How Students Use Generative AI: Insights from a Czech Survey

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

This opinion paper discusses the rapid development of generative artificial intelligence (GenAI) tools, which have significantly impacted educational research. This study examines the diverse applications of GenAI in educational settings, highlighting how it can enhance data analysis, automate literature reviews using generative AI tools, and facilitate personalized learning. By incorporating GenAI techniques such as automated content generation, researchers may expedite the data collecting process, generate insights from large-scale datasets, and develop adaptive learning materials that respond to student needs individually. This study also demonstrates methodological improvements made possible by GenAI, such as enhanced research design and the promotion of collaboration across disciplines. The researchers highlight best practices and potential pitfalls related to using GenAI tools in education research through a review of recent literature and case studies. As generative AI continues to impact the educational system, researchers and educators must exercise caution to maximize its potential. This paper's ultimate goal is to give researchers and educators a framework for efficiently utilizing GenAI technology, stressing the value of data integrity and ethical issues in promoting creative research approaches.

Generative AI use in instructor-assigned learning activities among nursing students: A cross-sectional survey of perceptions, ethical awareness, faculty restrictions, and continuance intention.

This study examined middle school students’ perspectives on generative artificial intelligence (GenAI) tools during a week-long civic engagement camp in which students enhanced their digital media literacy and GenAI knowledge while using an action civics cycle of inquiry to investigate and propose solutions to community issues. Following a lesson that included information on using GenAI for research, students worked collaboratively on inquiry projects addressing issues such as homelessness and animal welfare. The researchers explored students’ perceptions of GenAI’s benefits and challenges using surveys, focus groups, and written responses. Findings indicated that while students appreciated GenAI’s ability to compile and provide detailed information, they expressed skepticism regarding its accuracy and trustworthiness, as well as concerns related to dependence on the technology and ethical issues. This study offers insights for educators seeking to integrate GenAI in middle school education, emphasizing the need for instructional support that addresses both the technological affordances and limitations of GenAI.

User Experience (UX) practitioners, like UX designers and researchers, have begun to adopt Generative Artificial Intelligence (GenAI) tools into their work practices. However, we lack an understanding of how UX practitioners, UX teams, and companies actually utilize GenAI and what challenges they face. We conducted interviews with 24 UX practitioners from multiple companies and countries, with varying roles and seniority. Our findings include: 1) There is a significant lack of GenAI company policies, with companies informally advising caution or leaving the responsibility to individual employees; 2) UX teams lack team-wide GenAI practices. UX practitioners typically use GenAI individually, favoring writing-based tasks, but note limitations for design-focused activities, like wireframing and prototyping; 3) UX practitioners call for better training on GenAI to enhance their abilities to generate effective prompts and evaluate output quality. Based on our findings, we provide recommendations for GenAI integration in the UX sector.

Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Purpose The persistent shortage of cybersecurity professionals, coupled with the consistent increase and complexity of cyberattacks, requires a novel examination of the processes and tasks performed by those professionals to cope with the workloads. Research shows that artificial intelligence (AI) tools often target technical rather than managerial tasks, highlighting the need for continued human involvement in cybersecurity management. This study aims to examine how using Generative AI (GenAI) for cybersecurity managerial tasks has the potential to assist in reducing human errors and perform repetitive tasks, thus, reducing the cybersecurity managerial loads, allowing them to focus on more strategic aspects of their work. Design/methodology/approach This experimental research study used five GenAI platforms: ChatGPT, CoPilot, Gemini, MetaAI and Claude. Each GenAI platform generated a real-life scenario and guidelines for cybersecurity managers associated with a managerial cybersecurity task and then cross-evaluated the scenarios and guidelines following predetermined metric measures of (1) relevancy, (2) accuracy and reliability, (3) completeness as well as (4) clarity. Scores were generated by each of the five GenAI platforms based on the four metric measures and ranged from 1 = very low to 10 = very high. Then the scores were averaged across all measures and all five GenAI platforms for an overall metrics score ranging from 1 to 10. Analysis of variance was conducted to test for mean differences. Findings The experimental results indicated that there was a statistically significant mean differences in the scores received between all scenarios (F = 7.841, df = 4, p < 0.001). Specifically, the scenario generated by Claude achieved the highest overall average score (9.3), followed by Gemini (9.0), MetaAI (8.9), ChatGPT (8.7) and CoPilot (8.5). In general, the scenario generated by Claude consistently performed well across all metrics by all five GenAI platforms. Practical implications The rapid integration of GenAI capabilities into everyday activity may suggest that cybersecurity managers must currently be trained to use AI tools in their daily operations to alleviate their workloads. Having said that, some ethical issues and risks of using GenAI for cybersecurity managerial tasks must be further studied. Social implications The cybersecurity workforce shortage was reported to exceed 4 million workers worldwide in 2024 and is estimated to exceed 5 million by the end of 2025. Thus, it is significant to further understand the role of AI in improving the efficiency of managerial cybersecurity tasks. Originality/value The value of this research lies in three facets. First, the demonstration of using GenAI to perform managerial cybersecurity tasks. Second, the novel methodology, in which the GenAI platforms assess the outputs by self- and cross-evaluating them. Finally, the development of novel metrics to assess managerial tasks can be of great value for researchers and industry.

PurposeThe purpose of the study was to evaluate the impact of generative artificial intelligence (GenAI) on students' learning experiences and perceptions through a master’s-level course. The study specifically focused on student engagement, comfort with GenAI and ethical considerations.Design/methodology/approachThe study used an action research methodology employing qualitative data collection methods, including pre- and post-course surveys, reflective assignments, class discussions and a questionnaire. The AI-Ideas, Connections, Extensions (ICE) Framework, combining the ICE Model and AI paradigms, is used to assess students' cognitive engagement with GenAI.FindingsThe study revealed that incorporating GenAI in a master’s-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications. The AI-ICE Framework demonstrated most students were at the initial engagement level, with growing awareness of GenAI’s limitations and ethical issues. Course reflections highlighted themes of improved teaching strategies, personal growth and the practical challenges of integrating GenAI responsibly.Research limitations/implicationsThe small sample size poses challenges to the analytical power of the findings, potentially limiting the breadth and applicability of conclusions. This constraint may affect the generalizability of the results, as the participants may not fully represent the broader population of interest. The researchers are mindful of these limitations and suggest caution in interpreting the findings, acknowledging that they may offer more exploratory insights than definitive conclusions. Future research endeavors should aim to recruit a larger cohort to validate and expand upon the initial observations, ensuring a more robust understanding.Originality/valueThe study is original in its integration of GenAI into a master's-level instructional design course, assessing both the practical and ethical implications of its use in education. By utilizing the AI-ICE Framework to evaluate students' cognitive engagement and employing action research methodology, the study provides insights into how GenAI influences learning experiences and perceptions. This approach bridges the gap between theoretical understanding and the real-world application of GenAI, offering actionable strategies for its responsible use in educational settings.

Generative artificial intelligence (GenAI) tools have become increasingly accessible and have impacted school education in numerous ways. However, most of the discussions occur in higher education. In schools, teachers’ perspectives are crucial for making sense of innovative technologies. Accordingly, this qualitative study aims to investigate how GenAI changes our school education from the perspectives of teachers and leaders. It used four domains – learning, teaching, assessment, and administration – as the initial framework suggested in a systematic literature review study on AI in education. The participants were 88 school teachers and leaders of different backgrounds. They completed a survey and joined a focus group to share how ChatGPT and Midjounery had a GenAI effect on school education. Thematic analysis identified four main themes and 12 subthemes. The findings provide three suggestions for practices: know-it-all attitude, new prerequisite knowledge, interdisciplinary teaching, and three implications for policy: new assessment, AI education, and professional standards. They also further suggest six future research directions for GenAI in education.

Background: This research investigates the perceptions, benefits, and challenges of generative artificial intelligence (AI) tools among students of Technical and Vocational Education and Training (TVET). A sample of 200 students from various institutions in Malaysia including Technical and Vocational Education and Training (TVET) institutions and universities in the fields of engineering, information technology, business studies and hospitality were surveyed for this study. You were selected for this study due to your experience with generative AI in your academic and real-world learning experiences. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study addresses whether performance expectancy, effort expectancy, social influence and facilitating conditions are key factors in students' intentions to adopt and use generative AI. The findings demonstrate the importance of generative AI in improving TVET education but also spotlight challenges to its mainstream implementation. The study then draws recommendations for educators and policymakers, on how to ensure informed and effective AI use in TVET settings based on these findings.Objectives: The purpose of this research was to investigate the factors affecting the implementation of Generative AI among TVET students using UTAUT model, its advantages and disadvantages, as well as the role of institutional support and ethical concerns such as plagiarism and data privacy.Methods: Quantitative survey approach applied to collect comprehensive data on the adoption of generative AI among TVET students. A total of 200 students across multiple disciplines, including engineering, IT, and hospitality participated in this study. The participants were selected based on their exposure on generative AI tools to ensure relevance in assessing adoption factors. Data collection was conducted through a structured questionnaire based on the UTAUT model, covering constructs key such as performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey aimed to capture students’ perceptions, experiences, and challenges related to AI adoption in their fields of study. Additionally, regression technique was used to analysed the data and identify relationships between UTAUT constructs and adoption behaviour.Results: The findings of the study focus on the validation of the UTAUT constructs and the analysis of survey responses. The descriptive statistics (mean, standard deviation) and inferential statistics (correlation, regression analysis) were applied to understand the impact of various factors on AI adoption.Conclusions: This study highlights the potential of generative AI tools to change the landscape of TVET education. These tools can make the learning experience richer by improving the learning efficiency, enhancing creativity and improving problem-solving skills. Nevertheless, successful adoption is contingent on overcoming major obstacles, including technological literacy gaps, institutional support, and ethical considerations. The statistical analysis showed that performance expectancy and facilitating conditions were significant determinants of students' behavioral intention to adopt AI. It further stresses the importance of organized policies and training programs to promote responsible AI use. They can help facilitate an environment where generative AI can be maximized for TVET, ultimately ensuring that students are being equipped with essential digital skills necessary for a technology-driven future.

Aim: As generative artificial intelligence (GenAI) tools such as ChatGPT, Claude, and Gemini gain traction in higher education, their integration into assessment design raises pressing ethical questions.This research aimed to explore how pre-service teacher educators perceive the ethical implications of using GenAI in designing, administering, and evaluating student assessments.Specifically, it investigated issues around academic integrity, human judgment, bias, transparency, and institutional readiness. Methodology:The study adopted a qualitative design using semi-structured interviews with 18 pre-service teacher educators across diverse institutions.Data were analyzed thematically and triangulated with a review of recent global literature (2018-2024) on AI and ethics in education.Respondents were profiled in terms of their familiarity with GenAI, teaching roles, and institutional context.Results: Findings revealed a spectrum of views: while participants acknowledged the convenience and efficiency that GenAI brings to assessment design, many expressed concerns about overreliance, diminished authenticity, and potential threats to academic integrity.Ethical apprehensions included risks of plagiarism, algorithmic bias, lack of transparency, and erosion of human-centered judgment.Notably, participants stressed the importance of maintaining human oversight and advocated for institutional guidelines, professional training, and ethical awareness to guide AI use in teacher education. Conclusion:The study concludes that while GenAI presents transformative possibilities for assessment innovation, its ethical integration remains underdeveloped in many institutions.There is a critical need for teacher education programs to adopt human-centered approaches and develop explicit, values-based frameworks for AI use. Recommendations:The study recommends the institutionalization of ethical policies, provision of ongoing professional development, curricular integration of AI ethics, promotion of student integrity, and expanded research on local AI practices in education.These recommendations aim to ensure responsible, inclusive, and pedagogically sound adoption of GenAI in assessment systems.

Since the widespread release of generative artificial intelligence (GenAI) tools in recent years, there has been a dramatic impact on health professions education, particularly in the context of learner assessment. As GenAI continues to evolve, traditional paradigms in health professions education are changing, requiring students, administrators, and educators to navigate ongoing disruption to their practice. We examine how GenAI will specifically impact assessment practices, offering three key postulates to guide future teaching and learning: (1) educators must re-examine assessment constructs to align with GenAI-enhanced learning environments, (2) GenAI can outperform median learner performance with appropriate prompting, and (3) GenAI will become a required tool for health professions assessment. Ultimately, we believe that rather than viewing the advent of GenAI as a threat, educators should harness its potential to empower students and augment learning.

The uptake of generative artificial intelligence (GenAI) tools has implications for doctoral research and academic publication practices within both construction management and the wider academic context. Unless these implications are understood, GenAI tools have the potential to disrupt traditional relationships between doctoral researchers and their academic supervisors. Rather than exploring the technical competence and reach of GenAI tools, this study explores the nature of these challenges. GenAI is explored from both supervisor and doctoral perspectives for how its integration into doctoral research processes might shift relationships and affect practice. Informed by structuration theory, the research uses mixed methods to map shifts in agency and structure resulting from the adoption of GenAI tools. Findings highlight that the often-unacknowledged use of GenAI in doctoral research can confer undue agency on the technology that disrupts traditional relationships in an unacknowledged way. The rapid but often unacknowledged uptake of GenAI within doctoral research comes with a lack of consideration of the emotional support ascribed by students to the technology. It is concluded that GenAI tools should be openly incorporated into research and practice in a transparent, integrated approach. Practice relevance This research has relevance to the academic community both within the built environment disciplines and more general pedagogical implications. The identification of concerns over the reach and rapidity of GenAI adoption exposes potential changes to relationships and practices. Academics will be able to understand the shifts in relationships between stakeholders and the possible ramifications. The research exposes an unacknowledged proliferation of GenAI use in doctoral research and its underlying role in providing surrogate emotional support to doctoral students. By giving voice to stakeholders, this research exposes the lack of ethical frameworks around the use of GenAI and the need to consider its open and supported use, and its impact on developing the technical understandings and communication of doctoral researchers. The research uncovers some of the debates, concerns and possibilities that GenAI can bring to doctoral research practice, so that they can be intentionally addressed.

Background: Generative Artificial Intelligence (GAI) tools are increasingly used in research. At present, there is no standardised approach to reporting GAI use. We aimed to produce guidance to support authors in the use of GAI in scientific writing. Methods: A steering group of academic surgeons with experience in GAI developed draft statements for best practice in reporting GAI use. These statements were refined through iterative discussions using a nominal group technique. A broad network of surgeons and surgical researchers were invited to participate in an online consultation exercise to validate these statements by ranking using a Likert scale. A pre-planned threshold of ≥70% of participants scoring a statement ≥7 would lead to acceptance. Participants were additionally surveyed on the use, opportunities, and risks. Thematic analysis was completed using ChatGPT. Results: The steering group developed five draft statements, which were validated in the online consultation exercise by 124 participants from 46 countries. Four draft statements were accepted based on this exercise and consolidated into the final Generative AI Transparency (GAIT) guidance: (1) GAI use should be reported in a GAIT statement; (2) GAI use should be mapped using the Contributor Roles Taxonomy; (3) specific prompts used should be reported; (4) authors should retain final responsibility for their work. Example statements to be included in manuscripts include: (1) ChatGPT-4o was used in November 2024 to check and edit statistical code (formal analysis) and edit small sections of the manuscript text for clarity (writing: review &amp; editing). Prompts used are reported in the supplement. The authors should retain final responsibility for their work; (2) No Generative Artificial Intelligence was used to produce, draft, or edit this guidance paper. Conclusion: The GAIT 2024 guidance will support transparent, structured reporting of the use of generative AI in scientific writing, supporting the integrity of research outputs.

&lt;div align="center"&gt;&lt;table width="100%" border="0" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="605"&gt;&lt;p&gt;Integrating artificial intelligence (AI) in higher education, particularly in coding assignments for Information Technology (IT) students, represents a rapidly evolving research area with significant implications for academic practices and integrity. This study focuses on the ethical challenges faced by IT students when using AI tools like ChatGPT for coding assignments. Despite the growing use of AI in education, there is a notable gap in understanding how students perceive and navigate the ethical dilemmas associated with these technologies. To address this gap, this study employed a thematic analysis of qualitative data collected from interviews with IT students. The results reveal a complex landscape of ethical considerations, including issues of originality, academic integrity, and the potential for misuse of AI tools. Students reported challenges in balancing the benefits of AI assistance with the need to maintain independent learning and adhere to ethical standards. The implications of this research are significant for educators, institutions, and policymakers. Understanding the ethical challenges students face can inform the development of more effective teaching strategies, assessment methods, and institutional policies. This study contributes to the ongoing dialogue about AI ethics in academia, providing valuable insights for creating an educational environment that leverages the power of AI while upholding the principles of academic integrity and meaningful learning.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;