A Teaching Practice of Integrating Generative AI Tools into ESG Proposal Tasks: A Case Study of Project-Based Learning in a Brand Public Relations Strategy Course

L2 learners at higher education often face difficulties in writing business emails in English, which hinder effective workplace communication and academic success. Therefore, higher education institutions should educate their learners about business email literacy. This study analysed how thirty-one business degree Malay students at a public university in Malaysia utilized generative AI tools for composing business emails. These participants presented their reflection investigation on the application of generative AI in business email writing for their group class assignment for English for Business Communication. This study's research approach included a qualitative document analysis on PowerPoint presentation slides of the participants, and thematic analysis was used to analyse the data. The findings reveal three themes emerged from the study; Theme 1: Preferred generative AI, Theme 2: Optimising generative AI prompts for business email writing, and Theme 3: Ethical usage of AI. Theme 1 has three sub-themes; user friendliness, relevant business contexts and quality generated texts. The results showed that all groups had different AI tools due to their personal choices. Theme 2 has two sub-themes; prompts for external email and prompts for internal email. The participants used generative AI tools for idea expansion and paraphrasing. These L2 learners also wrote specific prompts for different types of email. Two emerging sub-themes of Theme 3 are writing assistance and best practices on ethical usage of generative AI. The participants stressed the significance of understanding plagiarism and effectively using generative AI tools. Learners should be educated on intellectual property and ethical AI tool usage. Higher education institutions should integrate these tools into their courses to enhance business email writing skills and prepare students for AI-driven workplaces, fostering ethical and effective usage.

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.

IntroductionIn recent years, numerous AI tools have been employed to equip learners with diverse technical skills such as coding, data analysis, and other competencies related to computational sciences. However, the desired outcomes have not been consistently achieved. This study aims to analyze the perspectives of students and professionals from non-computational fields on the use of generative AI tools, augmented with visualization support, to tackle data analytics projects. The focus is on promoting the development of coding skills and fostering a deep understanding of the solutions generated. Consequently, our research seeks to introduce innovative approaches for incorporating visualization and generative AI tools into educational practices.MethodsThis article examines how learners perform and their perspectives when using traditional tools vs. LLM-based tools to acquire data analytics skills. To explore this, we conducted a case study with a cohort of 59 participants among students and professionals without computational thinking skills. These participants developed a data analytics project in the context of a Data Analytics short session. Our case study focused on examining the participants' performance using traditional programming tools, ChatGPT, and LIDA with GPT as an advanced generative AI tool.ResultsThe results shown the transformative potential of approaches based on integrating advanced generative AI tools like GPT with specialized frameworks such as LIDA. The higher levels of participant preference indicate the superiority of these approaches over traditional development methods. Additionally, our findings suggest that the learning curves for the different approaches vary significantly. Since learners encountered technical difficulties in developing the project and interpreting the results. Our findings suggest that the integration of LIDA with GPT can significantly enhance the learning of advanced skills, especially those related to data analytics. We aim to establish this study as a foundation for the methodical adoption of generative AI tools in educational settings, paving the way for more effective and comprehensive training in these critical areas.DiscussionIt is important to highlight that when using general-purpose generative AI tools such as ChatGPT, users must be aware of the data analytics process and take responsibility for filtering out potential errors or incompleteness in the requirements of a data analytics project. These deficiencies can be mitigated by using more advanced tools specialized in supporting data analytics tasks, such as LIDA with GPT. However, users still need advanced programming knowledge to properly configure this connection via API. There is a significant opportunity for generative AI tools to improve their performance, providing accurate, complete, and convincing results for data analytics projects, thereby increasing user confidence in adopting these technologies. We hope this work underscores the opportunities and needs for integrating advanced LLMs into educational practices, particularly in developing computational thinking skills.

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.

The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used generative AI tools, ChatGPT, Bing and Bard. Each AI tool was prompted with an essay question from undergraduate (UG) level 4 (year 1), level 5 (year 2), level 6 (year 3) and postgraduate (PG) level 7 biomedical sciences courses. Anonymised AI generated output was then evaluated by four independent markers, according to specified marking criteria and matched to the Frameworks for Higher Education Qualifications (FHEQ) of UK level descriptors. Percentage scores and ordinal grades were given for each marking criteria across AI generated papers, inter-rater reliability was calculated using Kendall’s coefficient of concordance and generative AI performance ranked. Across all UG and PG levels, ChatGPT performed better than Bing or Bard in areas of scientific accuracy, scientific detail and context. All AI tools performed consistently well at PG level compared to UG level, although only ChatGPT consistently met levels of high attainment at all UG levels. ChatGPT and Bing did not provide adequate references, while Bing falsified references. In conclusion, generative AI tools are useful for providing scientific information consistent with the academic standards required of students in written assignments. These findings have broad implications for the design, implementation and grading of written assessments in higher education.

Generative artificial intelligence tools, such as ChatGPT, are freely available to anyone, including college students. Some perceive these tools as a game changer for higher education because they can enhance student learning experiences in various ways. The integration of generative AI tools in higher education has the potential to revolutionize teaching and learning, making education more accessible, efficient, and effective for students, like the introduction of the calculator. However, there are concerns that generative AI tools can also be misused and lead to unethical behavior. For example, students could use these tools to plagiarize essays, cheat on assignments and exams, and thereby devalue the learning experience for themselves and others. A mixed-method survey was developed to answer the following research questions: 1. How many first-year ME students use generative artificial intelligence tools? 2. How do first-year mechanical engineering students utilize generative artificial intelligence tools? 3. What are the perceptions of first-year mechanical engineering students about the utilization of generative artificial intelligence tools? A survey was given to first-year mechanical engineering students at a four-year public institution. The response rate to the anonymous survey was 69%. The results reveal that 42% of first-year mechanical engineering students are already using generative AI tools, with 75% planning to use generative AI tools in the future. The primary uses by students include idea generation, educational support, and writing assistance. While 61% acknowledge AI's potential for facilitating cheating, 70% believe these tools can enhance learning when used appropriately. The prevailing view among first-year mechanical engineering students is that generative AI, when employed responsibly, can enhance the learning process. This emphasizes the necessity of using generative AI technologies responsibly and adaptably when creating teaching strategies to ensure that they promote academic integrity and learning rather than acting as a barrier to it.

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.

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.

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?

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.

ABSTRACT:Publicly available generative AI tools, such as ChatGPT, Midjourney, and DALL-E 3, have the potential to transform product development by accelerating tasks and improving design ideation. Through case studies of scenario management and persona storyboarding, this research explores the strengths and limitations of generative AI (GenAI) tools. The results highlight GenAI's ability to accelerate routine tasks, improve ideation, and support iterative design, but also reveal limitations in contextual understanding and output quality. Key findings show that effective GenAI integration depends on precise prompt design, iterative interaction and critical validation. Despite their potential, GenAI tools cannot replace human expertise for nuanced design tasks. The study provides actionable insights and best practices for leveraging GenAI tools, paving the way for enhanced human-AI collaboration.

Conflict between the right to privacy and data protection and the right to protect trade secrets must be regarded as more or less inevitable. The balancing of different rights is an issue of fundamental importance in data protection and privacy. Navigating the spectrum between the protection of technological development and the protection of fundamental rights is crucial to ensure safer implementation of generative AI tools. The proportionality principle serves as a globally recognised legal instrument to resolve the existing conflicts of fundamental rights. This poses the question of whether there is a need to reevaluate the balance between the disclosure of technical aspects and privacy and data protection rights and related obligations imposed on privacy engineers when developing generative AI tools. The concept of the proportionality principle, which is composed of the test of necessity, suitability and proportionality `sensu stricto`, can address the most vital tensions or interactions between these rights (especially when supported by the application of the appropriate legal framework). Therefore, this paper contributes not only to the discussion of a balanced approach when implementing AI tools, but also presents some general considerations for the global, regional and country-specific legal regulations (including different types of regulations and modes of enforcement when taking into account some technical aspects of the AI tools) within artificial intelligence that can support achieving this aim. To this end, this paper could be of value not only for lawmakers and developers of generative AI systems, but equally for practitioners, including law firms, to navigate the complex ethical and regulatory landscape in a thoughtful and cautious way.

The object of research is a source code refactoring facilitated and proctored by generative artificial intelligence tools. The paper is aimed at assessing their impact on refactoring quality while determining their practical applicability for improving software maintainability and efficiency. The problem addressed in this research is the limitations of traditional rule-based refactoring tools, which require predefined rules and are often language-specific. Generative AI, with its advanced pattern recognition and adaptive learning capabilities, offers an alternative approach. However, its effectiveness in handling various refactoring tasks and its reliability remain undisclosed. The research involved multiple experiments, where four AI tools – ChatGPT, Copilot, Gemini, and Claude – were tested on various refactoring tasks, including code smell detection, efficiency improvements, decoupling, and large-scale refactoring. The results showed that Claude achieved the highest success rate (78.8%), followed by ChatGPT (76.6%), Copilot (72.8%), and Gemini (61.8%). While all tools demonstrated at least a basic understanding of refactoring principles, their effectiveness varied significantly depending on the complexity of the task. These results can be attributed to differences in model training, specialization, and underlying architectures. Models optimized for programming tasks performed better in structured code analysis, whereas more general-purpose models lacked depth in specific programming-related tasks. The practical implications of this research highlight that while Generative AI tools can significantly aid in refactoring, human oversight remains essential. AI-assisted refactoring can enhance developer productivity, streamline software maintenance, and reduce technical debt, making it a valuable addition to modern software development workflows.

The professional identity of language educators has been significantly influenced by the integration of generative AI tools in teaching writing skills and processes. These tools have, to some extent, assumed roles traditionally held by teachers. This study explores the paradox of adoption and resistance among language educators regarding the use of generative AI in writing instruction. Adopting a quantitative, descriptive-analytical approach, the study utilized a tripartite rubric (adoption, neutrality, resistance) to examine teachers’ attitudes across four AI-mediated writing stages: pre-writing, drafting/ initial writing, revising/editing, and publishing/feedback reception. In order to explore language educators' alignment with the dynamics of adoption, neutrality, and resistance, a total of 340 Arabic and English language teachers from secondary schools in Saudi Arabia participated in the study. Findings indicate that language educators demonstrated a neutral stance toward AI integration in the pre-writing and publishing/feedback reception stages, where AI serves as a supportive rather than a generative tool. This suggests that educators perceive AI as a facilitator in organizing ideas and refining final drafts without undermining their instructional role. Conversely, strong resistance emerged in the drafting/ initial writing and revising/editing stages, where AI directly engages in text production and modification. This reflects educators' concerns about diminished student engagement in writing development and the potential erosion of their professional role. Additionally, these findings reveal teachers’ concerns about how AI might alter their role in teaching writing and their doubts about students’ ability to use these tools responsibly. While adoption was present across all writing stages, it remained marginal, consistently overshadowed by neutrality or resistance. This suggests that, despite some recognition of AI’s potential, most educators remain hesitant to fully embrace it.

Integrating generative AI into digital multimodal composition: A study of multicultural second-language classrooms