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22 SES 13 C: AI impacts on learning
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22. Research in Higher Education
Ignite Talk Agency in Practice: How Chinese International Students Make Judgements About Generative AI Use in Academic Tasks University of Glasgow, United Kingdom Presenting Author:Generative AI is now commonly used by students in UK higher education. Evidence from a national student survey suggests that reported AI use increased within a short period of time, with many respondents indicating that they used AI to support learning activities as well as assessed coursework (Freeman, 2025). This level of uptake matters because it complicates the assumption that AI use occurs only in marginal or exceptional situations. Policy responses have developed quickly, but they have not yet resulted in a consistent shared approach. Within the UK sector, the Quality Assurance Agency has published guidance and resources that encourage institutions to safeguard academic standards while responding to generative AI (The Quality Assurance Agency [QAA], 2024). However, a global analysis of academic integrity policies indicates that references to AI-related risks vary considerably across institutions and often follow, rather than anticipate, technological developments (Perkin and Roe, 2024). As a result, a gap can emerge between the formal language of policy and how academic practices are experienced in everyday study. Much of the evidence from the perspective of students concentrates on attitudes and perceptions rather than on decision-making in context. Some studies show that students often regard generative AI as helpful, while also expressing concerns about reliability, ethical implications, and learning outcomes (Chan and Hu, 2023). While this work provides important insight into general views, it offers a limited understanding of how students respond when they encounter concrete academic tasks that require judgment about acceptable use. This uncertainty may be particularly pronounced for international students. Research on academic literacies suggests that expectations in higher education are frequently implicit, contested, and shaped by disciplinary norms rather than by a single transferable set of skills (Lea and Street, 1998). For students who move across educational systems, uncertainty should not be interpreted simply as an individual shortcoming. Instead, it reflects how academic conventions are communicated and evaluated. In this context, generative AI can function both as a form of assistance and as a potential source of risk. The same use of AI may be interpreted as legitimate learning support in one course and as inappropriate practice in another. This study focuses on Chinese international students in UK higher education and asks how students exercise agency when deciding whether, when, and how to use generative AI in academic tasks. Agency is treated here as something enacted through practice, rather than as a fixed characteristic that students possess to varying degrees. This perspective draws on research into generative AI tools supported learning, which suggests that digital tools can both support and constrain agency depending on how they are taken up in practice (Darvishi et al., 2024). It is also informed by recent reviews of the emerging literature on generative AI and agency, which point to ongoing tensions around control, access, and changing understandings of what agency entails (Roe and Perkins, 2024). The theoretical framework brings together academic literacies with sociomaterial and posthuman perspectives. Sociomaterial research emphasises that digital literacy practices emerge through relationships among learners, technologies, texts, and institutional contexts, rather than through individual capability alone (Gourlay and Oliver, 2013). Similarly, posthuman scholarship in digital education questions accounts of learning and authorship that locate responsibility solely within the individual, especially in digitally mediated environments (Bayne, 2016). While these perspectives offer valuable insights, they can remain abstract if they are not connected to concrete academic activity. For this reason, the study pays particular attention to moments during academic work when students pause, reconsider, accept, or reject AI input, as these moments offer a way to examine how agency and academic integrity are negotiated in practice. Methodology, Methods, Research Instruments or Sources Used The study adopts a design that links broad patterns of use with observed practices and participants’ own interpretations. The first stage consists of an online survey that explores how students use generative AI across a range of common academic tasks. It also gathers information about students’ perceptions of institutional guidance and their own understandings of acceptable boundaries. This stage provides an overview of patterns and variation, but it does not capture how decisions unfold during actual study activity. The second stage involves a writing task completed with screen recording. Participants carry out a short academic writing activity using their usual digital tools and practices, with the option to consult generative AI if they wish. This approach is adopted because reported perceptions tend to overlook hesitation and uncertainty. Research on writing processes suggests that screen recording can make visible aspects of decision making, such as pauses, revisions, and attention shifts that writers may not recall in retrospect (Chan, 2017). The resulting data are treated as evidence of practical reasoning rather than as an evaluation of writing ability. The final stage consists of interviews structured around specific moments from the recorded writing task. Participants are invited to reflect on selected episodes and explain what they were trying to achieve and how they understood the limits of acceptable AI use. Anchoring the discussion in concrete activity helps to move beyond general statements and allows closer examination of how institutional messages are interpreted in practice. It also enables exploration of how students relate AI use to learning aims, language support needs, and their perceptions of fairness. The study draws on academic literacies theory to situate integrity as negotiated rather than fixed categories. The design aims to connect students’ reported concerns with observed judgment practices and interpretations of institutional ambiguity. Conclusions, Expected Outcomes or Findings As data collection is ongoing, this study does not claim to present findings at this stage. The discussion offered here is exploratory in nature and draws only on a small amount of preliminary material alongside relevant literature. It is intended to outline possible analytical directions rather than to support conclusions about students’ practices or decision-making. First, the study is expected to indicate that students’ judgements about the use of generative AI differ across tasks and depend on how clearly expectations are communicated. Although reported levels of use are relatively high, this does not appear to correspond to widely shared understandings of what counts as acceptable practice. Instead, existing research suggests that students often make judgments in response to specific situations rather than relying on consistent or clearly defined rules (Freeman et al., 2025). Second, the study is expected to suggest that uncertainty in institutional guidance plays a role in shaping students’ decisions. Where policies differ across modules or provide limited detail, students may turn to peer practices, previous experiences, or personal interpretations of academic integrity. This can result in a range of approaches to AI use rather than a single common pattern. Third, the research seeks to develop a more detailed understanding of how agency is expressed during academic work that involves AI tools. Instead of treating agency as a fixed personal quality, the study focuses on how decisions take shape through the interaction of tools, task requirements, institutional expectations, and students’ own goals. This view is consistent with academic literacies research, which understands writing and judgement as practices that are socially situated and shaped by context. These insights may contribute to ongoing discussions about generative AI, academic integrity, and student agency in higher education. They may also offer practical value for institutions seeking to communicate guidance more clearly. References Bayne, S. (2016). Posthumanism and research in digital education. SAGE Handbook of E-learning Research, 82-100. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. Chan, S. (2017). Using keystroke logging to understand writers’ processes on a reading-into-writing test. Language Testing in Asia, 7(1), 10. Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 104967. Freeman, J. (2025). Student generative ai survey 2025. Higher Education Policy Institute: London, UK. Gourlay, L., & Oliver, M. (2013). Beyond ‘the social': digital literacies as sociomaterial practice. In Literacy in the Digital University (pp. 79-94). Routledge. Lea, M. R., & Street, B. V. (1998). Student writing in higher education: An academic literacies approach. Studies in higher education, 23(2), 157-172. Perkins, M., & Roe, J. (2024). Decoding academic integrity policies: A corpus linguistics investigation of AI and other technological threats. Higher Education Policy, 37(3), 633-653. Roe, J., & Perkins, M. (2024). Generative AI and agency in Education: A critical scoping review and thematic analysis. arXiv preprint arXiv:2411.00631. The Quality Assurance Agency [QAA]. (2024). Advice and resources on Generative AI. https://www.qaa.ac.uk/sector-resources/generative-artificial-intelligence/qaa-advice-and-resources 22. Research in Higher Education
Paper Lost in Translation? The Impact of AI Translation Tools on International Postgraduate Learning in English-Medium Instruction University of Glasgow, United Kingdom Presenting Author:Context The research is prompted by a classroom observation during a seminar in which several students were wearing AI-enabled smart glasses to translate spoken English into written text in their language real time. These devices record lectures, translate them and upload data to cloud-based systems, over which lecturers have no control, raising concerns about data ownership, consent, and academic governance. This moment prompted critical reflection on how emerging translation technologies are transforming international postgraduate learning environments. The study is situated within a postgraduate programme at a Scottish university in which over 98% of students are international. In this highly multilingual context, the increasing availability of AI has led to growing student reliance on translation tools to support lecture comprehension, seminar participation, and academic writing. Classroom observations indicate a significant shift in how linguistic accessibility is mediated, with AI technologies increasingly functioning as intermediaries between students and lecturers. Problem Although AI translation technologies may enhance access and inclusivity by lowering linguistic barriers (Fitas, 2025), their educational implications remain underexamined. Existing research suggests that reliance on translation tools may limit opportunities for language development and authentic interaction (Zhang, Li & Wu, 2025; Rapa et al., 2024), and inaccuracies in translation can result in partial or distorted comprehension (Altakhaineh et al., 2025). Moreover, the use of AI translation tools raises ethical concerns related to data privacy, algorithmic bias, and institutional governance (Regan & Jesse, 2019). In particular, the recording and cloud storage of lecturers’ speech may expose sensitive academic content and personal data to potential misuse. These issues are compounded by the fact that institutional AI policies tend to prioritise generative AI, often overlooking translation technologies, leaving gaps in guidance for both staff and students. Aims and research questions This project investigates the use of AI translation tools by international students in live EMI teaching sessions in a postgraduate programme at a Scottish university. The study aims to examine how these tools are used in practice and analyse their impact on students’ comprehension, engagement, and academic confidence. Specifically, the study seeks to explore students’ motivations for using these tools during lectures and seminars, the ways in which they are integrated into live learning activities, and students’ perceptions of their benefits and limitations. By systematically analysing cloud storage practices underpinning commonly used AI tools and their pedagogical effects, the project aims to deliver evidence-based guidance for institutions seeking to implement responsible and inclusive AI in multilingual higher education contexts. The study addresses the following research questions: 1. What are the primary motivations behind international students’ use of real-time AI translation during live lectures and seminar/tutorial discussions? 2. How do they use these tools and what specific tools do they use? 3. What benefits and challenges do students experience when using AI translation tools in EMI contexts? Preliminary analysis of existing literature indicates that, although a growing body of research has examined the role of machine translation in educational settings, limited attention has been paid to the use of AI-based translation tools within English-medium instruction (EMI) contexts. There is a lack of focused empirical and conceptual work examining how AI translation tools affect academic integrity and mediate the linguistic demands experienced by both students and instructors in EMI environments. This gap underscores the need for targeted research to clarify the pedagogical, ethical, and linguistic implications of AI translation in these settings. Methodology, Methods, Research Instruments or Sources Used This study employs a mixed-methods research design informed by a pragmatic paradigm. The study is exploratory prioritising enhancement of pedagogical practice while contributing to broader educational debates (Fanghanel et al., 2016). Technology Acceptance Model (TAM) (Davis, 1989) provides the theoretical framework, offering a well-established lens through which to analyse students’ adoption of AI translation tools, particularly in relation to perceived usefulness and ease of use. Data collection included an online survey distributed to 200 international postgraduate students, who speak English as a foreign language, enrolled in an education programme;168 students completed the survey. In-depth semi-structured interviews were conducted with 12 participants selected from survey respondents. Recruitment took place via email and course announcements, with participation voluntary and withdrawal permitted at any stage prior to data analysis. To ensure diversity, participants from a range of linguistic and cultural backgrounds who speak English as a foreign language were contacted. Interview participants were selected through purposive sampling, targeting students who reported active use of AI translation tools in academic contexts. Semi-structured interviews, approximately 30-45 minutes in duration, were conducted either online or in person according to participant preference. Data collection began with an anonymous online survey designed to capture types of AI translation tools used, frequency and contexts of use, and self-reported benefits and challenges. Survey invited interested participants to contact the researchers if they wished to be interviewed. Semi-structured interviews with 12 participants were subsequently used to explore motivations for tool use, impacts on classroom participation, and ethical considerations in greater depth. Quantitative survey data were analysed descriptively using Qualtrics to identify trends and patterns. Qualitative interview data are being analysed thematically following Braun and Clarke’s (2006) six-phase framework. Emergent themes will be compared with existing literature to situate findings within broader pedagogical and technological debates. Ethical approval for the project was granted by the School's ethics committee. The study adheres to established ethical principles, including informed consent, confidentiality, voluntary participation, and secure data management in compliance with GDPR and institutional policy. Participants received comprehensive information sheets and were provided informed consent prior to participation. Conclusions, Expected Outcomes or Findings The findings indicate that students’ use of AI-enabled translation tools is shaped not only by practical learning needs but also by affective and social considerations. Many participants reported downloading AI tools prior to moving to the UK after encountering discussions on local social media that characterised the Scottish accent as particularly challenging to understand. Many shared feeling self-conscious when using translation tools in class, expressing concern that their peers or lecturers might judge them as less capable. This sense of stigma was particularly pronounced in interactive classroom settings, where students felt that visible reliance on translation tools could undermine perceptions of their academic competence or language proficiency. As a result, some students described attempting to conceal their use of such tools despite recognising the potential benefits of real-time translation. A recurring theme in the data was uncertainty surrounding institutional policy. Students frequently expressed confusion about whether the use of translation tools was formally permitted during teaching sessions or in the completion of assessed work. In the absence of clear guidance, many adopted pragmatic strategies, relying on a combination of AI-powered translation, grammar-checking, and paraphrasing tools to support comprehension and note-taking. Several students also reflected on the longer-term effects of sustained translation tool use on their writing development. Some observed that their academic writing had begun to mirror the structure, tone, or phrasing commonly associated with AI-generated text, raising concerns about voice and originality. These reflections suggest that translation technologies may be influencing not only linguistic accuracy but also stylistic norms. Finally, the findings underscore significant equity and ethical issues linked to tool accessibility. Most students relied on free AI translation services, which were perceived as convenient but problematic. Participants reported concerns about data privacy, inconsistent recognition of diverse accents, and frequent translation inaccuracies, all of which affected comprehension and confidence. References Altakhaineh, A. R. M., Alghathian, G. A., and Jarrah, M. M. (2025). A comparative study of accuracy in human vs. AI translation of legal documents into Arabic. International Journal of Language & Law, 14, 63-80. https://doi.org/10.14762/jll.2025.063 Braun, V., and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa Davis, F. D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13 (3): 319–340, https://doi.org/10.2307/249008 Fanghanel, J., Pritchard, J., Potter, J. and Wisker, G. (2016). Defining and Supporting the Scholarship of Teaching and Learning (SoTL). Higher Education Academy. Fitas, R. (2025). Inclusive education with AI: supporting special needs and tackling language barriers. AI Ethics. https://doi.org/10.1007/s43681-025-00824-3 Rapa, A.A., Saja, I., Azmi, A. (2024). The Use of Artificial Intelligence (AI) Translation Tools: Implications for Third Language Proficiency. International Journal of Research and Innovation in Social Science (IJRISS), 8(09), 1952-1960. https://dx.doi.org/10.47772/IJRISS.2024.8090161 Regan, P. M., and Jesse, J. (2019). Ethical Challenges of Edtech, Big Data and Personalized Learning: Twenty-First Century Student Sorting and Tracking. Ethics and Information Technology, 21, 167-179. https://doi.org/10.1007/s10676-018-9492-2 Zhang, W., Li, A.W. and Wu, C. (2025). University students’ perceptions of using generative AI in translation practices. Instr Sci 53, 633–655 https://doi.org/10.1007/s11251-025-09705-y 22. Research in Higher Education
Poster Perceived Pedagogical and Ethical Challenges of Artificial Intelligence in Teacher Education: Teachers’ and Students’ Perspectives from Slovenia University of Maribor Faculty of Education, Slovenia Presenting Author:Recent research highlights the rapidly growing integration of artificial intelligence (AI) in higher education, emphasizing its potential to support learning, assessment, and instructional design (Bond et al., 2024). At the same time, empirical studies increasingly point to persistent challenges related to pedagogical readiness, ethical use, data protection, unequal access, and broader social and developmental implications of AI-supported learning environments (Holmes et al., 2021; Nguyen et al., 2022). These challenges are particularly salient in teacher education, where future teachers’ competencies, beliefs, and ethical orientations play a crucial role in shaping classroom practices and long-term educational outcomes (Su & Yang, 2023). The conceptual framework of this study is grounded in interdisciplinary research on technology integration in education, teacher professional competence, and responsible AI use. It draws on the Technological Pedagogical Content Knowledge (TPACK) framework (Voogt et al., 2012), which conceptualizes effective technology integration as the interplay between technological, pedagogical, and content knowledge. In addition, the framework incorporates emerging perspectives on ethical and responsible AI in education, including community-wide ethical frameworks (Holmes et al., 2021), pedagogical and policy-oriented analyses of AI risks (Nguyen et al., 2022), and the European Digital Competence Framework for Citizens (DigComp 3.0), which explicitly addresses ethical, critical, and responsible use of digital technologies (European Commission, Joint Research Centre [JRC], 2025). The Slovenian context of teacher education is relatively unified and small compared to many other EU countries. Initial teacher education is offered by only three universities and five faculties nationwide, with study programmes predominantly at the Master’s level and some at the Bachelor’s level. Structurally, Slovenian teacher education aligns with the Bologna two-cycle model and is therefore comparable to most EU systems in terms of qualification level and formal organization (European Commission/EACEA/Eurydice, 2023). Despite this structural alignment, comparative European indicators suggest challenges relevant to AI integration. While Slovenia maintains relatively high overall educational attainment, broader indicators such as population-level digital skills remain below the EU average, which may indirectly affect preparedness for advanced digital and AI-related pedagogies (European Commission, Directorate-General for Education, Youth, Sport and Culture, 2025). Moreover, OECD evidence indicates that recent graduates in Slovenia rate the quality of their initial teacher education below the OECD average, suggesting room for improvement in areas such as pedagogical innovation and digital competence development (OECD, 2016; OECD, 2025). While existing international literature has examined attitudes toward AI adoption in education, fewer studies have systematically compared teachers’ and students’ perceptions within teacher education programmes, particularly across both general pedagogical challenges and ethical challenges of AI use within a shared institutional context. Quantitative evidence comparing these stakeholder groups remains limited, especially in smaller educational systems that nevertheless reflect broader EU policy priorities and structural models. The objective of this study is therefore to examine how teachers and students in Slovenian teacher education programmes differ in their perceptions of AI-related challenges, and to situate these findings within a wider European discourse on responsible AI integration in higher education. The study addresses the following research question: What differences exist between teachers’ and students’ perceptions of (a) general pedagogical challenges and (b) ethical challenges related to the use of artificial intelligence in teacher education programmes in higher education? By comparing teachers’ and students’ perceptions across these dimensions, the study contributes empirical evidence to European and international debates on AI in teacher education. The findings aim to inform professional development, institutional policy, and the responsible, pedagogically grounded integration of AI in higher education in pedagogical study programmes on national and international level. Methodology, Methods, Research Instruments or Sources Used Research design The study employed a quantitative research design using a cross-sectional survey to examine teachers’ and students’ perceptions related to the use of artificial intelligence (AI) in teacher education. This study was conducted as part of the research project “Generative Artificial Intelligence in Education” (Project code: NRP 3350-24-3502). The project is funded by the Republic of Slovenia, the Ministry of Education, and the European Union – NextGenerationEU. Participants The student sample (n = 460) consisted predominantly of students enrolled in first-cycle (Bachelor’s) university programmes (67%), followed by Master’s-level students (16.3%) and students in professional higher education programmes (14.3%) and PhD programmes (2.3%). Geographically, students were recruited from three Slovenian institutions educating future teachers: two faculties at the University of Ljubljana (50.0%), the Faculty of Education, University of Maribor (25.0%), and the Faculty of Education, University of Primorska (25.0%). The teacher sample (n = 119) included higher education teachers and teaching assistants. Most participants were female (71.3%), followed by male participants (27.8%); one participant (0.9%) did not report gender. The average age of teachers was 46.35 years, with an average of 20.59 years of professional experience. Their institutional distribution was similar that of the student sample. Instrument Data were collected using a questionnaire, designed specifically for this study. The instrument was developed through multiple expert reviews conducted by members of the research team and external experts. This paper reports only results related to general pedagogical challenges and ethical challenges of AI use. General challenges included self-assessed AI knowledge, pedagogical readiness of teachers and students, effective ability to use AI, digital inequality and access, overreliance on AI, data privacy protection, and potential social and emotional impacts (e.g. interaction patterns, motivation, and emotional engagement). Ethical challenges included plagiarism, intellectual property, bias and unfairness, data privacy concerns, false or fabricated information, anthropomorphization of AI tools, environmental impacts, social impacts, dependence on AI use, and lack of critical judgment. Internal consistency for the teacher questionnaire was acceptable for exploratory research (general pedagogical challenges: Cronbach’s α = .68; ethical challenges: α = .86). For students, reliability was slightly higher (general pedagogical challenges: α = .69; ethical challenges: α = .89). Given the newly developed nature of the instrument, these values were considered satisfactory. Data collection and ethics Data were collected between December 2024 and January 2025. The study adhered to fundamental research ethics principles. Ethical approval was obtained from the Research Ethics Committee of the Faculty of Arts, University of Maribor (No. 038-30-196/2024/42/FF/UM). Conclusions, Expected Outcomes or Findings Independent-samples t-tests revealed statistically significant differences between teachers and students across all examined variables of general pedagogical challenges related to AI use in teacher education. Teachers reported higher self-assessed knowledge of AI than students and expressed stronger agreement with statements concerning pedagogical readiness, unequal access to AI tools, unintended negative effects of AI use, data privacy challenges, and potential social and emotional impacts on learners. Effect size for general pedagogical challenges estimates (Cohen’s d = 0.26–0.56) indicate small to moderate practical significance, with the largest differences observed for pedagogical readiness, access inequalities, and data privacy concerns. These findings suggest that teachers approach AI integration with a more cautious and risk-aware perspective, likely reflecting their professional responsibility for pedagogical quality, ethical compliance, and student well-being. Teachers also reported significantly higher concern than students across most ethical challenges of AI use, including plagiarism, intellectual property, bias and unfairness, data privacy, false or fabricated information, anthropomorphization of AI tools, environmental impacts, social impacts, and lack of critical judgment (p ≤ .002). No statistically significant difference was found for dependence on generative AI use, indicating a shared perception between the two groups on this issue. Effect sizes for ethical challenges ranged from small to large (Cohen’s d = 0.15–0.79). The strongest effects were observed for plagiarism and intellectual property, highlighting substantially greater concern among teachers. Moderate effects emerged for bias and unfairness, data privacy, misinformation, and lack of critical judgment, while smaller effects were found for anthropomorphization and environmental and social impacts. The findings indicate systematic differences between teachers’ and students’ perceptions of both general and ethical challenges of AI use in teacher education. Teachers consistently assign greater importance to AI-related risks, underscoring the need for targeted professional development, ethically grounded AI integration, and curricular approaches that address both technical competence and critical, responsible AI use in higher education. References Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, Article 4. https://doi.org/10.1186/s41239-023-00436-z European Commission, Directorate-General for Education, Youth, Sport and Culture. (2025). Education and training monitor 2025. Publications Office of the European Union. https://doi.org/10.2766/2720910 European Commission, Joint Research Centre. (2025). DigComp 3.0: The Digital Competence Framework for Citizens. Publications Office of the European Union. https://doi.org/10.2760/144121 European Commission/EACEA/Eurydice. (2023). Initial education for teachers working in early childhood and school education: Slovenia. Eurydice national education systems. https://eurydice.eacea.ec.europa.eu/eurypedia/slovenia/initial-education-teachers-working-early-childhood-and-school-education Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Buckingham Shum, S., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(2), 504–526. https://doi.org/10.1007/s40593-021-00239-1 Nguyen, A., Ngo, H., Hong, Y., Dang, B., & Nguyen, B. (2022). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28, 4221 - 4241. https://doi.org/10.1007/s10639-022-11316-w. OECD. (2016). Education policy outlook: Slovenia. OECD Publishing. https://www.oecd.org/content/dam/oecd/en/about/projects/edu/education-policy-outlook/398027-Education-Policy-Outlook-Country-Profile-Slovenia.pdf OECD (2025). Education at a glance 2025: OECD indicators. OECD Publishing. https://doi.org/10.1787/1c0d9c79-en Su, J. H., & Yang, W. P. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355–366. https://doi.org/10.1177/20965311231168423 Voogt, J., Fisser, P., Pareja Roblin, N., Tondeur, J., & van Braak, J. (2012). Technological pedagogical content knowledge – A review of the literature. Journal of Computer Assisted Learning, 29(2), 109–121. https://doi.org/10.1111/j.1365-2729.2012.00487.x | ||