‘OMG! You used AI’ – A critical exploration of linguistic stigmatization in the era of generative artificial intelligence

Adult learners are a neglected species in the generative artificial intelligence (GenAI) era. The sweeping changes brought by GenAI in the educational arena have implications for adult learning. GenAI in education will usher in a world of adult learning that will be radically different from its predecessor. However, how adult learners will apply GenAI technologies to achieve their educational and professional goals remains blurred. To address this gap, it is crucial to examine essential principles for integrating GenAI into adult learning. For effective digital transformation of education, GenAI should optimize adult learning and ensure the safety of adult learners. This study proposes a “GenAI adult learning ecology” framework (GenAI-ALE) for higher education institutions in this digital era permeated by GenAI. The GenAI-ALE considers eight (8) essential principles categorized into two main themes; institutional factors (GenAI curriculum design, GenAI divide, GenAI policy, GenAI ethics) and interpersonal factors (GenAI human-centered andragogy, GenAI literacy, GenAI interest, and GenAI virtual learning). Malcolm Knowles’ andragogical model is used to provide a context for integrating GenAI into adult learning. Applying the framework in a real-world context follows four iterative systematic steps; pre-perception and perception, GenAI readiness, assessment, and outcome. Reimagining new forms of adult learning in the GenAI revolution calls for higher education institutions to develop education systems where there is a synergy between humans (adult learners) and GenAI.

The rapid evolvement of Artificial Intelligence (AI) and the launch of ChatGPT and other Generative AI tools have concerned Higher Education Institutions (HEIs), which now need to develop comprehensive pedagogical guidelines and frameworks in this emerging AI era. These advancements have sparked discussions and research on their implications on assessment design and student assessment, with multiple opposing perspectives emerging. Whilst ChatGPT is perceived as an important opportunity for enhancing student learning, it is considered as a significant threat to academic integrity and student skills development. These differing perspectives create the need for teaching staff to reflect on their pedagogical practices on ChatGPT and Generative AI and propose potential paths forward for HEIs. Although research on Generative AI and assessment design is rapidly growing, the perspective of Graduate Teaching Assistants (GTAs) as teaching staff uniquely positioned to bridge the gap between faculty and students, is under-represented. To this end, in this practice paper, GTAs reflect on their dual identities as student and tutor to evaluate differing positions to the role of ChatGPT to support or hinder assessment. By being involved in a ChatGPT project for student assessment for MSc students in Engineering degrees, the authors present their reflections on the impact that ChatGPT and Generative AI technologies may have on HEIs, with a focus on assessment design, as well as on potential paths forward for the sector. This practice paper contributes to the ongoing discussions and research on the development of pedagogical guidelines and frameworks in the Generative AI era.

The innovative development of generative artificial intelligence technology is in its ascendant phase, and the associated technical risks cannot be overlooked. The generated content of generative artificial intelligence originates from the “feeding” of big data, posing risks to the security of data acquisition. The generated content is random, and the negligent behavior of responsible parties can easily lead to the risk of negligent crimes. In the era of intelligence, criminal law faces new challenges posed by the risk of negligent crimes brought about by generative artificial intelligence. In the era of weak artificial intelligence, regulating the risk of negligent crimes involving generative artificial intelligence can effectively fill the legislative loopholes in current data crimes legislation that lack prevention measures for the data security risks of generative artificial intelligence by adding the crime of artificial intelligence negligence endangering data security.

In the era of artificial intelligence, the promotion and application of generative artificial intelligence in the field of education is breaking the traditional educational scene, and it is urgent to build a teacher professional knowledge system that adapts to the background of the times. On the basis of analyzing the fundamental role of the Technological Pedagogical and Content Knowledge (TPACK) framework and the reconstruction role of Generative Artificial Intelligence (GenAI), the TPACK framework integrated with GenAI (Gen+TPACK) was proposed. The connotation of Gen+TPACK framework was analyzed from the dimensions of integration of GenAI and Technological Knowledge (TK), Pedagogical Knowledge (PK) and Content Knowledge (CK), as well as compound Technological Content Knowledge (TCK), Pedagogical Content knowledge (PCK), Technological Pedagogical knowledge (TPK), and Technological Pedagogical and Content Knowledge (TPACK). Four implementation paths of Gen+TPACK application were discussed which were the pedagogical innovation enabled by GenAI technology, the dynamic reconstruction of teaching process assisted by GenAI, the optimization of teaching decision supported by GenAI, and the intelligent expansion of teaching boundary. And finally the challenges faced by the application of Gen+TPACK were analyzed.

Background: This study aims to investigate the cognitive status and willingness to learn about generative artificial intelligence (GenAI) of science and technology (sci-tech) journal editors, and to propose corresponding training strategies to enhance their core competitiveness and promote the development of sci-tech journals in the artificial intelligence (AI) era. Methods: A questionnaire survey was conducted among editors of Chinese sci-tech journals, focusing on the respondents' understanding and attitudes towards GenAI and its usage boundaries, their willingness to learn GenAI technology, preferences for learning methods, and choices for improving learning outcomes and interest. Results: A total of 238 valid questionnaires were collected. The respondents were primarily female editors aged 31-50 with editorial or associate editor titles. Editors, 98.74% of whom had varying degrees of knowledge about GenAI, and 88.66% of whom believed that GenAI would partially replace their work in the future. Only 10.92% of the editors had a clear understanding of the usage boundaries of GenAI in academic publishing, while 84.87% considered it essential to learn GenAI technology. In terms of learning needs for GenAI technology, the most requested knowledge content was methods, techniques, examples of use and policy regulations, followed by technical theories and historical development processes. The preferred learning formats were on-site centralized training, online expert lectures, watching educational videos, regular training at work units, self-study and practice, and live exchanges with experts. The methods to enhance learning effectiveness and interest, in order of priority, include: establishing a learning communication group for constant updates, applying for relevant research projects, formulating incentive policies by the workplace, forming study groups with colleagues or like-minded peers at work, participating in competitions and awards organized by educational training institutions, and writing academic papers. Conclusion: Sci-tech journal editors have a high level of awareness regarding GenAI but lack knowledge of policy guidelines. They have an urgent need to learn GenAI technology, and it is essential to adopt diversified and practical learning strategies along with personalized learning formats to improve learning efficiency. Encouraging the editorial and publishing societies to lead professional and standardized GenAI training programs, and increasing incentives from relevant organizations and institutions to boost editors' enthusiasm for learning, while emphasizing international training is crucial.

Purpose. This article aims to analyze the role of generative artificial intelligence (GenAI), specifically ChatGPT, in educational activities while addressing concerns regarding academic integrity. The study explores the ambiguous boundaries of GenAI’s involvement in coursework, its potential ethical and technological challenges, and the need for clear policies regulating its use in education. Methodology. This study employs a mixed-methods approach, combining bibliometric analysis, direct interaction with ChatGPT, and a survey of Ukrainian students. Findings. The findings of this study reveal several key insights into the use of GenAI, specifically ChatGPT, in educational settings and its impact on academic integrity. The findings underscore the need for educational institutions to develop and implement policies that regulate GenAI’s role in academic activities. While GenAI offers significant potential as a technological assistant, there are risks associated with its misuse, particularly concerning academic dishonesty and the erosion of academic standards. Originality. The study’s originality lies in the comprehensive analysis of the problem of integrating GenAI, in particular ChatGPT, into the educational process from the point of view of academic integrity. For the first time, a systematic view of the stages of user interaction with GenAI has been proposed, potential points of violation of academic integrity at each of these stages are identified, and a “white box” concept has been developed to describe the use of GenAI, which allows controlling input and output parameters, minimizing risks. In addition, the study contains empirical data obtained as a result of a large-scale survey of Ukrainian students on their attitude to the use of GenAI in education, the level of awareness of university policies regarding GenAI, and support for the use of GenAI provided that academic integrity is observed. This outcome allows identifying the gap between existing practices and the need to develop effective strategies for integrating GenAI into the educational process. Practical value. The practical value of the work lies in the fact that the study’s results can serve as the basis for the development of clear recommendations and policies on using GenAI in higher education institutions. The proposed “white box” model can be applied to create practical tools that will help students and teachers understand the potential risks and consequences of using GenAI and develop skills for responsible use of these technologies. The student survey results can be used to inform and ensure dialogue between stakeholders on the optimal ways of integrating GenAI into the educational space, taking into account ethical aspects and the need to maintain academic integrity.

The study essentially aims to analyze the changes occurring within the practice of dakwah as a form of communicating Islamic messages in the era of digital technology and artificial intelligence. The practice of dakwah must retain its prophetic communication dimension even as it adjusts to new communication formats brought about by the digital era and artificial intelligence. This study employs a qualitative approach with a descriptive-analytical method. This method was chosen for its capacity to provide a profound understanding of complex phenomena within their contexts, aligned with the research objective to analyze the changes in communication patterns and their effects on dakwah practices. Data is gathered through documentation and observation of dakwah practices across various platforms and digital media. The study indicates that the shifts in dakwah communication patterns in the digital era and the advancement of artificial intelligence have significant and intricate impacts on the comprehensive communication of Islamic prophetic messages. Key points include: (1) Changes in the meaning and practice of dakwah depict a shift in focus from the spiritual and moral aspects to the dissemination of Islamic messages through technological platforms; (2) Changes in dakwah communication formats and platforms reflect the adaptation of preachers to digital trends and audience preferences; (3) The influence of social media in dakwah practices opens broad avenues for disseminating Islamic messages; (4) Artificial intelligence (AI) has notably contributed to presenting dakwah content to the public; (5) Normative dynamics in digital-era dakwah communication reveal the challenges in maintaining a balance between popularity and the integrity of religious teachings; (6) Ethical challenges and controversies in digital dakwah underscore the need to uphold moral and ethical values in religious communication; and (7) Education and supervision emerge as pivotal in addressing the challenges of digital dakwah. Content creators, preachers, and society at large must be equipped with a proper understanding of technology usage aligned with scholarly and ethical responsibility, ensuring that the conveyance of religious messages remains accurate, substantial, and consistent with Islamic values.

Generative artificial intelligence (GenAI) has received increasing attention from language researchers and educators due to its capability to influence student language performance. Very limited previous meta-analysis studies have examined instructional factors in their analysis, while it deserves great attention. Task-based language teaching (TBLT) is a widely applied language instructional approach. Hence, this systematic review and meta-analysis study adopts TBLT to investigate how these instructional factors (e.g., task-based variables, learner variables) impact student language learning in GenAI contexts. Using the PRISMA guidelines, we collected literature and included 25 empirical studies involving 2,431 participants. The study confirmed the positive influence of GenAI on language learning and identified three moderators—student AI literacy, task planning, and task assessors—that potentially moderate its effectiveness. This finding likely relates to the GenAI tools employed; specifically, ChatGPT—not primarily designed for language learning—was used most frequently. This general-purpose GenAI may require students to understand how AI works, how to plan their tasks, and the necessity of involving teachers as assessors. We suggest teachers should get more involved in language learning with GenAI, and more language learning-specific GenAI should be developed. These suggestions help institutions and teachers to plan their instructions in the AI era.

Objectives The purpose of this study is to analyze topics of media reports regarding generative artificial intellignece and education. Methods For this purpose, topic model, one of the text mining techniques, was used. First, we collected newspaper article data on ‘Generative Artificial Intelligence and Education’ through the Big Kinds service and then performed pre-processing and morphological analysis. Based on this, 30 keywords were analyzed. By measuring the number of topics and deriving the top 10 words for each topic, 13 topics were presented and explained. Results The 13 topics presented through the research results are as follows. Researchers focus on education that fosters artificial intelligence leadership, Gyeonggi GPT and fair art education, digital finance professional training, problems and responses to digital democracy, reliability and ethical use of artificial intelligence, artificial intelligence used in creation in various fields, training programs between Gyeonggi-do and State University of New York, regional cooperation model for artificial intelligence convergence education, artificial intelligence edutech support project and core talent training, Metaverse Lab support project, strengthening employee artificial intelligence capabilities for use in local government administrative work, Busan Metropolitan Office of Education's digital program attracting attention from overseas and changes in cultural arts education methods in the era of artificial intelligence were presented. Conclusions Based on the topics presented in this study, we proposed ways to apply and activate generative artificial intelligence in education. The five measures are strengthening teacher capacity for generative artificial intelligence, establishing guidelines for the development and use of artificial intelligence, learner capacity to use generative artificial intelligence correctly, developing and disseminating generative artificial intelligence teaching cases, and based on spontaneity. This is support from the Artificial Intelligence Teacher Research Group.

Background The rapid development of generative artificial intelligence (GenAI) is reshaping higher education by offering innovative solutions in course design, assessment, and learning experiences. Despite its potential, GenAI integration poses ethical, pedagogical, and practical challenges, but also a risk of academic misconduct. This study explores how Swedish higher education institutions (HEIs) are addressing GenAI through guidelines, policy documents, and public website information. Methods A qualitative manifest content analysis for objectivity and consistency was conducted on GenAI-related documents and website information from Swedish HEIs. Forty-nine institutions were contacted, with 36 providing relevant data. Data collection involved email correspondence and systematic searches on public websites. Results Few formal GenAI guidelines exist across Swedish HEIs. Independent institutions were more likely to have established guidelines for both staff and students, whereas universities or university colleges often provided more GenAI-related information on their websites. Five categories were identified: Good academic practice; GenAI use and governance in education; Information governance; Ethical and social impact; and GenAI essentials, the latter unique to websites. Good academic practice was the most emphasized, focusing on transparency, responsibility, and the challenges of GenAI-related misconduct. Conclusions Taken together, GenAI integration in higher education remains early and uneven, with some institutions implementing formal guidelines while others are still developing policies. This inconsistency calls for national directives to balance GenAI´s benefits with ethical concerns, promote GenAI literacy, and ensure equitable access. Rapid technological change challenges HEIs to update policies that ensure academic integrity and fairness. Future research should foster collaborative policy development among HEIs, policymakers, and technology providers.

Artificial intelligence (AI) is an interdisciplinary field, including mathematics, sciences, psychology, linguistics, sociology, and engineering (Bisconti et al., 2023; Chen, 2020; Zhuang et al., 2020). The development of its technologies requires experts from various disciplines. In education, generative AI (GenAI) tools such as ChatGPT and Dall-E facilitate interdisciplinary learning by breaking down barriers between disciplines and enabling students to see connections across different disciplines, resulting in a more holistic understanding of complex concepts. The tools enable students from different disciplines to collaborate on projects by combining their diverse knowledge and skills (Chiu, 2023, 2024; Chiu et al., 2023). For example, in STEAM education, students who are weak at coding can develop simple applications by asking GitHub Copilot, which generates specific code suggestions including variables, classes, and methods based on their situations. Those who are poor in art designs, can ask t Dall-E to generate visuals, fostering an interdisciplinary approach. Therefore, students can use the tools to complement the knowledge gaps they have because they have instant access to information and resources related to other disciplines (Kusters et al., 2020; Peters et al., 2023). Students become more adaptable, open, flexible, and innovative in their thinking. These enhance critical thinking skills, creativity, and problem-solving skills in students. This interdisciplinary learning using AI also fosters a sense of curiosity and a willingness to explore new ideas, which are essential skills for success in the AI era, better preparing students to address real-world problems and excel in a variety of disciplines. Most related current studies focus on how GenAI affects assessment and essay-based assignments (Chiu, 2024; Xia et al., 2024). However, student engagement in using GenAI tools in completing interdisciplinary learning remains unclear. Student engagement is a multi-dimensional concept generally considered to include behavioral, emotional, cognitive, and agentic components (Chiu, 2021; Fredricks, 2011), which can be explained by self-determination theory (SDT) through needs satisfaction (autonomy: feel having choices; competence: feel capable; relatedness: feel relevant) (Chiu, 2021; Ryan and Deci, 2020). This mixed-methods study investigated how GenAI tools engage students in interdisciplinary learning from SDT perspectives. The two main research questions are: RQ1: How is needs satisfaction related to student engagement in interdisciplinary learning? RQ2: How do GenAI tools satisfy students three SDT needs?. The participants were 310 university students from various academic major backgrounds (sciences: 72; engineering: 78; social sciences: 79; art: 76; others: five). All of them attended a 3-hour workshop on how to use GenAI in completing a project under the topic of sustainability. The participants tried different GenAI tools, and discussed how they completed the project. A questionnaire with validated itemswas used to examine how GenAI tools satisfy their needs for competency in a sustainability project (RQ1) (Chiu, 2024). Three iterative cycles of group interviews were conducted with randomly selected 30 participants to understand the roles and affordances of GenAI in interdisciplinary learning (RQ2). The quantitative findings suggested that during interdisciplinary learning, GenAI can satisfy student needs by fostering classification and generalization skills (grouping knowledge), encouraging unpacking model and solution skills (revising thinking), and providing alternative intelligence (inspiring with new ideas). The qualitative findings from this study were used to propose a framework for using GenAI to support interdisciplinary learning. The framework also visualizes the roles and affordances of GenAI in the learning.

As research progresses on the use of generative artificial intelligence (GenAI) tools like ChatGPT in education (Chiu et al., 2023; Law, 2024), there is an increasing need to understand students’ perceptions and their relationship with this technology (e.g., Ericsson et al., 2023), particularly in trilingual environments where the medium of instruction is not their first language (Chan & Hu, 2023). This chapter addresses this need by investigating university students’ perceptions of using GenAI within an English for Academic Purposes (EAP) course at a higher education institution in Hong Kong, exploring its potential and challenges in enhancing learning experiences. Through a case study design, students had both positive and negative perceptions toward GenAI and the tools that range from emotional reassurance they had when developing their academic language skills to ethical concerns such as cheating and privacy. These findings provide insights into the sustainable integration of GenAI chatbots in language classrooms, balancing technological advancements with ethical considerations to enhance educational outcomes. This chapter contributes to the ongoing discourse on the role of AI in education by offering practical strategies educators can use in collaboration with their students to structure GenAI and chatbot usage within the classroom while mitigating its associated challenges.

The rapid advancement of Generative Artificial Intelligence (GAI) technologies has significantly impacted various sectors, including higher education. This study investigates the behavioral intention of students in higher education institutions to adopt GAI and its effect on their academic performance mediating the intention to use GAI. This study uses an analytical cross-sectional design to assess the current relationships among behavioral intention factors, intention to use GAI, and academic performance. Data were collected using a quantitative approach from 279 online and 105 student self-administered responses through purposive sampling. Purposive sampling was employed to target students with GAI experience, ensuring relevant insights aligned with the study’s objective of examining adoption patterns among the active users in higher education settings. The students represent seven higher education institutions that are accredited by the University Grants Commission, Nepal. A seven-point Likert scale measured variables like performance expectancy, effort expectancy, social influence, facilitating conditions, intention to use GAI and academic performance. The final sample size was 384, and pilot testing ensured instrument validity. Data analysis was conducted using SMART Partial Least Square (PLS). SmartPLS was chosen for its capacity to handle complex models, making it suitable for analyzing predictive relationships without requiring the normal data distribution. The results show that all the factors of behaviorual intentions significantly influence the intention to use GAI; however, the effort expectancy and social influence did not influence academic performance. The mediating role of intention to use GAI was also ensured. The findings highlight the need for educational institutions to provide targeted training and clear ethical guidelines for responsible GAI use, emphasizing its integration into curricula to enhance academic performance.

Generative AI tools and assessment: Guidelines of the world's top-ranking universities

The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. As a result, assessment reform has become a pressing topic in the GenAI era. This paper presents the findings of a pilot study conducted at British University Vietnam exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from “no AI” to “full AI,” enabling educators to design assessments that focus on areas requiring human input and critical thinking. The pilot study results indicate a significant reduction in academic misconduct cases related to GenAI and enhanced student engagement with GenAI technology. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in higher education, promoting academic integrity while leveraging technology’s potential to enhance learning experiences. Implications for practice or policy: Higher education institutions should adopt flexible frameworks like the AIAS to guide ethical integration of GenAI into assessment practices. Educators should design assessments that leverage GenAI capabilities, while supporting critical thinking and human input. Institutional policies related to GenAI should be developed in consultation with stakeholders and regularly updated to keep pace with technological advancements. Policymakers should prioritise research funding into the impacts of GenAI on higher education to inform evidence-based practices.