Efficacy of Artificial Intelligence (AI) Voice Cloning in Phonetic Self‐Imitation for L2 Pronunciation Training

With the rapid development and widespread adoption of artificial intelligence (AI) tools, the implementation of instructional pedagogy has transformed significantly. English teachers need to understand how AI tools can improve their teaching and must acquire the necessary technical and pedagogical knowledge to effectively utilize AI technology. Although the integration of AI into language teaching shows potential benefits, there remains a dearth of comprehensive research on English teachers’ perceptions, readiness, and professional development requirements in relation to AI.To address these knowledge research gaps, our study aims to evaluate English teachers’ current understanding of AI tools and their training needs for integrating AI into the English language classroom. Our proposed model uses the technological pedagogical content knowledge (TPACK) framework, which incorporates English language teaching and information literacy contexts. This framework allows for a holistic assessment of teachers’ readiness for integrating AI within English language teaching practices.A study was conducted with a class of preservice English teachers in Hong Kong. An online survey was designed to assess the readiness of English teachers for applying AI tools in the classroom as well as their understanding and level of information literacy. This study helped identify and address potential issues with the survey before launching it to a wider audience. Our findings confirmed the validity and reliability of the instrument and indicated that preservice English teacher participants are generally prepared to integrate AI tools into the English classroom. Corelation analysis was also conducted to assess the relationships among the constructs and showed that technological pedagogical knowledge (TPK) and instructional literacy (IL) were significant predictors of the overall TPACK construct. The study suggested professional training in the selection, implementation and progress monitoring of specific AI tools for English Language teaching; pedagogy design; and the ability to search for appropriate resources for the English classroom. The framework can be enhanced by using a mixed-method approach and incorporating a qualitative study to triangulate the findings. An explanatory sequential design will be recommended to collect quantitative data first, then qualitative data will be collected for further analysis.

As the user of communication especially in English, the speaker has to consider the interlocutor’s position in order to achieve good communication. Here, the speakers which include native and non-native English speakers must choose an appropriate language style for the different interlocutors to avoid social consequences. The purposes of this research were to analyze the use of language style of those speakers in The Ellen Show. Also, it focused on the differences and the similarities between those speakers. Last, it focused on the factors influencing the use of language style. The research used the qualitative method which focuses on content analysis. Here, it focused on three native speakers and three non-native speakers of English as the guests in The Ellen Show. The Ellen Show is a talk show program with a casual discussion that talks about a particular topic or issue which consists of a host, the guest(s) being interviewed, the home audience, and the studio audience from which the host might get some responses from.The findings revealed that the native English speakers used all types of language styles. Meanwhile, the non-native speakers used three types of language styles. Then, the similarities were that both speakers applied formal style, consultative style, and casual style in their utterances. However, the difference was the non-native English speakers did not apply frozen style and intimate style. Furthermore, those speakers used language style because it influenced the participant, the setting, the topic, and the function. Therefore, it is concluded that language styles were useful in English utterances either by native speakers or non-native English speakers.
 
 The speaker has to consider the interlocutor’s position in order to achieve good communication. Here, the speakers which include native and non-native English speakers must choose an appropriate language style for the different interlocutors to avoid social consequences. The purposes of this research were to analyze the use of language style of those speakers in The Ellen Show. Also, it focused on the differences and the similarities between those speakers. Last, it focused on the factors influencing the use of language style. The research used the qualitative method which focuses on content analysis. Here, it focused on three native speakers and three non-native speakers of English as the guests in The Ellen Show. The findings revealed that the native English speakers used all types of language styles. Meanwhile, the non-native speakers used three types of language styles. Then, the similarities were that both speakers applied formal style, consultative style, and casual style in their utterances. However, the difference was the non-native English speakers did not apply frozen style and intimate style. Furthermore, those speakers used language style because it influenced the participant, the setting, the topic, and the function. Therefore, it is concluded that language styles were useful in English utterances either by native speakers or non-native English speakers.

The primary aim of this study was to determine if adults whose native language permits neither voiced nor voiceless stops to occur in word-final position can master the English word-final /t/-/d/ contrast. Native English-speaking listeners identified the voicing feature in word-final stops produced by talkers in five groups: native speakers of English, experienced and inexperienced native Spanish speakers of English, and experienced and inexperienced native Mandarin speakers of English. Contrary to hypothesis, the experienced second language (L2) learners' stops were not identified significantly better than stops produced by the inexperienced L2 learners; and their stops were correctly identified significantly less often than stops produced by the native English speakers. Acoustic analyses revealed that the native English speakers made vowels significantly longer before /d/ than /t/, produced /t/-final words with a higher F1 offset frequency than /d/-final words, produced more closure voicing in /d/ than /t/, and sustained closure longer for /t/ than /d/. The L2 learners produced the same kinds of acoustic differences between /t/ and /d/, but theirs were usually of significantly smaller magnitude. Taken together, the results suggest that only a few of the 40 L2 learners examined in the present study had mastered the English word-final /t/-/d/ contrast. Several possible explanations for this negative finding are presented. Multiple regression analyses revealed that the native English listeners made perceptual use of the small, albeit significant, vowel duration differences produced in minimal pairs by the nonnative speakers. A significantly stronger correlation existed between vowel duration differences and the listeners' identifications of final stops in minimal pairs when the perceptual judgments were obtained in an "edited" condition (where post-vocalic cues were removed) than in a "full cue" condition. This suggested that listeners may modify their identification of stops based on the availability of acoustic cues.

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

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Artificial Intelligence (AI) tools can potentially revolutionize classroom practices. English as a Foreign Language or Second Language (EFL/ESL) classrooms in higher education (HE) are also privy to the transformative features of AI. However, there is limited research on Bangladeshi HE teachers’ AI usage in teaching. This study, recognizing this gap, investigates Bangladeshi English teachers’ experiences and perceptions of using AI tools at the tertiary-level English classes. Data were collected employing an open-ended qualitative survey distributed among 17 English teachers from various public and private universities in Bangladesh. A thematic analysis suggests that most participants regularly use Turnitin, ChatGPT, and Grammarly among other AI tools. While some teachers did not routinely integrate AI tools into classroom activities, most considered them an efficient auxiliary tool for improving writing, vocabulary, grammar, designing materials, and detecting plagiarism. The results also revealed teachers' concerns about their lack of AI readiness and students’ malpractices using AI such as cheating and disinclination towards critical thinking. Based on the findings, the researchers discovered that effective integration of AI tools in learning can occur through a balance of human-led and AI-led instructions. The study recommended the need for teachers’ capacity building and skills training as mediators for contextually relevant AI usage, predominantly in writing. Spectrum, Volume 18, June 2023: 89-100

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

ChatGPT and other artificial intelligence applications speed up scientific writing.

Using an opportunity where students were explicitly permitted to use artificial intelligence (AI) applications to prepare an assignment, we compared the practices and beliefs of two distinct student cohorts: second-year science students at a large metropolitan university in Australia and students at a partner institution in the People's Republic of China (PRC). Data from an anonymous survey revealed that over 50% of students employed AI tools, primarily for idea generation and initial drafting, instead of using these tools as a source of text they would use in the assignments without editing or attribution. Reasons for use showed differences between native English speakers, their nonnative speaking classmates, and their classmates attending the PRC campus. Across the cohorts, there was a measured engagement with AI tools when preparing assignments. Notably, all cohorts declared a greater willingness to use such tools if institutional licenses were made available. Most students believed that they would use AI in their future professional roles and that their institutions should be providing guidance on the proper use of AI tools. At the Australian campus, sanctioning the use of AI in preparing the class assignment was not associated with any change in the average assignment marks from those gained by a cohort from the previous year where no sanction was in place. At the same time, there was a significant improvement in the average mark for the PRC students, which was associated with the same sanction.NEW & NOTEWORTHY We compared second-year science students at an Australian university and a partner institution in China on their use of AI tools for assignments. Over 50% used AI for idea generation and drafting. Notably, PRC students showed improved grades when AI was permitted, while Australian students saw no change. Most students expressed a willingness to use AI in their future careers and called for institutional guidance on AI use, especially if official licenses were provided.

Marketing scientists as well as practitioners believe that artificial intelligence (AI) holds the promise of productivity gains for organizations. However, there has been little scientific research into these theories. This study investigates the role of AI in enhancing marketing productivity, deriving insights from a case study conducted with the marketing team of an industrial software start-up. Drawing upon Case Study Analysis by Yin (2018) and Participatory Action Research by Kemmis and McTaggart (2007), the study employs a combination of survey interviews, AI tool research and AI tool testings. Key findings indicate that productivity gains are more likely than productivity impairments with the use of marketing AI tools. This effect is even stronger when knowledge workers possess high levels of AI skills and utilize AI tools with suitable capabilities. Having closely analyzed six marketing disciplines, particularly SEO / content and design demonstrated significant productivity gains including generative AI (GAI) tools the team already subscribed to like ChatGPT 4 and Canva, but also new AI solutions. While an AI tool’s level of integration only showed a weak positive productivity impact, future studies are suggested to further investigate this variable by comparing the effects of less advanced but more accessible tools like generative AI versus highly advanced, but less accessible business AI. Having navigated the vast and dynamic landscape of AI tools, insights further emphasize the importance of AI experience sharing and informed decision-making, implying knowledge of own user rights and always staying updated on AI advancements. Zooming out from process level, the work's literature review further highlights the role of environmental and organizational AI enablers, like budget allocation, fostering AI trust and mindset, but also implementing AI routines and responsibilities. Overall, this research underscores the imperative for companies, especially startups and SMEs, to explore AI technology as a means to enhance productivity and gain a competitive edge.

Vocabulary plays a crucial role in the teaching and learning of second or foreign languages. Given the latest technological advancements, especially concerning new artificial intelligence (AI) tools, it is essential to explore and analyze the impact of these tools on English language teaching. As a result, this study examines educators' views on the use of AI tools for teaching English vocabulary to students of the English language in Jordan. It also explores their perspectives on the most common AI tools, integration scenarios, and challenges. A mixed-methods research strategy was employed, with qualitative data consisting of open-ended exploratory questions. The two research designs were randomly assigned to 100 public school English language teachers in Amman, Jordan. The statistical analysis of the quantitative data was conducted using SPSS version 29, while the qualitative data underwent thematic analysis. The quantitative data indicated that English language instructors had a positive perspective on the benefits of using AI tools in English language teaching in general and for vocabulary acquisition in particular. This numerical data supported their views, demonstrating that these tools are appropriate and effective as they engage students and promote their autonomy in learning. The top five AI tools were ChatGPT, Kahoot, Duolingo, Quizlet, and Google Translate. However, English teachers expressed concerns about their lack of familiarity with and insufficient training on AI tools, ethical concerns regarding personal data privacy, a shortage of quality resources, and limited time due to the focus on completing the course delivery plan. These findings are of educational importance, as current textbooks should incorporate these AI resources to improve teaching efficiency. Additionally, teachers should receive adequate training to use these resources effectively, ensuring they can provide engaging and effective instructional methods for their learners.

ABSTRACTTo test the effects of reading speed on second-language (L2) sentence processing and the potential influence of conflicting native language word order, we compared advanced L2 learners of English with native English speakers on a self-paced reading task. L2 learners read faster overall than native English speakers. When differences in reading speed were controlled for, L2 learners were as sensitive to grammaticality manipulations as native English speakers. On-line reading times did not reflect any effect of cross-language conflict in the learners. Results from an end-of-sentence verification task showed a stronger bias toward a subject–object order in the cross-language conflict conditions in speed-matched L2 learners but not in L2 learners reading faster than native speakers. Results are compatible with hypothesized differences in resource allocation between L2 and native language processing.

Systematic literature reviews (SLRs) are essential for synthesising research evidence and guiding informed decision-making. However, SLRs require significant resources and substantial efforts in terms of workload. The introduction of artificial intelligence (AI) tools can reduce this workload. This study aims to investigate the preferences in SLR screening, focusing on trade-offs related to tool attributes. A discrete choice experiment (DCE) was performed in which participants completed 13 or 14 choice tasks featuring AI tools with varying attributes. Data were collected via an online survey, where participants provided background on their education and experience. Professionals who have published SLRs registered on Pubmed, or who were affiliated with a recent Health Economics and Outcomes Research conference were included as participants. The use of a hypothetical AI tool in SLRs with different attributes was considered by the participants. Key attributes for AI tools were identified through a literature review and expert consultations. These attributes included the AI tool's role in screening, required user proficiency, sensitivity, workload reduction and the investment needed for training. The participants' adoption of the AI tool, that is, the likelihood of preferring the AI tool in the choice experiment, considering different configurations of attribute levels, as captured through the DCE choice tasks. Statistical analysis was performed using conditional multinomial logit. An additional analysis was performed by including the demographic characteristics (such as education, experience with SLR publication and familiarity with AI) as interaction variables. The study received responses from 187 participants with diverse experience in performing SLRs and AI use. The familiarity with AI was generally low, with 55.6% of participants being (very) unfamiliar with AI. In contrast, intermediate proficiency in AI tools is positively associated with adoption (p=0.030). Similarly, workload reduction is also strongly linked to adoption (p<0.001). Interestingly, if expert proficiency is needed for the AI, authors with more scientific experience in their profession are less likely to adopt AI (p=0.009). However, more experience specifically with SLR publications increases AI adoption likelihood (p=0.001). The findings suggest that workload reduction is not the only consideration for SLR reviewers when using AI tools. The key to AI adoption in SLRs is creating reliable, workload-reducing tools that assist rather than replace human reviewers, with moderate proficiency requirements and high sensitivity.

PurposeThe purpose of the paper is to explore the rapidly evolving landscape of artificial intelligence (AI) tools in academic research, highlighting their potential to transform various stages of the research process. AI tools are transforming academic research, offering numerous benefits and challenges.Design/methodology/approachAcademic research is undergoing a significant transformation with the emergence of (AI) tools. These tools have the potential to revolutionize various aspects of research, from literature review to writing and proofreading. An overview of AI applications in literature review, data analysis, writing and proofreading, discussing their benefits and limitations is given. A comprehensive review of existing literature on AI applications in academic research was conducted, focusing on tools and platforms used in various stages of the research process. AI was used in some of the searches for AI applications in use.FindingsThe analysis reveals that AI tools can enhance research efficiency, accuracy and quality, but also raise important ethical and methodological considerations. AI tools have the potential to significantly enhance academic research, but their adoption requires careful consideration of methodological and ethical implications. The integration of AI tools also raises questions about authorship, accountability and the role of human researchers. The authors conclude by outlining future directions for AI integration in academic research and emphasizing the need for responsible adoption.Originality/valueAs AI continues to evolve, it is essential for researchers, institutions and policymakers to address the ethical and methodological implications of AI adoption, ensuring responsible integration and harnessing the full potential of AI tools to advance academic research. This is the contribution of the paper to knowledge.

Artificial Intelligence: The Prevalent Coauthor Among Early-Career Surgeons.