Generative AI as a learning assistant in ICT education: student perspectives and educational implications

The rapid development of generative AI (GenAI) technologies in recent years has enabled new opportunities as well as new challenges in higher education. While many studies in computer science have focused on GenAI in programming education, fewer have examined its possibilities and challenges in requirements engineering (RE). This study aims to explore the impact of GenAI on the pedagogical aspects of RE in higher education, focusing on the student perspective, to analyse how GenAI might influence learning experiences, knowledge acquisition, and skill development. The main research question to answer was: "What are the students’ perspectives of the integration of GenAI in the educational practices of requirements engineering?" An Action research strategy was employed, with one of the authors also serving as teacher in the investigated course. A mixed-methods approach was used to collect both qualitative and quantitative data from workshops and surveys. During the workshops, students used ChatGPT to generate and evaluate software requirements and compared these to manually crafted requirements. Thematic analysis of the qualitative data captured students’ perspectives, while survey data identified trends and preferences. Findings show that while students generally had a positive experience with GenAI, valuing its efficiency and the quality of generated requirements, they also recognized the need for human oversight to maintain accuracy. The study highlights both opportunities and challenges of using GenAI in RE education. While GenAI increased learning engagement and helped with brainstorming, students faced difficulties in creating effective prompts and found it time-consuming to refine AI-generated requirements. A hybrid approach, combining AI-generated and manually created requirements, proved most effective by balancing AI's advantages with human insights. Further research is needed on how GenAI could be effectively integrated into computer science education.

Research shows that feedback practices significantly impact key student outcomes, including performance, engagement, and satisfaction (Esmaeeli, Shandiz, Shojaei, Fazli & Ahmadi 2023). Feedback is a crucial component of learning in Higher Education (HE) and plays a vital role in developing critical thinking, improving retention, and enhancing student engagement. The importance of timely dialogic feedback in enhancing student engagement and potentially improving retention is well understood (Advance HE 2020). However, academic staff are increasingly time-poor, with reduced opportunities to provide regular in-depth quality feedback outside of that given for summative assessment (Henderson, Ryan & Phillips 2019). Early experimentations with using Generative AI (GenAI) such as ChatGPT to provide feedback for formative assessment recognises that students will learn and work in an AI-enabled world beyond their university studies (Bowditch 2023). GenAI can be leveraged inside and outside the classroom to achieve positive student engagement and improved skill development thereby affording them the skills and knowledge necessary to succeed (Hooda et al. 2022). Engaging with GenAI for feedback purposes offers an opportunity to increase equitable access to feedback across the student cohort, to support and further develop their critical skills and learning outcomes. As Verhoeven and Rana (2023) note, “AI disruption may present an opportunity to shift the focus from assessment of learning to assessment for learning”. Utilising GenAI for feedback purposes can provide rapid, personalised learning support, and aid with planning, drafting, and revising student work. However, this adoption of GenAI for feedback must be driven and developed by the educator, keeping the human in the loop to ensure quality (Atchley, Pannell, Wofford, Hopkins & Atchley). Our project draws on the principles of feedback literacy, current research on using AI as learning tool (Verhoeven & Rana 2023b; Tubino & Adachi 2022) and emphasises student-centred learning through dialogic feedback practices. The project draws on scholarship from Mollick and Mollick’s seven approaches to student use of AI (2023), Perkins, Furze, Roe and MacVaugh’s framework for ethical integration of AI in assessment (2024), and emerging work from Liu, Brightman, and Miller on GenAI and feedback (2023). This presentation addresses the conference theme of Technology, providing an overview and reflection on staff development and adoption of GenAI for feedback processes for the benefit of student learning. We will showcase four use cases of the use of GenAI to design and implement feedback creation for undergraduate formative assessment across the three Colleges at the University of Newcastle. All cases engage innovation in Technology Enhanced Learning (TEL) practice in developing GenAI tools to support student learning via feedback. The presentation addresses the benefits and challenges of each approach. Recognising the value of feedback in student learning, this PechaKucha is aimed at a diverse audience in HE. Our presentation will demonstrate applicability and adaptability to a range of disciplines as we explore the impact new and emerging GenAI technologies can have on HE. We will introduce the possibilities of using GenAI for feedback purposes, and encourage staff to consider experimenting with and adopting their own innovative TEL practices.

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.

Generative AI (GAI) is reshaping the future of work in architecture by introducing innovative ways for humans to interact with technology, transforming the design process. In education, GAI offers students immersive environments for iterative exploration, enabling them to visualize, refine, and present design concepts more effectively. This paper investigates how GAI, through a structured framework, can enhance the learning of design tasks in elaborating interior design proposals, and preparing students for the evolving professional landscape. Drawing on the platform Midjourney, students explored concepts, material moodboards, and spatial compositions, simulating professional scenarios. Each student was assigned a real client and tasked with developing tailored design solutions, guided by client and tutor feedback. This approach demonstrates how GAI supports the development of future-oriented skills, directly linking education to the technological shifts in professional practice (Araya, 2019). The study adopts a practice-based methodology, documenting the outcomes of an interior design workshop where students employed GAI tools to develop client-specific proposals. Students engaged in role-playing, meeting their assigned clients face-to-face to gather requirements, acting as junior architects. They analyzed client feedback to inform the design phase, after which they used a structured framework for better using GAI to iteratively refine their proposals. By generating AI-assisted visualizations of spatial configurations and materials, students developed final design solutions that aligned with client expectations. Data from GAI iterations, client feedback, and tutor evaluations were used to assess how effectively AI tools contributed to producing professional-quality designs (Schwartz et al., 2022). Two research questions frame this investigation: (1) How does Generative AI enhance students' ability to create client-specific interior design solutions, from concept generation to final visualization, within a structured educational framework? (2) How does the integration of GAI tools impact the teaching of iterative design processes in architecture, particularly in preparing students for the future of work in the profession? The findings reveal that GAI significantly improved students' design outcomes by enabling them to visualize and refine their proposals based on real-world scenarios. GAI facilitated the exploration of current trends and supported the creation of material moodboards and space visualizations. The iterative nature of AI tools allowed students to better grasp the relationships between spatial configurations, design choices, and client needs. Their final proposals, incorporating AI-generated outputs, were praised for their conceptual clarity and technical precision, reflecting how AI-driven processes can transform traditional workflows (Burry, 2016). This study illustrates the transformative potential of GAI in architectural education, particularly in fostering dynamic human-technology interactions. By leveraging AI, students maintained control over outputs while transforming abstract concepts into client-ready designs. Moreover, the iterative feedback loop enabled by GAI promoted a more adaptive and responsive learning process, giving students real-time insights into their design decisions. These insights reflect broader changes in the future of work, where AI-driven tools will become integral to professional practice. Future research could explore expanding GAI’s role in more complex design stages, such as schematic design and development, building on the benefits observed in this study.

Operator Training Simulators (OTS) are commonplace in the chemical engineering industry but often underutilized in universities (Patle et al., 2019). Like a ‘flight simulator’ for engineers, they are ‘digital twins’ of real plants, that can run many safety scenarios. In tertiary education OTS offer scalable, active learning environments and authentic assessment, particularly when integrated with Generative AI (GenAI). Our pedagogical design is scaffolded through UTAUT2 (Unified Theory of Acceptance and Use of Technology), offering immersive, industry-aligned, practice-based engineering educational design (Honig et al., 2025), something that is often difficult to do in conventional classroom teaching (Honig et al., 2024). Within the ASE core themes, this presentation on OTS integration will focus on technology-enhanced learning and authentic assessment. It will draw on learnings from the integration of GenAI into the OTS software: while most people think of interacting with GenAI through text-interfaces (like ChatGPT) here students can interact through the game-interface itself (for example opening a valve or if an alarm trips, the GPT ‘knows’ and can automatically respond). In response to remote learning challenges presented during COVID-19 lockdowns (Honig et al., 2022), a modified industry-grade OTS (TSC Simulation) was embedded into undergraduate subjects. The simulator, originally designed for professional operator training, was adapted to educational needs by including assessment-focused scenarios and then augmented with a GPT-powered AI teaching assistant. Over four years, it has been used in both second- and third-year core Chemical Engineering subjects, providing students with a unique opportunity to interact with digital twins, analyze process safety incidents, and apply critical thinking in real-time problem-solving. Using a Design-Based Research framework, the initiative evolved through iterative cycles of student use, feedback, and redesign. Mixed methods evaluation involved pre- and post-use surveys grounded in the UTAUT framework, performance data from quizzes and assignments, and qualitative student feedback. The integration of GenAI was evaluated for usability, performance expectancy, and impact on learning outcomes. Students’ comprehension of safety concepts was compared across user groups—with and without chatbot access—using assessments and reflective discussions. Across cohorts, the OTS was rated highly for its realism and performance benefits, with a Likert average of 4.32 (out of 5) on performance expectancy. The GenAI chatbot, acting as a plant supervisor, facilitated guided root-cause analyses and reflection. Within a limited sample size, students with access to the AI assistant indicated higher quiz performance (67%) than those without (59%). However, effort expectancy for the OTS rated lower, highlighting the complexity of adapting industry-grade software to educational contexts. Improvements were made by redesigning activities to fall within students’ Zones of Proximal Development, particularly when supported with new GPT-based adaptive learning assistants, utilizing an agent structure. This initiative offers a replicable model for incorporating industry technologies and GenAI into curriculum-aligned, scalable assessment formats. It demonstrates how immersive learning tools can address gaps in traditional practicals, support student autonomy, and align with ASE’s call for flexible, digitally enhanced, and inclusive educational experiences. We will share initial learnings. Significant broader outcomes have also emerged from the work: as well integrating GPTs into simulators as AI-assistants for education, GPTs can similarly be integrated into real plants as AI-engineers for process control. The presentation will outline opportunities for GenAI integration into tertiary education, with a specific focus on integration into simulation based learning itself (as opposed to interaction through a chat interface). The presentation will have an interactive component allowing participants to build a customized chatbot through a purpose built interface for the conference presentation. References Honig, C., Rios, S., & Desu, A. (2025). Generative AI in engineering education: understanding acceptance and use of new GPT teaching tools within a UTAUT framework. Australasian Journal of Engineering Education, 1-13. Honig, C. D., Desu, A., & Franklin, J. (2024). GenAI in the classroom: Customized GPT roleplay for process safety education. Education for Chemical Engineers, 49, 55-66. Honig, C. D., Sutton, C. C., & Bacal, D. M. (2022). Off-campus but hands-on: Mail out practicals with synchronous online activities during COVID-19. Education for Chemical Engineers, 39, 84-93. Patle, D. S., Manca, D., Nazir, S., & Sharma, S. (2019). Operator training simulators in virtual reality environment for process operators: a review. Virtual Reality, 23, 293-311.

The study examines the transformative potential impact of Generative AI (GAI) on society, media, and media education, focusing on the challenges and opportunities these advancements bring. GAI technologies, particularly large language models (LLMs) like GPT-4, are revolutionizing content creation, platforms, and interaction within the media landscape. This radical shift is generating both innovative educational methodologies and challenges in maintaining academic integrity and the quality of learning. The study aims to provide a comprehensive understanding of how GAI impacts media education by reshaping the content and traditional practices of media-related higher education. The research delves into three main questions: the nature of GAI as an innovation, its effect on media research and knowledge acquisition, and its implications for media education. It introduces critical concepts such as radical uncertainty, which refers to the unpredictable outcomes and impacts of GAI, making traditional forecasting and planning challenging. The paper utilizes McLuhan’s tetrad to analyze GAI’s role in media, questioning what it enhances or obsoletes, retrieves, or reverses when pushed to extremes. This theoretical approach helps in understanding the multifaceted influence of GAI on media practices and education. Overall, the research underscores the dual-edged nature of GAI in media education, where it presents significant enhancements in learning and content creation while simultaneously posing risks related to misinformation, academic integrity, and the dilution of human-centered educational practices. The study calls for a balanced approach to integrating GAI in media education, advocating for preparedness against its potential drawbacks while leveraging its capabilities to revolutionize educational paradigms.

This study considers the potential effects of Generative AI on the skills development of ‎professionals in the social media advertising and marketing industry (particularly around ‎creativity and adaptability). More specifically, Generative AI technologies are rapidly disrupting ‎social media platforms and advertising practices; therefore, the need to understand their impact ‎on the professional skills development landscape is essential. To that end, this study employed ‎both qualitative and empirical lenses to identify how advertising professionals respond to ‎Generative AI's increasing presence and the adaptation of their skill development from that ‎recognized role. This research provides significant evidence of the growing skills with social media advertising ‎bequeathed by Generative AI, interconnecting workplace learning theories, industry strategies ‎, and barriers in innovation and the digital realm. It gives practical advice to organizations about ‎basic competencies necessary to develop and the repercussions of relying on generative tools in ‎order to remain competitive. The research identifies Generative AI not only as a strategic ‎investment for the organization, enhancing marketing efficacy and overall performance, but also ‎enhancing innovation and learning. This proves its financial worth with tangible proof of lower ‎campaign costs, faster content production, and greater conversion resulting from organizations ‎putting AI-enhanced tools into operation. In demonstrating that the evolving Generative AI ‎capabilities are indicators of an increase in productivity, measures such as increased marketing return ‎on investment will place inordinate importance on building GenAI skills‎.

The rapid development of generative AI (GenAI) raises new questions in higher education such as: What should be the university policy regarding GenAI? How ought courses be redesigned for fair and resilient assessment? What the added pedagogical and didactical values when involving GenAI in teaching and learning activities? Different universities have rapidly created and presented contradictory standpoints and draft policies, and teachers show different opinions regarding the pros and cons of GenAI. This study has been carried out with a student perspective, where 16 students have been examining their own Master's programme on sustainable information provision. Students have assessed the assessment in their previous courses in the Master's programme. The aim of the study is to investigate how sustainable course activities and assignment are, and to explore how GenAI tools might support and facilitate teaching and learning activities. Moreover, the students were given the task to test detection software on GenAI generated solutions to assignments in chosen Master's courses. Students conducted these tasks as a part of a 7.5 ECTS project course in the same Master's programme as the investigated courses are a part of. For inspiration and for background information on artificial intelligence to the project work students participated in the first Symposium on AI Opportunities and Challenges (SAIOC) in December 2023. Data have been gathered from reports of 3 group projects where 16 students have investigated 5 freely chosen courses in the programme in each group work. Beside from testing GenAI tools in existing activities and assignments students also interviewed the subject matter experts that are responsible for the chosen courses. Results were firstly analysed and presented in group reports, combined with 16 individual reflection essays. Regarding the individual essays students were instructed to bring up ethical perspectives on GenAI in higher education, and also to present and discuss suggestions for how the current course design and assignments better could be redesigned for improved sustainability and fairness. Finally, all the group reports and the individual reflection essays were thematically analysed by the author, who also is the subject matter expert and main teacher for the project course. Findings show that many of the existing assignments in the Master's programme could be partly solved with different GenAI tools. The AI generated solutions showed different levels of quality and correctness for different types of activities and assignments. An ethical concern that many student essays brought up was the relatively poor quality of the tested detection software. A question in one of the essays was if teachers should use detection software with an accuracy rate just above 50% to evaluate student submissions. The recommendations from both the students and the author are to provide clear instructions about when GenAI is allowed and not in course activities, and to redesign the course structure for continuous assessment. With or without GenAI tools, a continuous assessment where the whole study path through a course is assessed, and not only isolated submissions, would strengthen fairness and sustainability. Finally, several students suggest oral examinations as a complement to the existing assessment methods, even if their findings showed that GenAI tools can be used to prepare oral presentations.

In the current spring of Artificial Intelligence, the rapid development of Generative AI (GenAI) has initiated vivid discussions in higher education. Opportunities as well as challenges have been identified and to cope with this new situation there is a need for a large-scale teacher professional development. With basic skills about GenAI teachers could use the new technology as an extension of the existing technology enhanced teaching and learning. The aim of this paper is to present and discuss the project FAITH (Frontline Application of AI and Technology-enhanced Learning for Transforming Higher Education). FAITH is a higher education pedagogical development initiative for institutional development for teachers with good fundamental skills in traditional pedagogy. A project with the overall objective of increasing the staff understanding of AI and to develop new competencies in the field of GenAI and technology enhanced learning. The research question that guided this study was: "What are the perceived opportunities, challenges and expectations of involving GenAI in higher education?" The overall research strategy for the FAITH project is design-based research, which involves iterative and cumulative development processes. In the early iteration that this study was a part of has been carried out inspired by Collective Autoethnography where members of the steering group behind the FAITH project, and members of the project team have constituted the main focus group. Data were collected by structured interviews where two GenAI tools also have been interviewed. Findings show that the expectations are high, but that the FAITH ambition of institutional development is depending on teachers’ motivation for taking an active part in the project. Another challenge could be that many teachers see GenAI as something that threatens the current course design, and that a general ban of GenAI is the appropriate solution. One of, several identified opportunities, is that a general revision of syllabi and assessment in an adaptation for GenAI enhanced learning would improve the current course design.

Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.

The rapid adoption of generative AI (GenAI) in education has raised critical questions about its implications for learning and teaching. While GenAI tools offer new avenues for personalized learning, enhanced feedback, and increased efficiency, they also present challenges related to cognitive engagement, student agency, and ethical considerations. Learning analytics (LA) provides a crucial lens to examine how GenAI affects learning behaviours and outcomes by offering data-informed insights into GenAI’s impact on students, educators, and educational ecosystems. Thus, obtained insights allow for evidence-based decision-making aimed at balancing GenAI’s benefits with the need to foster deep learning, creativity, and self-regulation of learning. This special issue of the Journal of Learning Analytics presents 10 research papers that explore the intersection of GenAI and LA, offering diverse perspectives that benefit students, teachers, and researchers. To structure these contributions, we adopt Clow’s generic framework of the LA cycle, categorizing the papers into four key areas: (1) understanding learning and learner contexts, (2) leveraging AI-generated data for learning insights, (3) applying LA methods to generate meaningful insights, and (4) designing interventions that optimize learning outcomes. By bringing together these perspectives, this special issue advances research-informed educational practices that ensure that GenAI’s potential is harnessed responsibly, reinforcing educational goals while safeguarding learners’ autonomy and cognitive development. Collectively, these contributions illustrate the reciprocal relationship between GenAI and LA, demonstrating how each can inform and refine the other. We reflect on the broader implications for LA, including the need to re-examine the boundaries of LA in the presence of GenAI, while preserving key principles from human-centred design and maintaining ethical and privacy standards that are foundational to LA.

Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.

This research aimed to study the impact of generative AI technology, specifically ChatGPT, on the development of digital entrepreneurship skills among university students. It explores how AI technologies can be integrated into higher education to develop core digital entrepreneurship skills, including innovation, creative thinking, and entrepreneurial initiative. A quasi-experimental design was employed, involving two groups of graduate students from the College of Education at King Khalid University. The experimental group was exposed to generative AI platforms (ChatGPT), while the control group was trained using traditional educational methods. To measure the impact, assessment tools, such as the Digital Entrepreneurship Scale and the Entrepreneurial Product Scorecard, were used before and after the intervention. The results indicated significant positive differences in favor of the group that used ChatGPT, demonstrating improved digital entrepreneurship skills and the ability to create pioneering digital products. Generative AI technologies provided a conducive environment for students to systematically organize their ideas, generate innovative solutions, and effectively analyze data. This research demonstrates that the integration of generative AI platforms, such as ChatGPT, into university curricula has a positive impact on the development of digital entrepreneurship skills. These technologies also enhance students' entrepreneurial mindset and innovative capabilities, helping to bridge the gap between academic learning and the practical requirements of the digital job market. Educational institutions should therefore integrate generative AI into their curricula and provide comprehensive training for faculty and students to maximize the benefits of these technologies. In doing so, universities can better prepare students for dynamic professional environments and foster sustainable and innovative digital entrepreneurship initiatives.

This study examined pre-service teachers' perspectives on integrating generative AI (GenAI) tools into their own learning and teaching practices. Discussion posts from asynchronous online courses on ChatGPT were analyzed using the Diffusion of Innovations framework to explore familiarity, willingness to apply ChatGPT to instruction, potential benefits, challenges, and concerns about using GenAI in teaching and learning. The course discussions significantly increased pre-service teachers' awareness and foundational knowledge while reducing anxiety towards AI technologies. However, despite exposure to ChatGPT, only a few confirmed intentions to adopt AI tools in their teaching practices, potentially reflecting lingering uncertainties evidenced by emotional responses, such as worry and concern. Professional development in AI literacy can address these uncertainties and enhance GenAI familiarity. The study offers insights into responsible GenAI adoption in education and how higher education can leverage ChatGPT to enhance pre-service teacher learning.

The purpose of this study is to analyze the experiences and educational needs of foreign undergraduate students enrolled in Korean universities using Generative AI and to find ways to effectively utilize Generative AI in the writing process. To this end, a survey was conducted on 219 foreign undergraduate students who took Liberal Arts <College writing> courses at A University. As a result of the analysis, 39.7% of foreign undergraduate students who participated in this survey answered that they had used Generative AI when performing assignments at university. Respondents mainly used Generative AI for outlines, summaries, solving exercises, and writing general reports, and used Generative AI to better understand the content, to generate ideas, to translate, and to revise their expressions. And as a result of analyzing their educational needs, we found that foreign undergraduate students need writing, citation, and writing ethics education when using Generative AI, even if they are aware of citation methods and problems when using Generative AI in the process of performing university assignments. Based on these results, this study suggested educational implications for writing when using Generative AI in writing subjects. It is necessary for us to teach ciation methods and writing ethics when using Generative AI. Also, it is necessary for us to teach writing students using Generative AI the types of writing that take into account the majors of foreign undergraduate students or the types of writing that learners write with frequently. How to use Generative AI in writing classes can be taught to foreign undergraduate students, especially in the writing revision stage.