EXPLORING THE POWER OF GENERATIVE AI: ENHANCING BUSINESS EMAIL WRITING SKILLS FOR L2 LEARNERS

This study investigated the impact of generative AI usage on academic engagement among students at a selected College of Education in Ghana. The study aimed to examine the kinds of generative AI tools, identify the different ways students utilize these GenAI in their learning, and assess the overall influence on their academic engagement by employing a sequential explanatory mixed-methods design. This design was chosen to provide a comprehensive understanding through both quantitative and qualitative data. Ninety-four students participated in the quantitative phase via purposive sampling and completed a survey examining the types of generative AI tools they use and the effects on their academic engagement. Additionally, twelve students were interviewed to gather in-depth qualitative insights that could not be captured by the survey. Findings Revealed that generative AI positively influences students’ academic engagement and improves their learning environment. It serves as an effective tool to enhance learning and engagement. However, findings from some respondents via qualitative interview reveal that, excessive reliance on generative AI also poses risks by encouraging laziness and overdependence, less creativity and immersive engagement due to easy access to the AI tools, which may affect academic integrity. The implication for this study is that generative AI tools like ChatGPT spark curiosity by offering instant feedback, tailored learning journeys, and interactive experiences that turn complex concepts into manageable insights. The study highlights generative AI as a double-edged tool: while it empowers students with efficiency, creativity, and deeper engagement, it also risks encouraging shortcuts, dependency, and ethical breaches. Ensuring responsible and ethical integration of AI is therefore vital, with academic integrity anchored in fairness, honesty, and originality remaining at the heart of scholarly practice.

This paper reports preliminary findings from an ongoing, campus wide research project on effective methods for generative AI applicability in pursuit of effective and engaging teaching and learning activities. Generative AI has had a tremendous adoption rate since the public release of ChatGPT 3.5 on November 30th 2022. This has necessitated that educators and administrators consider the potential opportunities and threats usage of generative AI by students and faculty may have on higher education. Recognizing the inevitability of generative AI, the researchers have proposed a university-wide research project to ascertain the changes in faculty and students perspectives when using generative AI The research project is two-fold. First, a longitudinal survey has been developed to address research questions about usage and perceptions of generative AI change over time. The second prong of this research project focuses on the implementation of new and continuing generative AI professional development workshops. These “AI Institutes” are targeted educational opportunities to provide faculty, staff, and students with hands-on experiences that model appropriate ways to teach and learn with generative AI tools. Workshops change based on audience needs, but will be designed to support such processes as introductory and advanced lessons on building learning activities which engage students with generative AI, administrative shortcuts, best practices for writing, and our university’s AI policy and principles. The longitudinal survey, thus, allows the research team to gauge changes in perspectives as the “AI Institutes'' are deployed and widespread adoption of generative AI tools become more mainstream. This paper reports on the first year of this research project, including one survey and one AI Institute. This research on integrating generative AI technologies into teaching and learning has important implications for the field of networked learning. As the paper explores, rapid advances in AI are changing how students and faculty interact with content and each other. Findings from the longitudinal survey and AI Institutes could provide insights into how to thoughtfully leverage these emerging tools to enhance connections, dialogue, collaboration, and co-creation of knowledge within digital learning networks. While further research is needed, this project takes an important first step in assessing faculty and student perceptions that can inform appropriate AI integration. Lessons learned could guide other institutions exploring the potentials and pitfalls of weaving generative AI into networked learning ecosystems.

There is a heightened concern over undergraduate students being over‐reliant on Generative AI and using it recklessly. Reliance behaviours describe the frequencies and ways that people use AI tools for tasks such as problem‐solving, influenced by individual factors such as trust and AI literacy. One way to conceptualise reliance is that reliance behaviours are affected by the extent to which learners consciously evaluate the relative performance of AI and humans, suggesting the potential impacts of critical thinking on reliance. This study, thus, empirically investigates the relationship between critical thinking and reliance behaviours. Critical thinking includes disposition and skills. However, limited empirical studies have investigated how critical thinking influences learners' reliance behaviours when solving problems with Generative AI. Hence, the current study conducted path analyses to investigate how critical thinking is associated with reliance behaviours and how it mediates the effect of individual factors on reliance behaviours. We collected 808 survey responses on critical thinking disposition and skills, reliance behaviours (a self‐developed and validated scale, including reflective use, cautious use, thoughtless use, and collaborative use), trust towards AI, and AI literacy from undergraduates after a problem‐solving task with Generative AI. The results indicate that (1) critical thinking is positively associated with the collaborative, reflective, and cautious use of Generative AI, suggesting that these three types of use of Generative AI could be considered desirable behaviours in human–AI problem‐solving; (2) trust positively predicts thoughtless use; (3) critical thinking can offset the influence of trust on collaborative, reflective and cautious use; and (4) critical thinking can amplify the influence of AI literacy on reflective, cautious and collaborative use. This study contributes new insights into understanding the role of critical thinking in fostering desirable reliance behaviours, including reflective, cautious and collaborative use, and provides implications for future interventions when applying Generative AI for problem‐solving. Practitioner notes What is already known about this topic? Generative AI tools can potentially enhance problem‐based learning (PBL) by supporting brainstorming and solution refinement. Reliance behaviours in human‐AI collaboration are influenced by factors such as trust in AI and AI literacy. Strategy‐graded reliance emphasizes the reasoning process leading to reliance behaviours, focusing on thoughtful engagement with AI tools, and this cognitive process can be captured by critical thinking. What this paper adds? Critical thinking is positively associated with the reflective, collaborative, and cautious use of Generative AI. Critical thinking mediates the effects of trust and AI literacy on reliance behaviours, amplifying reflective, cautious and collaborative use while mitigating the thoughtless use of Generative AI. The study introduces a nuanced understanding of reliance behaviours by applying a strategy‐graded framework, emphasising cognitive engagement rather than a purely outcome‐based understanding of reliance behaviours. Implications for practice and/or policy Educational interventions could consider critical thinking when integrating AI tools in problem‐solving contexts. Students' trust in AI needs to be balanced with critical thinking skills to reduce overreliance and enhance thoughtful engagement with AI tools.

Generative AI (GenAI) tools such as ChatGPT, Gemini and Copilot have created concerns in academia, particularly after the launch of ChatGPT. GenAI and AI have been the buzz words and academics are discussing about the possibilities of its positive and negative impacts on educations and research. Recently, studies have been conducted on the influence of GenAI tools in education and research. With the above concerns and the impact of GenAI, grounded on Vygotsky's Zone of Proximal Development (ZPD) as a theoretical lens, this study explores how English language teachers integrate GenAI tools to enhance teaching and learning. Particularly, this study explores the integration of GenAI tools in English language teaching and learning, focusing on teaching efficiency, student engagement, personalized learning, and writing skills, subscribing to exploratory research methods grounded on semi-structured interviews. The findings of the study affirmed the positive impact of GenAI tools on teaching efficiency, students’ engagement, and writing skills. The results indicated that GenAI positively influences teaching efficiency and student engagement in learning. The implications of this research highlighted the potential of GenAI tools to create a more intelligent and personalized learning environment for English language teaching that benefits both educators and learners.

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>There are few studies on factors influencing cloud computing adoption in higher education institutions. However, there are lacks of understanding of the cloud computing adoption issues in the university. The main objective of this study is to investigate factors influencing cloud computing adoption in a higher education institution. The research method involved using qualitative interviewing with relevant stakeholders and case study at one public university in Malaysia. The analysis was done by using Atlat.ti. There are eighteen factors that have been coded into three main categories of Technological, Organizational and Environmental. These are among factors to influence the decision of cloud computing adoption for a public university. The first category (Technological) consists of nine factors; (i) relative advantage, (ii) cost reduction, (iii) ease of use, (iv) compatibility, (v) operational requirement, (vi) security, (vii) sustainability, (viii) trialability and (ix) complexity, The second category (organizational) consists of four factors; (i) infrastructure readiness, (ii) top management, (iii) knowledge and IT skillset and (iv) financial. The third category (environmental) consists of five factors; (i) Cloud Service Provider, (ii) Geographical, (iii) Data Privacy, (iv) Guideline and Policy, (v) Service Level Agreement (SLA). The result may provide a reference for the adoption of cloud computing in the area of mobile learning or mobile computing. Future work involves conducting similar studies at other case studies including public and private universities in Malaysia.</p></td></tr></tbody></table>

Background and Objectives: The emergence of AI-driven writing assistants has sparked discussions on the potential benefits and drawbacks of these tools in higher education. While AI tools can enhance writing skills, provide instant feedback, and facilitate brainstorming, concerns persist regarding academic integrity, ethical considerations, and over-reliance on AI. This study aimed to explore Thai university students' perceptions and experiences with generative AI tools—ChatGPT and Gemini—within the context of academic writing in English. By examining these students' subjective experiences, this research also sought to better understand the benefits and risks associated with AI-driven writing assistants in higher education, particularly in Thailand’s unique academic environment. Methodology: A qualitative approach was employed using semi-structured interviews with 12 Thai university students selected through purposive sampling. The study applied thematic analysis to identify key patterns and insights from the participants' responses. NVivo software was used for data organization. The study was grounded in the constructivist paradigm, emphasizing students' subjective experiences and contextual understanding of AI usage in academic writing. Main Results: Thai university students view ChatGPT and Gemini as valuable aids for academic writing, particularly in brainstorming, structuring ideas, and improving grammar. AI-assisted feedback boosted confidence and writing quality, but concerns about over-reliance, academic integrity, and ethical considerations were prominent. Students employed strategies like paraphrasing and cross-referencing AI-generated content to ensure originality. While AI enhanced language learning through real-time feedback, some feared it might lead to superficial learning and reduced engagement in skill development. The findings underscore the need for clear academic guidelines to help students balance AI use with independent learning. Discussions: This study highlights the dual nature of AI integration in academic writing—offering both significant advantages and potential risks. Students demonstrated a pragmatic approach, leveraging AI for efficiency while maintaining an awareness of ethical considerations. Their cautious engagement suggests that AI is seen as a supplementary tool rather than a complete replacement for traditional writing and learning methods. The findings align with broader discussions on responsible AI use in education, emphasizing the importance of balanced and mindful engagement with AI technologies. The study also underscores the importance of institutions developing clear guidelines on AI usage, as well as offering digital literacy programs that can help students navigate the ethical and practical aspects of AI integration. These efforts could ensure that AI tools enhance the learning experience without compromising academic integrity or the development of essential writing skills. Conclusions: This study provides valuable empirical insights into how Thai university students perceive and utilize AI tools, particularly ChatGPT and Gemini, in academic writing. AI tools were found to significantly support writing quality and language development, yet concerns over over-reliance, ethics, and integrity remain. The findings stress the necessity of institutional policies and structured guidance to foster responsible AI use. Future research should include cross-cultural comparisons to examine variations in AI adoption within higher education. Longitudinal studies could assess AI’s long-term impact on writing proficiency, and targeted interventions should be developed to promote balanced and ethical AI integration in academic contexts.

The influence of service quality on organizational performance has captured a greater attention in corporate and academic world. The public universities in Malaysia are no different than such corporate world in terms of quality, services, and outcome. Hence, investigation of the influence of academic service quality on the organizational performance in public universities in Malaysia is the key attention of this study. A survey was conducted by drawing a sample of 435 international students from three public universities in Malaysia, by using a questionnaire developed by modification of SERVQUAL. The modified questionnaire assessing academic service quality comprises of five dimensions such as academic resources, academic competence, skills development, attitude, and responsiveness. Data obtained were analyzed by using techniques such as principal component analysis, one-way ANOVA, correlation, and multiple regression. Results indicated that the three universities provided the same level of academic service quality. It was also found that all the five dimensions of academic service quality significantly influenced the organizational performance. This study concludes by advocating the need of comparative studies between public and private universities in Malaysia and across countries.

This systematic review investigates the ethical challenges and strategic responses surrounding the use of Generative AI (GenAI) and related tools in academic writing within global higher education. Following the PRISMA 2020 framework, a rigorous search and screening process across academic databases identified 18 peer-reviewed articles published between 2020 and 2025, which were subjected to in-depth thematic analysis. The findings reveal four major ethical concerns: threats to academic integrity through plagiarism, authorship misrepresentation, and diminished originality; issues of bias and fairness arising from algorithmic limitations and unequal access to technology; limited transparency due to nondisclosure of AI use and the absence of clear citation standards; and risks to data privacy linked to the use of student and proprietary information. In response, the literature highlights strategies that include the development of institutional ethical guidelines and policies, enhanced digital literacy and training for faculty and students, improved design and regulation of AI tools with embedded ethical safeguards, and the promotion of transparent human–AI collaboration guided by human oversight. This review demonstrates the significance of adopting a comprehensive, multi-layered approach rather than relying on isolated interventions. For educators, it underscores the need to cultivate critical digital literacy skills; for policymakers, it emphasizes the importance of enforceable and context-sensitive frameworks; and for researchers, it points to future inquiry on the ethical–technological nexus. Collectively, the findings provide actionable insights to ensure that GenAI’s integration into academic writing supports integrity, fairness, and trust in higher education.

Thus, the appearance of the Generative Artificial Intelligence opened up a great turn in many areas, including education and creative industries. This paper seeks to understand the deep impact that Generative AI is going to have on learning and creating processes for the social context of Generation Z (Gen Z) students – born in digital culture. The work looks into the possibilities and challenges that Gen Z in collaboration with Generative AI leads to the future of learning and creativity. This paper is relevant as it offers some understanding of the ongoing changes in the education and creativity together with the escalating growth in technology. The nature of the association between the members of Generation Z and the Generative AI needs to be known by the educational stakeholders, policymakers, and business executives to leverage value from the existing and upcoming technologies together with dealing with possible negative impacts. The purpose of this study was to explore the nature and uses of Generative AI, and its effects on the learning and creativity of Gen Z, in addition to identifying the advantages, disadvantages, opportunities, and risks/partities’ concerns that are commensurate with the integration of this technology in teaching/learning and creative processes. To achieve the objectives of the study the following research methodology was used: The research used both a literature review and documentary research. The materials used included academic publications, Industry reports, books and other credible internet sources on Generative AI and its impact on the education and creativity of the Gen Z. The document analysis included policy papers, educational technology reports, case studies and white papers from academic and professional bodies as well as other industries that involve Generative AI. Several insights show that using Generative AI can positively impact learners’ experiences, engagement, and creativity. However, there was some controversy about the excessive usage of AI and claimed that because of it people may get worse at critical thinking. The following were noted to be major concerns; Ethical Issues: they included issues to do with bias in the algorithms as well as the right to privacy of data. Thus, the findings of this research point to a three-way settlement with respect to the use of Generative AI in education and creative industries. It underlines the guideline of how human creativity and critical thinking ought to be sustained, while using AI tools. Proposals include, the need to teach critical thinking alongside AI use, fostering ethical AI consciousness, surged AI education, appropriate non-ethnical AI data set, strong AI policies and pro positive AI inspires and creative constructive use. The research implications for future studies include studying the changes in the achievement of learning outcomes over a period of time, wherein Generative AI has been incorporated and understanding how this technology influences different learning styles and needs, the issues of ethical and privacy concern, the requirement of professional development to educators in relation to Generative AI and finally, the comparison information and communication technology for learning between different cultures. Related to that, further studies on the effectiveness of AI in approaches like collaborative learning, its potential on preparing learners for employment, and on the psychology of students would be helpful in informing the future advancement of Generative AI in school and particular creative areas.

Although artificial intelligence is fundamentally reshaping the ecology of music learning, existing research has disproportionately emphasized performance outcomes while underexamining psychological mechanisms, leaving the tension between technological empowerment and cognitive dependence theoretically underarticulated. Following PRISMA 2020, we systematically searched four databases and included 21 empirical studies to examine how three AI tool types-assessment-oriented AI, generative AI, and Comprehensive/adaptive AI-differentially shape learners' self-beliefs and cognitive agency in music education. The evidence base remains geographically and developmentally concentrated: most studies were conducted in China and in higher education, while early childhood settings were absent. Using thematic analysis, we conducted cross-type comparisons and synthesized psychological pathways. Assessment-oriented AI most consistently strengthened ability beliefs via objectified, visualized feedback and positioned cognitive agency around self-monitoring, self-reactiveness, and self-reflectiveness. Generative AI tended to enhance value-attitude beliefs and intentionality by lowering technical barriers and reconfiguring learners' creative roles toward aesthetic decision-making and output curation. Comprehensive/Adaptive AI more often supported forethought and sustained engagement by dynamically maintaining alignment between task challenge and learner capability. Across studies, psychological empowerment manifested as increased perceived competence and control, heightened motivation and engagement, and visible self-regulated learning behaviors. Cognitive dependence, however, emerged through outsourcing evaluative authority, score-driven goal distortion, algorithm-accommodating self-censorship, and attributional shifts that tether confidence to technological support. Developmental differences were also observed regarding dependence mechanisms: primary learners tended to perceive AI as a restrictive "scoring referee," whereas higher education students demonstrated strategic agency in orchestrating AI assistance. Specifically, a critical construct-tool mismatch was identified: while assessment AI consistently supports self-reflectiveness, generative AI currently lacks sufficient evidence for fostering learners' forethought. In light of the identified construct-tool mismatch, future research should prioritize addressing the paucity of evidence on how generative and adaptive AI foster forethought and intentionality, thereby clarifying whether such technologies ultimately reconstruct or erode learners' cognitive agency.

This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.

Study purpose This study explores the usage of generative AI tools by journalists in sub-Saharan Africa, with a focus on issues of misinformation, plagiarism, stereotypes, and the unrepresentative nature of online databases. The research places this inquiry within broader debates of whether the Global South can effectively and fairly use AI tools. Design/methodology/approach This study involved conducting interviews with journalists from five sub-Saharan African countries, namely Congo, DRC, Kenya, Tanzania, Uganda, and Zambia. The objective of the study was to ascertain how journalists in sub-Saharan Africa are utilizing ChatGPT. It is worth noting that this study is a component of an ongoing project on AI that commenced on September 19, 2022, shortly after receiving IRB approval. The ChatGPT project was initiated in January 2023 after discovering that our participants were already employing the Chatbot. Findings The study highlights that generative AI like ChatGPT operates on a limited and non-representative African corpus, making it selective on what is considered civil and uncivil language, thus limiting its effectiveness in the region. However, the study also suggests that in the absence of representative corpora, generative AI tools like ChatGPT present an opportunity for effective journalism practice in that journalists cannot completely rely on the tools. Practical implications The study emphasizes the need for human agencies to provide relevant information to the tool, thus contributing to a global database, and to consider diverse data sources when designing AI tools to minimize biases and stereotypes. Social implications The social implications of the study suggest that AI tools have both positive and negative effects on journalism in developing countries, and there is a need to promote the responsible and ethical use of AI tools in journalism and beyond. Originality/value The original value of the study lies in shedding light on the challenges and opportunities associated with AI in journalism, promoting postcolonial thinking, and emphasizing the importance of diverse data sources and human agency in the development and use of AI tools.

Purpose– The aim of this study is to build upon Jainet al.'s (2007) work by investigating the knowledge-sharing barriers and strategies of academic staff in public and private universities in Malaysia which have received relatively little research attention to date.Design/methodology/approach– A survey of 502 questionnaires was collected on both public and private universities in Malaysia. Data were analysed using SPSS.Findings– The overall findings show that private universities are more effective and are more willing to share knowledge. Linking knowledge sharing with non-monetary rewards and fair performance appraisals are strongly recommended in private universities, while monetary rewards, recognitions, publication of knowledge on websites and newsletters and the use of appropriate technology tools and systems are strongly recommended in public universities.Practical implications– Top management in Malaysian public and private universities must play their role and put in more effort to ensure academics have the proper platform and support to share their knowledge.Originality/value– This study is perhaps one of the first to address the comparison between knowledge sharing among lecturers in private and public universities in Malaysia.

Generative AI tools stand at the threshold of innovation and the erosion of the long-standing values of creativity, critical thinking, authorship, and research in higher education. This research crafted a novel framework from the technology, organization, and environment (TOE) framework to guide higher educational institutions in Nigeria to navigate the ethical dilemma of generative AI. A questionnaire was used to collect data from twelve higher institutions among lecturers, students, and researchers across the six (6) geopolitical zones of Nigeria. The structural equation modeling was used to analyze the data using the SPPS Amos version 23. The results revealed that factors such as perceived risks of generative AI, Curriculum support, institutional policy, and perceived generative AI trends positively impact the need for a generative AI ethical framework in higher educational institutions in Nigeria. Furthermore, the study contributes to the adoption of theory to navigate the ethical dilemma in the use of generative AI tools in higher educational institutions in Nigeria. It also provides some practical implications that suggest the importance of inculcating ethical discussions into the curriculum as part of institutional policy to create awareness and guidance on the use of generative AI.

Popular and scholarly critiques regarding the epistemic power of contemporary generative text AI tools raise some interesting questions in regard to Gandini et al.'s heuristic of algorithmic public opinion. This commentary therefore asks: what is the relationship between algorithmic public opinion and AI-generated text? Given the staggeringly fast uptake of generative AI tools, will social media platforms remain key players in the formation and circulation algorithmic public opinion, or does generative AI necessitate critical attention to a new kind of public opinion – one that is shaped, constituted and generated by AI?