSE has taken two-fold initiatives to integrate AI in its school curricula. The first is to introduce AI as an independent subject of study, and the second is to integrate AI as a parallel pedagogical approach to instruct other multidisciplinary school subjects and vice versa. Finally, the study makes some concluding remarks regarding innovative AI incorporation in K–12 education. [ABSTRACT FROM AUTHOR]

The proliferation of artificial intelligence (AI) technology has brought both innovative opportunities and unprecedented challenges to the education sector. Although AI makes education more accessible and efficient, the intentional misuse of AI chatbots in facilitating academic cheating has become a growing concern. By using the indirect questioning technique via a list experiment to minimize social desirability bias, this research contributes to the ongoing dialog on academic integrity in the era of AI. Our findings reveal that students conceal AI-powered academic cheating behaviors when directly questioned, as the prevalence of cheaters observed via list experiments is almost threefold the prevalence of cheaters observed via the basic direct questioning approach. Interestingly, our subsample analysis shows that AI-powered academic cheating behaviors differ significantly across genders and grades, as higher-grade female students are more likely to cheat than newly enrolled female students. Conversely, male students consistently engage in academic cheating throughout all grades. Furthermore, we discuss potential reasons for the heterogeneous effects in academic cheating behavior among students such as gender disparity, academic-related pressure, and peer effects. Implications are also suggested for educational institutions to promote innovative approaches that harness the benefits of AI technologies while safeguarding academic integrity. [ABSTRACT FROM AUTHOR]

The philosophy for information system (IS)-related projects, such as artificial intelligence (AI) projects, embodies systematic and scientific approaches, that encompass the development, use, and applications of IS by focusing on the interactions among individuals, organizations, and society. However, many organizations still need to learn more about such approaches to ensure the success of AI projects. Design Thinking offers an opportunity to transform the way organizations design and develop strategies by integrating a human point of view with the possibility of implementing emerging technologies. The practice of Design Thinking provides some tools and methods for the problem-solving aspects of the development and implementation of AI initiatives. Bringing Design Thinking to AI projects is worth exploring, especially in bridging the creativity gap in terms of user-centric, innovative mindsets and toolsets. How can Design Thinking be embedded in the learning processes and enabled in the context of AI projects in the classroom environment? This study presents a technological data-driven design approach to provide an overview of the most important points and directions for an organization's AI strategy in the proof-of-concept stage. A case study of AI initiatives utilizing computer vision (CV)–AI to improve analytical solutions for trade exhibitions is presented to capture the ideas and solutions and depict the practicability and implications of our proposed framework. The results of this study show that Design Thinking practices can be enlisted to help students frame their creations and that these practices have a valuable contribution as alternatives when designing AI-related curricula for teaching and learning. [ABSTRACT FROM AUTHOR]

Online education, while not a new phenomenon, underwent a monumental shift during the COVID-19 pandemic, pushing educators and students alike into the uncharted waters of full-time digital learning. With this shift came renewed concerns about the integrity of online assessments. Amidst a landscape rapidly being reshaped by online exam/homework assistance platforms, which witnessed soaring stocks as students availed its questionable exam assistance, and the emergence of sophisticated artificial intelligence tools like ChatGPT, the traditional methods of assessment faced unprecedented challenges. This paper presents the results of an observational study, using data from an introductory statistics course taught every semester by the author, and delves into the proliferation of cheating methods. Analyzing exam score results from the pre and post introduction of ChatGPT periods, the research unpacks the extent of cheating and provides strategies to counteract this trend. The findings starkly illustrate significant increases in exam scores from when exams of similar difficulty were administered in person (pre-Covid) versus online. The format, difficulty, and grading of the exams was the same throughout. Although randomized controlled experiments are generally more effective than observational studies, we will indicate when we present the data why experiments would not be feasible for this research. In addition to presenting experimental findings, the paper offers some insights, based on the author's extensive experience, to guide educators in crafting more secure online assessments in this new era, both for courses at the introductory level and more advances courses The results and findings are relevant to introductory courses that can use multiple choice exams in any subject but the recommendations for upper-level courses will be relevant primarily to STEM subjects. The research underscores the pressing need for reinventing assessment techniques to uphold the sanctity of online education. [ABSTRACT FROM AUTHOR]

This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023–2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The "Artificial Intelligence Awareness Scale", developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs. [ABSTRACT FROM AUTHOR]

Affective intelligent tutoring systems (ATSs) are gaining recognition for their role in personalized learning through adaptive automated education based on students' affective states. This scoping review evaluates recent advancements and the educational impact of ATSs, following PRISMA guidelines for article selection and analysis. A structured search of the Web of Science (WoS) and Scopus databases resulted in 30 studies covering 27 distinct ATSs. These studies assess the effectiveness of ATSs in meeting learners' emotional and cognitive needs. This review examines the technical and pedagogical aspects of ATSs, focusing on how emotional recognition technologies are used to customize educational content and feedback, enhancing learning experiences. The primary characteristics of the selected studies are described, emphasizing key technical features and their implications for educational outcomes. The discussion highlights the importance of emotional intelligence in educational environments and the potential of ATSs to improve learning processes. This review identifies gaps in the current research and suggests future directions, including broader implementation across diverse educational settings and deeper integration of affective data to refine system responsiveness. Future research should investigate the integration of advanced natural dialogue modules and generative AI to create more sophisticated interfaces, underscoring the role of affective adaptation in educational technology. [ABSTRACT FROM AUTHOR]

In the 1970s, research on artificial intelligence in education emerged with the aim of acknowledging and accommodating the psychological aspects of the learning process. Since then, its applications have evolved and it is now used for student learning and assessment, teachers' pedagogical practice, management of educational institutions, and lifelong learning. Nevertheless, the ethical challenges of educational programmes using these systems have not been thoroughly studied. Anchored on the theoretical frame of dialogic ethics, this paper presents a section of a participatory futures research project. The goal of the research is to develop a toolkit that educators can use to ensure a smooth and ethical transition to artificial intelligence-based education while preserving the interests of educational development. This paper emphasises the need for an informed and participatory process that involves all stakeholders and begins with an expert consultation through the Delphi method, the results of which allowed the construction of eight hypothetical futures scenarios. These scenarios provide evidence that examining the ethics of using artificial intelligence systems presents an opportunity to reflect on the ethics of education as a whole. They highlight the challenge of balancing the benefits and drawbacks of such systems, especially concerning educational goals and the interplay between diverse educational actors and personal development in educational settings. The study outcomes are intended to encourage discussions on the integration of ethical artificial intelligence in education and facilitate the continuing professional development of teachers by equipping them with scenarios that can be used as a resource for training purposes. [ABSTRACT FROM AUTHOR]

Online collaborative learning has been broadly applied in higher education. However, learners face many challenges in collaborating with one another and coregulating their learning, leading to low group performance. To address the gaps, this study proposed an artificial intelligence (AI)-enabled feedback and feedforward approach that not only provide feedback but also offer recommendations for future actions to support online collaborative learning. In total, 153 college students participated in this study, and they were divided into three conditions. Fifty-one students conducted online collaborative learning with the AI-enabled feedback and feedforward approach, another 51 students carried out online collaborative learning with the AI-enabled feedback approach, and the remaining 51 students participated in traditional online collaborative learning without either type of support. The results indicated that the AI-enabled feedback and feedforward approach could significantly boost the level of collaborative knowledge building, coregulated behaviours, and group performance. Research and practical implications of the findings are discussed in depth. [ABSTRACT FROM AUTHOR]

The rapid advancements in generative AI models present new opportunities in the education sector. However, it is imperative to acknowledge and address the potential risks and concerns that may arise with their use. We analyzed Twitter data to identify critical concerns related to the use of ChatGPT in education. We employed BERT-based topic modeling to conduct a discourse analysis and social network analysis to identify influential users in the conversation. While Twitter users generally expressed a positive attitude toward using ChatGPT, their concerns converged into five categories: academic integrity, impact on learning outcomes and skill development, limitation of capabilities, policy and social concerns, and workforce challenges. We also found that users from the tech, education, and media fields were often implicated in the conversation, while education and tech individual users led the discussion of concerns. Based on these findings, the study provides several implications for policymakers, tech companies and individuals, educators, and media agencies. In summary, our study underscores the importance of responsible and ethical use of AI in education and highlights the need for collaboration among stakeholders to regulate AI policy. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technology can enhance picture book reading experience. The present study aimed to explore the influence of reading AR picture books on primary school students' reading comprehension, story retelling, and reading motivation. Eighty second graders, who were from two classes at a rural primary school in a coastal city in East China, were recruited through convenience sampling to participate in this quasi-experimental study. The two classes were randomly assigned to be either the experimental group (AR picture book reading) or control group (print picture book reading), and both groups read three traditional Chinese picture books during reading classes throughout three weeks. T-test analyses revealed that, before the study began, the two groups did not have significant differences in reading comprehension, story retelling, or reading motivation. However, after the three weeks of instructional intervention, the experimental group significantly outperformed the control group in all three aspects. Furthermore, MANCOVA analysis showed that reading AR picture books was more effective in boosting participants' performance on implicit questions during the reading comprehension tests. They also performed significantly higher on story retelling tests, especially when it came to story structures regarding settings and plots. In addition, AR picture book reading was more conducive to improving participants' level of attention and confidence in reading. This work adds to the ongoing endeavors in incorporating AR picture books into classroom settings, and will inform future development of original Chinese picture books with AR technology. [ABSTRACT FROM AUTHOR]

The future of education relies on the integration of information technologies, emphasizing the importance of equity and inclusiveness for quality education. Teacher education programs are essential for fostering qualified educators for the future. Integrating AI in education is crucial to ensure inclusivity and comprehensive services for all. This study aims to evaluate student teachers' perceptions of using AI in learning and teaching, and to provide suggestions for enhancing sustainable education through information technologies. A qualitative research design was adopted to gather perceptions and experiences from 240 student teachers who participated in a seminar on AI usage and completed self-reflection tasks. These student teachers, enrolled in various teaching methods and principal courses, contributed to the thematic analysis. The study reveals that AI should be carefully planned and incorporated into lesson plans to enhance personalized learning. Student teachers reported that AI supports and motivates the learning process, effectively transforming students' needs and learning experiences. However, they also noted potential drawbacks, such as AI imposing restrictions on the teaching profession, replacing teachers, and producing biased results. The study suggests that capacity-building strategies for student teachers should be enriched across different courses to raise awareness about AI's applications. [ABSTRACT FROM AUTHOR]

The relevance of the research lies in the necessity of including new technologies (in particular, generative artificial intelligence) in the process of language education in order to adapt students and teachers to new conditions of the information environment, to master new skills and abilities, to improve the quality of education. The highly productive value of generative artificial intelligence for teaching Russian as a foreign language at the level of professional education in higher education is emphasised. The aim of the research is to reveal the learning potential of generative artificial intelligence, which is achieved by applying the following methods: modelling of the learning process with the use of generative artificial intelligence; observation; generalisation of experience and forecasting of students’ achievements with regard to the development of linguistic and communicative competence, as well as flexible skills in academic and subsequent professional activities; linguodidactic analysis; descriptive method. The materials of the work have a pronounced practice-oriented character, as they represent a set of tasks and exercises, composed (performed) with the use of systems based on generative artificial intelligence (Gemini (Bard), Kandinsky, GPTchat (OpenAI), Perplexity, Shedevrum). The tasks are aimed at developing linguistic, communicative, professional, ICT-competences, as well as cognitive (analytical, generative and other) and creative abilities, and other flexible skills (Soft Skills) relevant for academic and further professional activities in the language being learnt (here - Russian as a foreign language). The limitations of generative artificial intelligence in terms of understanding the nuances of linguistic-cultural, social, emotional-psychological, stylistic context, which determines the role of the teacher in the learning process, are noted. We emphasise not so much mentoring, but rather cooperative-activity functions of the teacher, who simultaneously learns together with his students by accessing a resource that is gigantic in terms of information volume and technological capabilities, and directs, supervises, and corrects the activity of generative artificial intelligence and students. The prospects of the research are outlined, which imply the expansion of generative artificial intelligence technology possibilities in language education.

This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023–2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The “Artificial Intelligence Awareness Scale”, developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.

Abstract Online education, while not a new phenomenon, underwent a monumental shift during the COVID-19 pandemic, pushing educators and students alike into the uncharted waters of full-time digital learning. With this shift came renewed concerns about the integrity of online assessments. Amidst a landscape rapidly being reshaped by online exam/homework assistance platforms, which witnessed soaring stocks as students availed its questionable exam assistance, and the emergence of sophisticated artificial intelligence tools like ChatGPT, the traditional methods of assessment faced unprecedented challenges. This paper presents the results of an observational study, using data from an introductory statistics course taught every semester by the author, and delves into the proliferation of cheating methods. Analyzing exam score results from the pre and post introduction of ChatGPT periods, the research unpacks the extent of cheating and provides strategies to counteract this trend. The findings starkly illustrate significant increases in exam scores from when exams of similar difficulty were administered in person (pre-Covid) versus online. The format, difficulty, and grading of the exams was the same throughout. Although randomized controlled experiments are generally more effective than observational studies, we will indicate when we present the data why experiments would not be feasible for this research. In addition to presenting experimental findings, the paper offers some insights, based on the author's extensive experience, to guide educators in crafting more secure online assessments in this new era, both for courses at the introductory level and more advances courses The results and findings are relevant to introductory courses that can use multiple choice exams in any subject but the recommendations for upper-level courses will be relevant primarily to STEM subjects. The research underscores the pressing need for reinventing assessment techniques to uphold the sanctity of online education.

Educators have widely adopted artificial intelligence (AI) as a product of technology to prepare teaching materials and enhance their understanding of technology integration in language teaching. As prospective teachers who may teach digital native students later, pre-service teachers must be more capable and knowledgeable in technologically-based pedagogy, materials, and assessment. This study aimed to explore English as a foreign language (EFL) pre-service teachers' technological knowledge of utilizing AI-powered tools using the technological pedagogical content knowledge (TPACK) framework and investigate their strategies in the advanced of their technological knowledge. This mixed-method research design employed a five-point Likert scale questionnaire and a semi-structured interview as instruments to collect data. Fifty-five EFL pre-service teachers were purposively selected. Data from the questionnaire were analyzed statistically and descriptively, and data from interviews were analyzed thematically. The first findings of this study comprehensively revealed that EFL pre-service teachers exhibit a moderate level of proficiency in addressing technological knowledge (TK), technological pedagogical knowledge (TPK), technological content knowledge (TCK), and TPACK in utilizing AI-powered tools. Meanwhile, the second finding revealed that the participants employed four key strategies to advance their technological knowledge using AI-powered tools: engaging in TPD programs, collaborating with tech-savvy colleagues, staying informed about AI trends, and experimenting with AI-powered tools. [ABSTRACT FROM AUTHOR]

This column shows that Filipino instructors and administrators collectively acknowledge AI as a transformative tool with the potential to enhance teaching, streamline administrative tasks, and boost research productivity. They highlight AI's benefits in facilitating learning with specific examples, but recognize its limitations, particularly in contextualizing experiences. While they positively regard AI tools at work, they have some concerns such as cheating, data fabrication, and potential decline in creativity and critical thinking. Hence, they emphasize the necessity of regulatory policies and ethical guidelines governing AI use in higher education. They also report whether their home institutions have created AI policies. This column advocates for Philippine educational institutions to reassess their stance on AI's increasing presence. [ABSTRACT FROM AUTHOR]

In this series, we explore technology-related themes and topics. The series aims to discuss and demystify what may be new areas for some readers and to consider their relevance for English language teachers. This paper explores the potential applicability of ChatGPT—a generative, text-based artificial intelligence (AI) chatbot—to ELT. It offers insights and guidelines for using ChatGPT to develop receptive and productive skills. First, ChatGPT can help teachers generate input materials for listening and reading practices. Second, with ChatGPT, teachers can create individualized opportunities for students to practice their speaking and writing skills. We also note important caveats for teachers to consider when implementing ChatGPT as an instructional tool. By exploring its potential benefits and limitations, this paper contributes to the growing discourse on technology integration in ELT and offers practical recommendations for creating a productive learning environment using AI-driven language models like ChatGPT. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) in secondary social studies education is revolutionizing the way students prepare for their future. By embedding 'Future Ready Practical Life Skills' into the curriculum, educators can effectively address the question, "Why do I have to learn this?" and equip students with the necessary competencies for success in college, careers, and active civic life in an AI-dominant future. This article explores how AI nurtures critical thinking, digital literacy, adaptability, ethical reasoning, and civic engagement -- skills that are crucial not only for navigating advanced technologies but also for dealing with daily life situations. By creating a dynamic and personalized learning experience, AI-driven social studies education fosters vital competencies for students to confidently face challenges and make informed decisions in their personal and civic lives. This paradigm shift in teaching and learning sets students up for success in an AI-shaped future while empowering them to navigate the complexities of daily life. [ABSTRACT FROM AUTHOR]

The author explores the use of advanced digital technology, such as generative artificial intelligence (AI), by art and media educators in teaching visual culture. Topics discussed include the importance of critical thinking in using digital technology in promoting civic engagement in students, and the need for art and media educators to consider the impact of AI on existing inequities and discriminations.

The introduction and implementation of Artificial Intelligence (AI) in higher education has brought out new opportunities and obstacles. The utilization of AI will result in a significant transformation of the governance structure within global higher educational institutions. The potential use of AI involves exploring the educational implications of how teachers may enhance their teaching methods, how students can improve their learning experience, and how institutions of higher education can make more accurate and timely judgments. This is significant because the workload has significantly increased as a result of the widespread expansion of higher education. Given the circumstances, AI assistance is crucial. The implementation of artificial intelligence in higher education is a significant matter in this context. The objective of this study is to investigate the feasibility of individuals adopting it. To do this, we have formulated hypotheses and a conceptual framework, which we then validated through a survey by obtaining feedback from a total of 240 respondents. Research has discovered that the model can assist authorities in promoting the implementation of artificial intelligence in higher education. The outcome of this study will help practitioners understand the insights of people's intentions and psychology in adopting AI in educational sectors. [ABSTRACT FROM AUTHOR]

Copyright of Western Anatolia Journal of Educational Sciences (WAJES) / Batı Anadolu Eğitim Bilimleri Dergisi is the property of Dokuz Eylul University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Western Anatolia Journal of Educational Sciences (WAJES) / Batı Anadolu Eğitim Bilimleri Dergisi is the property of Dokuz Eylul University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The world eagerly anticipates advancements in AI technologies, with substantial ongoing research on the potential AI applications in the domain of education. The study aims to analyse publications about the possibilities of artificial intelligence (AI) within higher education, emphasising their bibliometric properties. The data was collected from the Scopus database, uncovering 775 publications on the subject of study from 2000 to 2022, using various keywords. Upon analysis, it was found that the frequency of publications in the study area has risen from 3 in 2000 to 314 in 2022. China and the United States emerged as the most influential countries regarding publications in this area. The findings revealed that “Education and Information Technologies” and the “International Journal of Emerging Technologies in Learning” were the most frequently published journals. “S. Slade” and “P. Prinsloo” received the most citations, making them highly effective researchers. The co-authorship network primarily comprised the United States, Saudi Arabia, the United Kingdom, and China. The emerging themes included machine learning, convolutional neural networks, curriculum, and higher education systems are co-occurred with AI. The continuous expansion of potential AI technologies in higher education calls for increased global collaboration based on shared democratic principles, reaping mutual advantages. [ABSTRACT FROM AUTHOR]

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating artificial intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system's effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the potential of AI in transforming language education but also highlights the critical role of cultural sensitivity in designing educational technologies. [ABSTRACT FROM AUTHOR]

The growing impact and importance of artificial intelligence in society has led to an increasing interest for the potential of artificial intelligence as an educational tool in schools to aid both students and teachers. In this study we investigate digitally skilled K-12 mathematics teachers’ (N=85) attitudes towards and expectations on the role of artificial intelligence in the classroom. The study was done by conducting and analyzing the results of a web-based survey among Swedish and Finnish speaking mathematics teachers using a mixed methods strategy. The Will, Skill and Tool framework was used for the analysis. The survey was done before the introduction of ChatGPT-3. The results indicate that the teachers’ attitudes toward AI tools in school are characterized by interest, openness, and awareness. Teachers have a balanced view on the possibilities and risks of AI use in school. However, the teachers also stress that there is a risk that AI tools will shift the focus from learning key mathematical skills towards learning and interaction with the AI tools themselves. The research concluded that the K-12 mathematics teachers surveyed have broad experience with digital tools and will likely become early adopters of AI tools in the classroom. [ABSTRACT FROM AUTHOR]

Copyright of Route Educational & Social Science Journal (Ress Journal) is the property of Ress Academy Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International & Comparative Education is the property of International & Comparative Education Editorial, Beijing Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article explores the growing body of research evidence supporting the integration of Artificial Intelligence (AI) in elementary social studies education. It identifies and analyzes ten key evidence-based applications of AI that have demonstrated potential to enhance student engagement, personalize learning experiences, and cultivate essential historical thinking skills. The discussion critically evaluates the pedagogical implications, advantages, and challenges associated with AI integration in this context. Recommendations are provided for the responsible and effective implementation of AI tools in elementary social studies classrooms. [ABSTRACT FROM AUTHOR]

Copyright of Central Asian Journal of Art Studies is the property of Central Asian Journal of Art Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Generative AI refers specifically to a class of Artificial Intelligence models that use existing data to create new content that reflects the underlying patterns of real-world data. This contribution presents a study that aims to show what the current perception of arts educators and students of arts education is with regard to generative Artificial Intelligence. It is a qualitative research study using focus groups as a data collection technique in order to obtain an overview of the participating subjects. The research design consists of two phases: (1) generation of illustrations from prompts by students, professionals and a generative AI tool; and (2) focus groups with students (N = 5) and educators (N = 5) of artistic education. In general, the perception of educators and students coincides in the usefulness of generative AI as a tool to support the generation of illustrations. However, they agree that the human factor cannot be replaced by generative AI. The results obtained allow us to conclude that generative AI can be used as a motivating educational strategy for arts education. [ABSTRACT FROM AUTHOR]

This study was dedicated to developing the Scale of Mathematics Teachers' Technology Integration (SMTTI), a comprehensive instrument designed to assess the competencies of primary mathematics teachers in integrating digital technologies. The research encompassed data collection through a web-based questionnaire distributed to mathematics teachers across Chongqing, China. Both exploratory and confirmatory factor analyses were conducted to determine the SMTTI's efficacy as a measurement tool. These analyses established the robust reliability and validity of the SMTTI, affirming its effectiveness as an instrument for assessing the technology integration capabilities of mathematics teachers. The scale exhibited excellent psychometric properties, characterized by strong factor loadings, high internal consistency, and good model fit indices. The SMTTI advances educational technology research by providing a nuanced self-efficacy assessment tool that aligns with the emergent needs of post-pandemic education environments. Its distinctiveness lies in its empirical foundation and tailored approach to evaluating primary mathematics teachers' self-efficacy for technology integration. The scale fills a critical gap in the existing literature by delivering a context-specific tool designed for the post-digital transition in educational practice, aiding in the formulation of targeted professional development programs and policy making. This addresses a need for updated instruments in the field, complementing the self-reported surveys focusing on practical application and current educational demands. The SMTTI contributes to refining the use of digital technology in educational settings by providing an instrument sensitive to the context-specific needs of teachers, thereby supporting more accurate instruments in this evolving domain. [ABSTRACT FROM AUTHOR]

Engineering studies can negatively impact the academic performance and mental health of those with physical disabilities. The impact of Artificial intelligence (AI) counselling technique on stress management of engineering students with physical disability was explored. There's a dearth of research relating to AI counselling technique and students with physical disabilities. The pretest posttest control group quasi-experimental research design was adopted. Six hypotheses guided the study. Participants were 98 physically challenged engineering students selected across ten institutions of higher learning in Nigeria. Experimental groups and the Control group were all pretested using self-designed Perceived Stress Level Scale (PSLS). The experimental group was treated with AI-enhanced counselling for 5 weeks while the control group was exposed to conventional counselling. Then subjected to PSLS. Percentage, mean, standard deviation and analysis of covariance (ANCOVA) were used. Findings were a significant main effect of AI counseling technique on stress management (F-ratio (1, 97) = 7.235), a significant main effect of level of study on stress management (F-ratio (2, 98) = 4.409). A significant interaction effect of level of student and AI counseling technique on the stress management (F-ratio (2, 96)=5.352) no significant interaction effect of gender, level of study and AI counseling technique was found (F-ratio (3, 95) =1.274). Incorporating AI counseling technique for managing stress was recommended. [ABSTRACT FROM AUTHOR]

The article explores the application of OpenAI's GPT-4 in providing feedback for English as a second or foreign language writing, utilizing Hubbard's framework for computer-assisted language learning. Topics discussed include the operational description of GPT-4, its suitability for teachers and learners, and the challenges and benefits associated with its implementation.

The article explores the use of ChatGPT in secondary school English language teaching, emphasizing the need for learners to develop both productive and critical skills when interacting with artificial intelligence chatbots. Topics discussed include implementing ChatGPT in the classroom, students' responses to its use, and strategies for fostering productive and critical engagement.

Copyright of Anatolian Journal of Educational Leadership & Instruction / Anadolu Eğitim Liderliği ve Öğretim Dergisi is the property of Anatolian Journal of Educational Leadership & Instruction and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research introduces an innovative intelligent model developed for predicting and analyzing sentiment responses regarding audio feedback from students with visual impairments in a virtual learning environment. Sentiment is divided into five types: high positive, positive, neutral, negative, and high negative. The model sources data from post-COVID-19 outbreak educational platforms (Microsoft Teams) and offers automated evaluation and visualization of audio feedback, which enhances students’ performances. It also offers better insight into the sentiment scenarios of elearning visually impaired students to educators. The sentiment responses from the assessment to point out deficiencies in computer literacy and forecast performance were pretty successful with the support vector machine (SVM) and artificial neural network (ANN) algorithms. The model performed well in predicting student performance using ANN algorithms on structured and unstructured data, especially by the 9th week against unstructured data only. In general, the research findings provide an inclusive policy implication that ought to be followed to provide education to students with a visual impairment and the role of technology in enhancing the learning experience for these students. [ABSTRACT FROM AUTHOR]