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English language education in Japan has long been criticized for its traditional methods emphasizing grammar and reading at the expense of communicative competence. This article explores the potential of Artificial Intelligence in Education (AIEd) to address this issue. A review of literature explored critical challenges faced by Japanese EFL learners, including Japanese teachers' low English proficiency and attitudes towards English teaching, heavy focus on entrance examinations in high school, overemphasis on grammar in EFL curricula and textbooks, lack of authentic communicative practice, and differences in cultural values. An examination of technology integration in Japanese education revealed that while many institutions have begun incorporating technology, its widespread adoption has been gradual. Several case studies support the use of AI to address the psychological barrier to speaking by offering a safe and engaging learning environment, thus boosting confidence and fluency. Furthermore, in the Japanese language context, AI can lower anxiety, promote creativity, and offer personalized learning. In addition to the individual benefits, AI empowers institutions to tailor learning needs, teachers to shift their role from instructors to facilitators, and students to become independent critical thinkers. Finally, challenges and limitations are addressed, including ethical considerations surrounding data privacy, overreliance, predictive patterns, and watermarking. Despite potential drawbacks, the benefits of AIEd merit a deeper exploration of its adoption in EFL curricula. AI tools offer a practical solution to prepare Japanese EFL students to communicate in English effectively and confidently and thus participate in the global landscape.

At a time when language trainers need to determine how best to integrate new digital tools that provide students with linguistic information, our aim in this article is to assess the potential value of information provided by two types of tools now widely used by students: machine translation via online translators and state-of-the-art generative AI platforms such as ChatGPT. In particular, the aim is to explore whether access to such information can help students develop their linguistic analysis skills to improve their understanding of the language prior to performing a translation task. Our case study deals with the analysis of complex English compounds--a known challenge for French learners of English as a second language--and involves two distinct cohorts: students in their third and final year of an undergraduate program in applied foreign languages, and students in their second and final year of a master's program in professional translation. We evaluated the participants' ability to identify head nouns--a necessary skill for comprehending complex noun phrases--under two conditions: (i) without access to any additional information and (ii) with access to machine translation outputs provided by a generic online translator. Subsequently, we explored the capabilities of advanced generative AI tools--in this instance ChatGPT--in correctly identifying head nouns. Our results show that students may benefit from the presence of machine translation outputs, albeit with varying degrees of success. However, our experimentation with ChatGPT suggests that generative AI tools may be more effective in helping students analyze and understand complex noun phrases in English, in particular since the launch of ChatGPT-4o.

The effectiveness of providing Written Corrective Feedback (WCF) in English as a Foreign Language (EFL) classrooms to enhance students' general writing skills has been well-established. With the advent of various feedback modes in modern classrooms, this pilot study aimed to explore EFL students' perceptions and preferences regarding the effectiveness of three distinct feedback modes: peer feedback, artificial intelligence (AI), and teacher feedback. The study also aimed to determine the distribution of feedback across specific writing components, while accounting for revisions made. Fifteen pairs of participants completed a short writing task and received WCF from peers, followed by an AI tool (ChatGPT), and finally from their teacher. Subsequent revisions to their writing were made after each feedback mode. Using a qualitative approach via survey analysis, findings indicate that participants perceived both ChatGPT and teacher feedback as effective, with peer feedback also being generally regarded as effective by most participants. Preferences lean towards teacher WCF and a combination of teacher and ChatGPT WCF. Additionally, the study identified feedback distribution across writing components, with implications discussed for refining EFL classroom feedback practices.

This paper explores the reliability of using ChatGPT in evaluating EFL writing by assessing its intra- and inter-rater reliability. Eighty-two compositions were randomly sampled from the Written English Corpus of Chinese Learners. These compositions were rated by three experienced raters with regard to 'language', 'content', and 'organization'. The writing samples were also rated by ChatGPT twice over some time, and the average scores were calculated. Independent samples t-test was conducted to compare the average scores given by ChatGPT and human raters. Pearson correlation analyses were conducted between the two sets of overall scores given by ChatGPT to calculate the intra-rater reliability, as well as between average scores given by ChatGPT and human raters for inter-rater reliability. The results of comparative analysis shows that ChatGPT may be used for evaluating EFL essays, as the scores are similar to those provided by reliable human raters. However, the result of correlation analyses shows that the intra-rater reliability of ChatGPT is not high enough to be acceptable, r=0.575, p<0.01 and the strength of the inter-rater reliability is moderate as well, r=0.508, p<0.01. Besides, there is no significant relationship between their average scores on 'organization' of the writings, r=0.181, p>0.05. Thus, it can be concluded that ChatGPT is not a reliable tool to rate and score EFL writings using the prompt in this study. One of the possible reasons for the unreliability of ChatGPT as a rater of EFL writing seems to be related to scoring for the 'organization' of the essay. These findings imply that while ChatGPT has potential as an evaluative tool, its current limitations, particularly in assessing organization, must be addressed before it can be reliably used in educational settings.

This study explores the perceptions of second language (L2) educators on the surge of Large Language Models (LLMs) like ChatGPT, and their potential impact on language education. We surveyed over 100 L2 instructors, asking questions about their ideas for AI-proofing assignments, their policies, and their perceptions of how this tool will impact the profession. Data was collected through an anonymous survey and analyzed with a mixed-method approach through a constructivist lens. The results show mixed feeling: ranging from excitement for the pedagogical potential of ChatGPT to fear for potential academic dishonesty and job security in some. The results analysis provides insight into L2 educators use and policies of ChatGPT. The quantitative data highlighted that instructors' levels of excitement or concern about the adoption of ChatGPT in language education correlate with their personal experience with the tool, in line with Bax's (2003) normalization framework. Those with prior interaction with ChatGPT showed more enthusiasm for its educational potential than those without. Interestingly, this study found no significant difference in attitudes across different age groups or years of teaching experience. The qualitative data show that L2 educators anticipate wide use of ChatGPT in their students' assignments, yet a large majority (90%) is confident in their ability to identify students use of Chat GPT and Google Translate in their work. This study also reviews the current use and policies regarding MT and ChatGPT, highlighting a significant number of instructors prohibiting their uses. The results also highlight different ways instructors have been AI proofing their assignments and how they are currently using ChatGPT in their professions. In conclusion, this study advocates for a proactive use of these tools emphasizing the importance of adopting innovative pedagogies, a student-centered classroom approach, and certain ethical considerations.

Via AI video generators, it is possible to create educational videos with humanistic instructors by simply providing a script. The characteristics of video types and features of instructors in videos impact video engagement and, consequently, performance. This study aimed to compare the impact of human instructors and AI-generated instructors in video lectures on video engagement and academic performance. Additionally, the study aimed to examine students' opinions on both types of videos. Convergent-parallel approach mixed method was used in this study. A total of 108 undergraduate students participated: 48 in the experimental group, 52 in the control group, and eight in the focus group. While the experimental group (AI-generated instructor) and control group (human instructor) watched 10 minutes of two videos each in two weeks, the students in the focus group watched both types of videos with human and AI-generated instructors. Data were collected through the Video Engagement Scale (VES) after the experimental process, and the Academic Performance Test as a pretest and posttest was administered in both groups. The findings of the experimental part revealed that learners' video engagement was higher in the course with the human instructor compared to the course with the AI-generated instructor. However, the instructor type did not have a significant effect on academic performance. The results based on the qualitative part showed that students thought the AI-generated instructor caused distraction, discomfort, and disconnectedness. However, when the video lesson topic was interesting or when students focused on the video with the intention of learning, these feelings could be ignored. In conclusion, even in today's conditions, there is no difference in performance between human and AI-generated instructors. As AI technology continues to develop, the difference in engagement is expected to disappear, and AI-generated instructors could be used effectively in video lectures.

Learners might have several challenges while attempting to learn a second/foreign language. Learners of Chinese face linguistic, psychological, and educational challenges. The integration of technology, especially artificial intelligence (AI), into teaching foreign languages is a blessing for teachers and learners. This study delved into the auxiliary role of AI-powered applications in mitigating the linguistic, psychological, and educational challenges which non-Chinese learners face while learning Chinese/Mandarin language. Qualitative research was employed, and 20 teachers of Chinese language were selected through theoretical sampling. In-depth interviews were used for collecting data, and MAXQDA was used for thematic analysis. Findings revealed that AI-powered educational applications are useful for helping language learners overcome the commonly reported linguistic, psychological, and educational challenges which non-Chinese learners and teachers of Mandarin might encounter. Findings verify the effectiveness of AI-powered applications, such as ChatGPT, Poe, Brainly, and so forth, in helping teachers and learners of Chinese language learn grammar, structure, idioms, and cultural issues of Chinese language. Findings have implications for foreign language (Chinese) learners and teachers, educational technologists, as well as syllabus designers.

Artificial intelligence (AI) revolutionizes education by fundamentally altering the methods of teaching and processes of learning. Given such circumstances, it is essential to take into account the mental and psychological well-being of teachers as the architects of education. This research investigated the links between teacher immunity (TI), work passion (WP), job satisfaction (JS), occupational well-being (OW-B) and psychological well-being (PW-B) in the context of AI-assisted language learning. In order to achieve this objective, 392 Iranian teachers of English as a foreign language (EFL) were given the Language Teacher Immunity Instrument, the Work Passion Scale, the Job Satisfaction Questionnaire, the Occupational Well-Being Scale, and the Psychological Well-Being at Work Scale. By using confirmatory factor analysis and structural equation modeling, the study identified and quantified the impacts of TI, WP, JS, OW-B, and PW-B via data screening. The findings emphasize the crucial role that TI and WP play in providing a balance in teachers' JS, OW-B, and PW-B while applying AI in their language instruction. The broad ramifications of this research are explored.

Artificial intelligence (AI) has contributed to various facets of human lives for decades. Teachers and students must have competency in AI and AI-empowered applications, particularly when using online electronic platforms such as learning management systems (LMS). This study investigates the structural relationship between AI literacy, academic well-being, and educational attainment of Iranian undergraduate students. Using a convenience sampling approach, we selected 400 undergraduate students from virtual universities equipped with LMS platforms and facilities. We collected data using three instruments--an AI literacy scale, an academic well-being scale, and educational attainment scale--and analyzed the data using Smart-PLS3 software. Results showed that the hypothetical model had acceptable psychometrics (divergent and convergent validity, internal consistency, and composite reliability). Results also showed that the general model had goodness of fit. The study thus confirms the direct effect of AI on academic well-being and educational attainment. By measuring variables of academic well-being, we also show that AI literacy in China and Iran significantly affects educational attainment. These findings have implications for students, teachers, and educational administrators of universities and higher education institutes, providing knowledge about the educational uses of AI applications.

New digital technologies such as social robots are embodied computers with human-like features and conversational capabilities that can socially interact with people. Social robots have been used in education as a learning tool to support second language learning. This essay discusses current research literature that has explored how social robots could be utilized to support second language (L2) international students studying at English-speaking universities. Insights into the potential application and limitations of using social robots to support L2 students outside their home countries to promote their social and academic well-being will also be discussed. Based on a synthesis of current and relevant research gathered from the literature, the affordances of using social robots for L2 students included learning L2 through human-robot interactions, enhanced motivation, and engagement in their learning environments. However, the limitations of the use of this technology included attitudes towards robot-assisted learning (e.g., unfamiliarity with learning with a social robot) and the novelty effect of social robots. Further research is needed to deepen our understanding of not only the role of social robots for supporting language learning, but also how they could aid L2 students in their successful transition to a foreign university, culture, and social context.

This research study explored the perspectives of 20 educational technologists from four Saudi Arabian universities regarding the integration of AI-powered technology, particularly ChatGPT, into online higher education. The study used a qualitative research method that relied on the principles of theoretical sampling to select participants and conducted in-depth interviews to collect their insights. The approach taken for data analysis was thematic analysis, which uncovered a rich range of insights on both the challenges and opportunities associated with students' use of AI-integrated technology in the context of online higher education. Ten significant challenges emerged that shed light on the complexities and intricacies of integrating AI-powered technology into educational environments. These challenges included issues related to technological infrastructure, pedagogical adaptation, and the need for comprehensive training programs to empower both teachers and learners. Additionally, eight threats were examined that highlighted concerns about data security, privacy, and potential risks associated with AI technology in educational institutions. This study not only provided a comprehensive overview of the current landscape of AI-integrated technology in Saudi Arabian higher education, but also provided valuable insights for education stakeholders, technologists, and policy makers. It underscored the necessity of proactive measures to mitigate challenges and threats while harnessing the opportunities presented by AI technology to enhance the quality and effectiveness of online higher education.

This study examines the effects of corrective feedback (CF) on language learners' writing anxiety, writing complexity, fluency, and accuracy, and compares the effectiveness of feedback from human teachers with an AI-driven application called Poe. The study included three intact classes, each with 25 language learners. Using a quasi-experimental design with pretest and posttest measures, one class received feedback from the teacher, one from the Poe application, and the third received no response to their writing. Data were generated though tests and a writing anxiety scale developed for the study. Data analysis, conducted using one-way ANOVA tests, revealed significant effects of teacher and AI-generated feedback on learners' writing anxiety, accuracy, and fluency. Interestingly, the group that received AI-generated feedback performed better than the group that received teacher feedback or no AI support. Additionally, learners in the AIgenerated feedback group experienced a more significant reduction in writing anxiety than their peers. These results highlight the remarkable impact of AI-generated CF on improving writing outcomes and alleviating anxiety in undergraduate language learners at East China University of Political Science and Law . The study demonstrates the benefits of integrating AI applications into language learning contexts, particularly by promoting a supportive environment for students to develop writing skills. Educators, researchers, and developers can use these findings to inform pedagogical practices and technological interventions to optimize the language learning experience in primary school settings. This research highlights the effectiveness of AI-driven applications in language teaching. It highlights the importance of considering learners' psychological well-being, particularly anxiety levels, when developing effective language learning interventions.

Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their intention to use AI in distributed EFL learning contexts. This mixed-methods study, based on the technology acceptance model (TAM), examined the determinants of behavioral intention to use AI among 464 Chinese EFL college learners. As to quantitative data, a structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI. Second, attitude toward AI significantly and positively predicts behavioral intention to use AI. However, contrary to the TAM assumptions, perceived usefulness does not significantly predict either attitude toward AI or behavioral intention to use AI. Third, mediation analyses suggest that perceived ease of use has a significant and positive impact on students' behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness. As to qualitative data, semi-structured interviews with 15 learners, analyzed by the software MAXQDA 2022, provide a nuanced understanding of the statistical patterns. This study also discusses the theoretical and pedagogical implications and suggests directions for future research.

This experimental study investigates the impact of ChatGPT-simplified authentic texts on university students' reading comprehension, inferencing, and reading anxiety levels. A within-subjects design was employed, and 105 undergraduate English as a foreign language (EFL) students engaged in both original and ChatGPT-simplified text readings, serving as their own controls. The findings reveal a significant improvement in reading comprehension scores and inferencing scores following ChatGPT intervention. However, no significant change in reading anxiety levels was observed. Results suggest that ChatGPT simplification positively influences reading comprehension and inferencing, but its impact on reading anxiety remains inconclusive. This research contributes to literature on the use of artificial intelligence (AI) in education and sheds light on ChatGPT's potential to influence language learning experiences within higher education contexts. The study highlights the practical application of ChatGPT as a tool for helping students engage in authentic text readings by making text more comprehensible. Based on the findings, several multifaceted implications that extend to various stakeholders in the field of language education are provided.

Artificial Intelligence (AI), as an innovation in technology, has greatly affected human life. AI applications such as ChatGPT have been used in different fields, particularly education. However, the use of AI applications to enhance undergraduate students' academic emotions and test anxiety has not been appropriately investigated. This study addresses the effects of undergraduate students' test anxiety and academic emotions. A total of 160 undergraduate students majoring in different fields of study were selected through convenience sampling and divided into control and experimental groups. Both groups received test anxiety and academic emotions scales at the onset of the treatment. The students assigned to the experimental group were trained to use ChatGPT and monitored for learning and doing their assignments outside the classroom during the semester. The two groups received the scales at the end of the semester, which lasted 16 weeks. Independent samples t-tests were used for analyzing the data. Results revealed that using AI-empowered applications significantly reduced the students' test anxiety and negative academic emotions but enhanced their positive academic emotions. Students can use ChatGPT as an auxiliary instrument to overcome their negative emotions and enhance their educational attainment. Findings affect teachers, educational technologists, educational psychologists, and students.

No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA protocol was used, and the Web of Science Core Collection (2019-2023) was searched. VOSviewer was used for networking and clustering, and in-depth analysis was employed to answer the research questions. Among the main results, it is worth noting that the scientific literature has focused on the following areas: (a) predicting student dropout, (b) automatic grading of short answers, and (c) recommending MOOC courses. It was concluded that pedagogical challenges have included the effective personalization of content for different learning styles and the need to address possible inherent biases in the datasets (e.g., socio-demographics, traces, competencies, learning objectives) used for training. Regarding deep learning, we observed an increase in the use of pre-trained models, the development of more efficient architectures, and the growing use of interpretability techniques. Technological challenges related to the use of large datasets, intensive computation, interpretability, knowledge transfer, ethics and bias, security, and cost of implementation were also evident.

Several barriers hinder students from producing clear and impactful written work. Writing assignments are often given on an individual basis, similar to homework, and without any assistance. Students in a classroom context have access to both their classmates and the teacher while they are working in groups or pairs as part of their assignments. The majority of students, however, are clueless about how to begin their homework assignments. The introduction of artificial intelligence in education may help solve this problem. The current research intended to demonstrate the effects of employing automated writing evaluation (AWE) in fostering learners' writing skills, motivation to write, enjoyment of writing, and academic buoyancy in open and distributed English as a foreign language (EFL) learning. The participants were 86 intermediate EFL students from China. The participants in the experimental group (n = 44) received instruction and feedback from their teachers only; participants in the control group (n = 42) were exposed to their teachers' instruction as well as AWE. The results of data analysis via one-way multivariate analysis of variance indicated that the participants in the experimental group outperformed their peers in the control group in motivation to write, enjoyment in writing, academic buoyancy, and academic success in writing. Further in-depth discussions proceed regarding the implications of the study.

The consideration of students' emotional and psychological health is crucial to facilitate effective teaching and grading practices. This study set out to shed light on the interplay between self-esteem (S-E), cognitive-emotion regulation (CER), academic enjoyment (AE), and language success (LS) in artificial intelligence (AI)-supported online language learning. To this end, the foreign language learning self-esteem scale, the Cognitive Emotion Control Questionnaire, the foreign language enjoyment scale, and a researcher-made test were distributed to 389 English as a foreign language learners in China. Screening the data with confirmatory factor analysis and structural equation modeling, the effects of S-E, CER, AE, and LS were identified and quantified. These results highlighted the important function that online courses assisted by AI perform in enhancing students' CER and AE. This implied that students who have cultivated a robust sense of self-efficacy are adept at effectively regulating their cognitive and affective processes in AI-supported language learning. Possible improvements in language education are discussed, as are the study's broader implications.

In education, the integration of artificial intelligence (AI) has presented opportunities to transform the dynamics of online learning. This study investigated the impact of an AI-powered application, namely ChatGPT, on the autonomy of Saudi Arabian primary students participating in online classes. It also explored how the implementation of Chat GPT influenced Saudi Arabian primary students' autonomy. In this mixed-methods study, a quasi-experimental design assessed the impact of ChatGPT on learner autonomy among 250 Saudi Arabian primary students from six primary schools in Riyadh, Saudi Arabia. The quantitative analysis employed descriptive statistics and t-tests, while the qualitative data underwent interpretative phenomenological analysis. To ensure coding reliability, 20% of the codes were independently reviewed by an external coder, with a 94% inter-coder agreement coefficient reached through consensus. Findings revealed that ChatGPT significantly affected the participants' perceptions of autonomy and its different dimensions. Qualitative data showed that AI-powered applications contributed to the students' autonomy in 10 different ways. Participants also mentioned that AI-powered apps might have some negative consequences. This study has theoretical implications for redefining learner autonomy in the digital age and calls for the exploration of many facets of autonomy. Practical applications from this study include strategic integration of AI into online education, data security, and the need for orientation programs.

The rapid advancement of artificial intelligence (AI) in education underscores transformative prospects for open and distributed learning, encompassing distance, hybrid, and blended learning environments. This qualitative study, grounded in narrative inquiry, investigates the experiences and perceptions of 141 preservice teachers engaged with AI, mainly through ChatGPT, over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies. Employing Strauss and Corbin's methodological approach of open, axial, and selective coding to analyze reflective narratives, the study unveils significant themes that underscore the dual nature of AI in education. Key findings reveal ChatGPT's role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage. Specifically, ChatGPT was found to empower personalized learning and streamline procedures, yet challenges involving information accuracy and data security remained. The study significantly contributes to teacher education discourse by revealing AI's complex educational impacts, highlighting an urgent need for comprehensive ethical AI literacy in teacher training curricula. However, critical ethical considerations and practical challenges involving academic integrity, information accuracy, and balanced AI use are also brought to light. The research also spotlights the need for responsible AI implementation in open and distributed learning to optimize educational outcomes while addressing potential risks. The study's insights advocate for future-focused AI literacy frameworks that integrate technological adeptness with ethical considerations, preparing teacher candidates for an intelligent digital educational landscape.

The transformative impact of advancements in educational technology, particularly those powered by artificial intelligence (AI), on the landscape of education and the teaching profession has been substantial. This study explores the repercussions of AI-powered technologies on teachers' autonomous behavior, digital burnout, and professional development. The study involved a cohort of 320 high school teachers in China segregated into control and experimental groups. The experimental group received instructions on AI-integrated applications and how they might be used in education. However, the teachers assigned to the control group did not receive information on the use of AI educational applications. Three distinct questionnaires probing autonomous behaviors, digital burnout, and online professional development were administered, and the ensuing data were analyzed using independent sample t-tests. The findings elucidate a discernible positive impact of AI-integrated technology intervention on teachers' professional development and autonomous behaviors. The incorporation of AI-enhanced tools facilitated an augmentation in teachers' professional growth and bolstered their independent and self-directed instructional practices. Notably, using AI-integrated technology significantly reduced teachers' susceptibility to digital burnout, signifying a potential alleviation of stressors associated with technology- mediated teaching. This research provides valuable insights into the multifaceted effects of AI-powered technologies on educators, shedding light on enhancing professional competencies and mitigating digital burnout. The implications extend beyond the confines of this study, resonating with the broader discourse on leveraging technology to augment the teaching profession and optimize the learning environment.

This study proposes a hypothetical model combining the unified theory of acceptance and use of technology (UTAUT) with self-determination theory (SDT) to explore design professionals' behavioral intentions to use artificial intelligence (AI) tools. Moreover, it incorporates job replacement (JR) as a moderating role. Chinese-speaking design professionals in regions influenced by Confucian culture were surveyed. An analysis of 565 valid cases with AMOS (Analysis of Moment Structures) supported the structural model hypothesis. The model explains 52.1% of the variance in behavioral intention to use (BIU), proving its effectiveness in explaining these variances. The results further validate the importance of performance expectancy (PE) over effort expectancy (EE) in influencing BIU. Additionally, it has been shown that the impact on intrinsic motivation (IM) and extrinsic motivation (EM) can be either amplified or diminished by anxiety about JR. For individuals experiencing higher levels of JR anxiety, there is a marked increase in IM. They may perceive adopting AI tools as an opportunity to enhance their skills and job security. Conversely, this anxiety also significantly boosts EM, as the potential for improved efficiency and productivity with AI use becomes a compelling incentive. These findings suggest new paths for academic researchers to explore the psychological impacts of AI on design professionals' roles. For practitioners, especially in human resources and organizational development, understanding these dynamics can guide the creation of training programs that address job replacement anxiety.

This study, which is quasi-experimental in nature, looks into how language learners' willingness to communicate and engagement in English as a foreign language (EFL) classrooms are affected by Duolingo. The control and experimental groups comprised two complete classes with forty EFL students. To compare learner engagement and communication willingness scores before and after treatment, the study used independent samples t-tests. The results demonstrated the groups' initial homogeneity by showing no discernible differences prior to the intervention. The results confirmed the effects on learner engagement, which showed significant gains in affective, cognitive, and behavioral domains, indicating Duolingo's beneficial impact on engagement in general. Furthermore, the significant effect sizes observed confirmed Duolingo's contribution to improved language attitudes, engagement, and communicative confidence. Compared to the control group, the experimental group's willingness to speak, read, write, comprehend, and communicate generally improved in a manner that was statistically significant. The significant effect sizes demonstrate how well Duolingo works to improve different aspects of willingness to share. The study emphasizes the pedagogical tool's adaptability and encourages teachers to integrate Duolingo for a comprehensive and technologically enhanced language learning experience. Practical implications arise for EFL teachers who use online learning resources.

The release of ChatGPT has marked the dawn of a new information revolution that will transform how people communicate and make meaning. However, to date, little is known about the implications of ChatGPT for L2 composition instruction. To address this gap, the present study uses a systematic review design to synthesize available research on the educational potentials of ChatGPT as an instructional assistant, outline the implications of these potentials for L2 writing instruction, and discuss their practical applications. The findings, based on a meta-analysis of 42 research articles, demonstrate that ChatGPT can enhance L2 writing instruction by boosting learners' motivation, automating instructional tasks, and offering instantaneous, personalized feedback to learners. These findings have important implications for harnessing the instructional potential of generative AI in L2 writing classes.

Artificial intelligence (AI) has undergone considerable advancement in the contemporary period and represents an emerging technology in higher education. Cultural contexts significantly shape individuals' perceptions, attitudes, and behaviors, particularly in the realm of technology acceptance. By adopting a cross-cultural lens, this research explores the potential variations across Chinese and international students from diverse countries in terms of attitudes and their behavioral intentions toward AI use. With a technology acceptance model (TAM) framework, the research used a survey approach, employing questionnaires as the primary means of data collection. The data were then analyzed through structural equation modeling and descriptive statistics. A substantial discrepancy was found in the prevalence, attitudes, and behavioral intentions toward AI use between Chinese and international students. Findings further revealed a stronger effect of perceived ease of use on both attitudes and behavioral intentions among international students compared with their Chinese counterparts. Findings suggest that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology. The study emphasizes the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces. These insights contribute to the evolving discourse on technology acceptance in higher education and offer practical implications for educators and institutions toward optimizing AI integration in pedagogical practices.

This study aimed to present a comprehensive bibliometric analysis of 1,726 academic studies from among those indexed by the Web of Science database platform between 2013 and 2023, to provide a general framework for the concept of artificial intelligence in education (AIEd). Trends in publications and citations across countries, institutions, academic journals, and authors were identified, as well as collaborations among these elements. Several bibliometric analysis techniques were applied, and for each analysis, the motivations behind the execution and method of producing findings were documented. Our findings showed that the number of studies on the concept of AIEd has increased significantly over time, with the U.S. and China being the most common countries of origin. Institutions in the U.S. stand out from those around the world. Pioneering journals in education have also emerged as prominent in the field of AIEd. On the other hand, collaboration between authors has been limited. The study was supplemented with keyword analysis to reveal thematic AIEd concepts and to reflect changing trends. For those exploring artificial intelligence in education, our insights on popular topics offer valuable guidance toward greater understanding of the latest advancements and key research areas.

Across many language backgrounds, a consistent hurdle to accessing United States higher education is understanding the basic information necessary to apply for admission and financial aid and complete the many enrollment management processes necessary to begin one's college career (apply for housing, receive and submit vaccinations, register for classes, etc.). However, to date, no studies have explored how this type of higher education information can be simplified and translated into Arabic, one of the most widely spoken languages in the world and a linguistic background shared by tens of thousands of prospective international students (and their families) seeking higher education in the United States. This case study reports on research-to-practice work conducted with the University of Iowa, specifically how the university simplified their enrollment management information and how that information was translated into Arabic for native Arabic speakers seeking access to the University of Iowa. Findings reveal that the institution simplified text to speak more directly to prospective student audiences by using second person pronouns and simpler sentence structure and diction to engage this audience. Moreover, analyses of machine and human translations of English to Arabic suggest that human translation should be the preferred mechanism of translating higher education information, as Google Translate and ChatGPT provided adequate but not perfect translations of Iowa's information. Implications for practice and college access are addressed.

Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the practical applicability and performance of these automated models in authentic settings remains largely an under-researched area. In such settings, the quality of data features or attributes is often affected by noise, which is referred to as attribute noise. This paper undertakes a systematic exploration of the impact of attribute noise on the performance of different collaboration-quality estimation models. Moreover, we also perform a comparative analysis of different ML algorithms in terms of their capability of dealing with attribute noise. We employ four ML algorithms that have often been used for collaboration-quality estimation tasks due to their high performance: random forest, naive Bayes, decision tree, and AdaBoost. Our results show that random forest and decision tree outperformed other algorithms for collaboration-quality estimation tasks in the presence of attribute noise. The study contributes to the MMLA (and learning analytics (LA) in general) and CSCL fields by illustrating how attribute noise impacts collaboration-quality model performance and which ML algorithms seem to be more robust to noise and thus more likely to perform well in authentic settings. Our research outcomes offer guidance to fellow researchers and developers of (MM)LA systems employing AI techniques with multimodal data to model collaboration-related constructs in authentic classroom settings.

Large-scale assessments play a key role in education: educators and stakeholders need to know what students know and can do, so that they can be prepared for education policies and interventions in teaching and learning. However, a score from the assessment may not be enough--educators need to know why students got low scores, how students engaged with the tasks and the assessment, and how students with different levels of skills worked through the assessment. Process data, combined with response data, reflect students' test-taking processes and can provide educators such rich information, but manually labelling the complex data is hard to scale for large-scale assessments. From scratch, we leveraged machine learning techniques (including supervised, unsupervised, and active learning) and experimented with a general human-centred AI approach to help subject matter experts efficiently and effectively make sense of big data (including students' interaction sequences with the digital assessment platform, such as response, timing, and tool use sequences) to provide process profiles, that is, a holistic view of students' entire test-taking processes on the assessment, so that performance can be viewed in context. Process profiles may help identify different sources for low performance and help generate rich feedback to educators and policy makers. The released National Assessment of Educational Progress (NAEP) Grade 8 mathematics data were used to illustrate our proposed approach.

Integrating Artificial Intelligence [AI] into mathematics education offers promising advancements and potential pitfalls. Striking a balance between AI-driven developments and preserving core pedagogical principles is critical in the teaching and learning environment. AI has emerged as a transformative force in various fields, including education. In the realm of mathematics education, AI technologies offer a spectrum of potential benefits (including personalize instruction, adaptive assessment, interactive learning environments, and real-time feedback, among others) and challenges (such as lack of creativity and problem-solving skills, inability to explain reasoning, bias in data and algorithms, absence of emotional intelligence and data privacy and security concern etc.). This conceptual study used autoethnography as the methodology and qualitative content approach to analyze data. The study discussed historical background of AI and considered ethical issues around AI. It was concluded that the journey to harness the full potential of AI in mathematics education requires careful navigation of the good, the bad, and the ugly aspects inherent in this technological evolution.

The research on artificial intelligence in mathematics education has attracted much attention from researchers since the last decade. This study aims to provide holistic information about artificial intelligence in mathematics education research using bibliometric analysis. Data for the analysis were extracted from the Scopus database from 1986-2023. The analysis, conducted using R-packages (Bibliometrix) and VOSviewer software, identifies the relevant nations, affiliations, journals, articles, and keywords on artificial intelligence in mathematics education research. The study reveals that 565 documents have been published in 354 journals, with an average annual growth rate of 11.27%. These documents, on average, have received 14.61 citations each. The research field has engaged a total of 1,847 authors, with an average of 3.26 authors contributing to each document. Additionally, 17.17% of these publications involved international co-authorship, indicating a moderate level of global collaboration. Our findings reveal a growing interest in using artificial intelligence as an educational tools and methods, particularly in the United States and China, which lead in publication output and citations. The analysis also reveals emerging trends and research gaps. The keywords such as "virtual reality," "sustainable development," and "COVID-19" reflect recent research focus on artificial intelligence in mathematics education research. Meanwhile, the keywords such as "mathematical literacy," "assessment," and "gamification" identified as underexplored areas, suggesting potential opportunities for future research on artificial intelligence in mathematics education research.

Generative Artificial Intelligence (AI-Gen) in education is a challenge for educational administrations due to the benefits and risks of its use. This research seeks to analyse the scientific production of AI-Gen in the educational field. The method used was a bibliometric analysis of descriptive and quantitative documents from the Web of Science database. The VOSviewer statistical programme was used to identify the keyword clusters and to create the network map. The results of this study ascertain that 2023 is the birth of AI-Gen research in education and has since produced a total of 178 papers, where Australia heads the ranking of countries with studies on this subject matter. The ChatGPT phenomenon materialises as a focus of study in the majority of cited research. The study includes seven keyword clusters, where the IA-Gen cluster which is related to terms such as challenges, risks and opportunities are highlighted. Following the discussion, the main conclusion of the study denotes that further research is required on the use of AI-Gen during the teaching-learning process in several educational stages to prevent plagiarism and to obtain a comprehensive understanding of this technology as an educational resource.

The options for Artificial intelligence (AI) tools used in teacher education are increasing daily, but more is only sometimes better for teachers working in already complex classroom settings. This team discusses the increase of AI in schools and provides an example from administrators, teacher educators, and computer scientists of an AI virtual agent and the research to support student learning and teachers in classroom settings. The authors discuss the creation and potential of virtual characters in elementary classrooms, combined with biometrics and facial emotional recognition, which in this study has impacted student learning and offered support to the teacher. The researchers share the development of the AI agent, the lessons learned, the integration of biometrics and facial tracking, and how teachers use this emerging form of AI both in classroom-based center activities and to support students' emotional regulation. The authors conclude by describing the application of this type of support in teacher preparation programs and a vision of the future of using AI agents in instruction.

The use of artificial intelligence (AI) is not a new concept. Still, the press, the worry, and the hype around the potential benefits and limitations of the explosion of these tools in this field is a current topic in teacher education. In this article, the authors summarize the past use of AI, present easily adaptable tools in teacher education, and discuss what is on the horizon in industry and special education teacher education. The authors highlight tools that should be considered in programs today, followed by ways to expand the field of AI in teacher education to support the learning outcomes of struggling students.

This article presents engaging and practical methods of helping educators to "remix" the evidence-based and high-leverage practices they are already familiar with to include the new capabilities of artificial intelligence (AI). Transformational modern technologies can be powerful and disruptive, possessing the potential to impact multiple areas of society, including education. One of the best ways for educators to implement AI in their teaching is using it to help support and extend their current practices (Mishra et al., 2023). Similar to how a new remix on the radio can make an old favorite song fresh again, educators can use AI to uphold and enhance their existing instructional strategies and skills. However, adapting to the new paradigm of AI in education may be challenging for teacher preparation programs in special education. The authors of this article apply some of the strategies from "Leveraging Emerging Technology to Design an Inclusive Future with Universal Design for Learning" (McMahon & Walker, 2019) to effectively implement AI in education. Rather than needing to start fresh or relearn how to teach while incorporating AI, teachers can view this article as a foundation for how to apply AI tools to support current practice. The recommendations are based on Universal Design for Learning (UDL) and strategies for adapting AI tools to support high-leverage practices and established evidence-based practices. The authors aim to inspire special educators to start using AI to help "remix" and innovate the implementation of their existing instructional strategies.

Exploring the role of artificial intelligence (AI) in education is important as it challenges traditional teaching practices and shapes how educators may approach them in the future. In this article, we document the transformative integration of AI in special education teacher preparation, highlighting how we, as early adopter professors, attempted to navigate this journey, offering practical applications for AI use. Practical applications of generative AI tools include aligning course objectives, developing modules, and creating assignments and assessment measures. Additionally, we describe innovative uses for AI, such as incorporating chatbots in teacher preparation courses, navigating curriculum development, generating case studies, and aligning individualized education program (IEP) goals with curricular standards. We also explore how AI can be employed as a reflective coaching tool for teaching practice. Ethical considerations are emphasized, focusing on transparent communication about AI use and documenting the learning process to humanize assessment experiences and mitigate potential risks.

Integrating Artificial Intelligence-Powered Personalized Learning (AI-PPL) in special education teacher preparation represents a shift toward tailoring educational experiences to meet the unique needs of preservice teachers and students with disabilities. This article explores the implementation of AI-PPL tools in teacher preparation programs, highlighting their potential to customize learning experiences, provide adaptive feedback, and enhance engagement through interactive content. This review of current AI-PPL functionalities, such as adaptive learning environments and customized feedback mechanisms, demonstrates how AI-PPL can impact teaching practices and student learning outcomes. The article introduces critical attributes for successful AI-PPL integration, such as ensuring accessibility and inclusivity. It calls for further professional development to enhance educator competency and skills. By presenting real-world examples and guiding questions for special education faculty, the authors offer practical insights for educators and faculty members to effectively navigate the complexities of adopting AI technologies in teacher preparation programs.

Artificial intelligence (AI) has transformative potential to support the education of students with moderate to severe intellectual disabilities (M/SID) and their teachers. Although research and evidence-based practices (EBPs) are integral to fostering positive student learning outcomes, educators face challenges in effectively implementing these strategies. In this article, we discuss how higher education faculty can prepare educators to harness the use of AI as a powerful tool to support the implementation of EBPs in the classroom, addressing teacher fluency and maintenance of application.

Artificial intelligence (AI) text generators, such as ChatGPT and Copilot, have been used for various purposes such as creating written content, writing or debugging computer code, answering questions, providing information, and improving written communication. This robust functionality of AI text generators along with the high rate of use reported by college students highlights the need for instructors in special education teacher preparation programs to contend with the impact of AI on teaching and learning. The purpose of this article is to provide examples of how teacher educators might reimagine assignments and activities in the era of AI text generators. Specifically, the authors explore ways that AI applications might be intentionally incorporated or, conversely, intentionally restricted in the design of course assignments and activities.

With the introduction of artificial intelligence (AI), specifically generative AI and large language models (e.g., ChatGPT, Google Gemini), into education, there is a conversation regarding what knowledge teachers still need and will need moving forward. In this article, we describe how AI can, and should, be aligned to the Technological Knowledge, Pedagogical Knowledge, and Content Knowledge (TPACK) framework, as a part of special education teacher preparation. Additionally, we explore the implications of AI on the TPACK framework, specifically how AI can be integrated within each of the three components, specific tools that support each aspect, and guiding questions that teacher-educators and pre-service teachers should be using when considering AI. We will provide teacher-educators with example activities they can use with their pre-service teachers to introduce AI and integrate its use within their curriculum, framed within the TPACK.

This study aims to generalize the reliability of the GAAIS, which is known to perform valid and reliable measurements, is frequently used in the literature, aims to measure one of today's popular topics, and is one of the first examples developed in the field. Within the meta-analytic reliability generalization study, moderator analyses were also conducted on some categorical and continuous variables. Cronbach's [alpha] values for the overall scale and the positive and negative subscales, and McDonald's [omega] coefficients for positive and negative subscales were generalized. Google Scholar, WOS, Taylor & Francis, Science Direct, and EBSCO databases were searched to obtain primary studies. As a result of the screening, 132 studies were found, and these studies were reviewed according to the inclusion criteria. Reliability coefficients obtained from 19 studies that met the criteria were included in the meta-analysis. While meta-analytic reliability generalization was performed according to the random effects model, moderator analyses were performed according to the mixed effect model based on both categorical variables and continuous variables. As a result of the research pooled, Cronbach's [alpha] was 0.881, 0.828, and 0.863 for total, the negative, and positive subscales respectively. Also, McDonald's [omega] was 0.873 and 0.923 for negative and positive subscales respectively. It was found that there were no significant differences between the reliability coefficients for all categorical variables. On the other hand, all continuous moderator variables (mean age, standard deviation age, and rate of female) had a significant effect.

ChatGPT has surged interest to cause people to look for its use in different tasks. However, before allowing it to replace humans, its capabilities should be investigated. As ChatGPT has potential for use in testing and assessment, this study aims to investigate the questions generated by ChatGPT by comparing them to those written by a course instructor. To investigate this issue, this study involved 36 junior students who took a practice test including 20 multiple-choice items generated by ChatGPT and 20 others by the course instructor, resulting in a 40-item test. Results indicate that there was an acceptable degree of consistency between the ChatGPT and the course instructor. Post-hoc analyses point to consistency between the instructor and the chatbot in item difficulty, yet the chatbot's results were weaker in item discrimination power and distractor analysis. This indicates that ChatGPT can potentially generate multiple-choice exams similar to those of the course instructor.

In the context of education, the issues of integrating artificial intelligence (AI) into teaching and maintaining academic integrity in students' use of AI are particularly relevant. This paper empirically examined the issue of ChatGPT usage for writing homework from the perspectives of students and professors. Study research methods included both quantitative and qualitative approaches. In Study 1, an anonymous questionnaire was administered to 350 Croatian students, users of ChatGPT, to investigate their perceptions, attitudes, habits, and intentions regarding ChatGPT usage for homework assignments. In Study 2, twelve faculty members were tested on their accuracy of distinguishing between original students' papers and ChatGPT-generated papers. For this purpose, 25 different versions of papers for 8 different courses were prepared. The results of the students' survey showed that most students still do not use ChatGPT regularly and have neutral attitudes about its usefulness, ease of use, risks, and intentions for future use. In addition, they were moderately concerned about ethical issues around its usage. Differences across gender and field of study were found. Professors, on the other hand, reported having average self-efficacy in appraising authorship, which is in line with their low average accuracy of 53%. Accuracy in distinguishing was lowest when ChatGPT was instructed to write a paper as a student. These results strongly suggest the necessity for clear guidelines, plagiarism detection tools, and educational initiatives to promote ethical use of AI technology.

This study aimed to investigate the impact of AI-powered Speech Recognition Technology (AISRT) in improving English pronunciation and speaking skills among EFL learners. Additionally, it explored the opinions and responses of EFL learners towards the use of this technology for pronunciation and speaking skill enhancement. The research employed a pretest/post-test design and a survey questionnaire with a multiple-choice rating scale and openended questions. The study included a sample of EFL learners who received instruction and practice using an AI-SRT program over a designated period. Data were collected through pretest and post-test assessments to measure changes in pronunciation and speaking skills. A survey questionnaire was administered to gather participants' opinions and responses regarding the program's effectiveness and their overall experience. The data were analyzed using descriptive statistics to examine the central tendency, variability and distribution of pre-test and post-test scores. Comparative analysis was conducted using paired t-tests to determine the significance of the improvement in pronunciation and speaking skills. Qualitative data from the survey questionnaire were analyzed thematically to identify recurring themes and patterns in participants' opinions. The findings of this study contribute to the understanding of the impact of AI-powered speech recognition technology on EFL learners' pronunciation and speaking skills. The results reveal the effectiveness of such technology and provide valuable insights into learners' perceptions and experiences. This research has implications for language educators, curriculum designers, and developers of educational technology in designing effective pronunciation and speaking skill enhancement tools for EFL learners.

Interpreting and incorporating machine learning technology from a human perspective helps define the role of product designers in the era of artificial intelligence. With this background, this study developed a 7-week design course about machine learning-based product design. Subsequently, in Fall 2023, a class with seven undergraduate students from different majors was held on the subject of "home camera." The design process consisted of (a) user research and need definition based on machine learning understanding and (b) machine learning-based interaction design development using a coding platform. The course was designed to explore the role of designers who implement human-centered design planning and prototypes considering machine learning tools. In addition, the course provided a basis for suggesting the direction of design education methods given emerging AI technology. The essence of design education is not merely about efficiency; rather, it is about nurturing designers' ability to think critically and use tools to cultivate innovation.

This paper considers the impact of technological processes on human thought, specifically the implications of artificial intelligence (AI) on writing instruction. The main purpose of this paper is to present instructional considerations that will elevate human voice and reduce student temptations to turn to AI unreasonably to produce a piece of writing while still providing responsible options for the incorporation of AI in the writing process.

The current paper examined the impact of a set of individual, technological, and institutional variables on the adoption of artificial intelligence (AI) among teachers at private schools. The rationale for this study lies in its contribution to the understanding of how teacher characteristics, institutional support, and technological perceptions affect AI adoption in educational settings. The study used data collected from teachers (n=306) from seven school located in Azerbaijan in 2024. The study suggested that perceived usefulness of AI increases teachers' use of AI for educational purposes, while perceived ease of use of AI has no statistically significant impact. The study also documented a statistically significant link between institutional policy and the use of AI by colleagues on the one hand, and AI adoption among schoolteachers on the other. Finally, the study found evidence relating to the link between AI adoption and the age of the teacher, such that teachers who are younger were more likely to adopt this technology. Surprisingly, personal innovativeness and level of openness to new experiences did not stimulate teachers to adopt AI for teaching. The findings contribute to improving the field's understanding of teachers' attitudes and motivations for using AI for instructional purposes. The study findings also highlight the role of administrative regulation and school policies in stimulating the adoption of new technologies. These findings contribute to relatively novel literature relating to the application of AI in education and provide useful recommendations for administrators of educational institutions.

This research aims to compare the social studies curricula of Thailand and New York State, USA, analyze their similarities and differences, and propose guidelines for improving the Thai social studies curriculum. The study employed a qualitative research methodology, using documentary analysis of the Thai Basic Education Core Curriculum B.E. 2551 (Revised B.E. 2560) in the social studies, religion, and culture learning area, and the New York State K-12 Social Studies Framework. The findings revealed that the social studies curricula of Thailand and New York State differ in many aspects, including their fundamental philosophies, goals for student development, curriculum structures, learning content, and instructional approaches. The Thai curriculum emphasizes cultivating morally good citizens with a love for the nation, while the New York curriculum focuses on developing knowledgeable, skilled citizens who actively participate in a democratic society. In today's rapidly changing world, the development of Thailand's social studies curriculum should foster 21st-century skills, digital citizenship, and a sense of global citizenship among learners while maintaining Thai identity and values. Policy-level recommendations include creating a new vision, designing a flexible curriculum, developing online platforms, and integrating artificial intelligence. At the practical level, suggestions include creating community learning innovations, using the city as a classroom, developing a competency-based curriculum, and building learning communities with local partners.

The emergence of innovative artificial intelligence (AI) technologies, such as ChatGPT, which was just released in November 2022, has the potential to significantly transform the current state of education. Put another way, learning is changing as a result of chatbots' personalized assistance, group discussions and collaborations, evaluations of students' work, encouragement of self-directed learning, and raised student enthusiasm and engagement. A significant number of scholarly works has been done to examine the use of ChatGPT in education. This study aims to make a valuable contribution by conducting a bibliometric analysis of previous research, namely studies conducted throughout the study period, which mostly examined the implementation of ChatGPT in educational settings. The crucial data gathered from this analysis will help to identify the linkage and themes within the published literature. Lastly, this study will attempt to highlight the literature gap which future studies can be conducted in this very area of interest.