Интеграция технологий искусственного интеллекта в лингвометодическую подготовку будущих учителей иностранного языка

The acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.

In today’s evolving world of work, Artificial Intelligence (AI) has emerged not merely as a technological tool but as a catalyst for rethinking how jobs are structured and performed (Bankins et al., 2024; Parker & Grote, 2022). Across diverse job families and organizational contexts, tools like generative AI (GAI) go beyond mere instruments for data analysis or structuring and can adopt increasingly far-reaching roles through rapid progress in generating new graphic or textual content or algorithmic decision making (Ferràs- Hernández, 2018; Walsh et al. 2019). Central to this symposium is the recognition that this changing nature of work due to digitalization, propelled by AI and related tools, calls for a fresh look at established theories of job design and strategies for human resource management. Traditionally, research has illuminated how fostering characteristics like autonomy, skill and task variety, or adequate demands affect employee motivation, satisfaction, and well-being and thereby further organizational outcomes (Oldham & Fried, 2016; Parker et al., 2017). Yet the introduction of AI – particularly generative AI – adds complexity: Who performs which tasks? How are decisions made, and by whom? Which skills remain uniquely human, and which may be augmented or even entirely adopted by AI? Such questions demand a more nuanced and strategic approach to designing work in the future that balances efficiency and innovation with psychological and ethical considerations. The four presentations in this symposium aim to contribute to theory building on how AI affects work in terms of work design, the interplay of employees and AI use, and work outcomes. In particular, they highlight potential risks or unique challenges when working with AI (e.g., in terms of less enriched work, lowered autonomy levels, threats to job identity or risks due to disclosure of use) and path the way for interventions to achieve benefits of such technologies at work. In detail, the empirical studies in this symposium illuminate these issues at three interconnected levels: 1. Task-Level Insights: Two contributions focus on the more granular task characteristics (e.g., autonomy, skill variety) in modern, AI-enhanced work contexts. They demonstrate how AI use influences the allocation of specific tasks when designing work roles and how the perception of task characteristics changes when AI is used in a decision-making task. 2. Job- and Identity-Level: One paper zooms out from individual tasks to jobs as a whole and explores how AI-driven change might disrupt employees’ job identity. This lens demonstrates how task changes cascade into bigger questions of role definition and identity. 3. Organizational Context: Finally, one presentation adopts the broadest perspective and investigates managerial and organizational factors that might shape employees’ willingness to disclose and ethically integrate AI tools. Taken together, these presentations point toward a future in which AI’s transformative potential hinges on thoughtful coordination across tasks, roles, and organizational systems. By combining established frameworks with new insights drawn from (quantitative and qualitative) surveys and experiments conducted in Germany, Austria, the United States, the United Kingdom, and Australia, these contributions illustrate how AI can enrich, rather than diminish, work and thereby strengthen human-centered organizations. By focusing on variables at different levels, the studies path the way for applications in practice, emphasizing the importance of individual approaches (e.g., training for job design, supporting self-efficacy), or on the organizational level (e.g., aligning HR practices, develop clear and supportive organizational policies and guidelines for the use of AI at work). Investigating the Influence of Work Design Perspective and AI Integration on Enriched Job Design Author: Tanja Bipp; Heidelberg University Author: Thea Ebert; Aalen University Collaborative Intelligence: Integrating Generative AI in Knowledge Work and its Impact on Job Design Author: Sabina Hodzic; University of Graz Author: Simon Grob; University of Graz Author: Bettina Kubicek; University of Graz Job Identity as Starting Point for SHRM in AI-Driven Organizational Change Author: Sophie Berretta; Ruhr University Bochum Author: Annette Kluge; Ruhr University Bochum Silent Adoption: Understanding the Drivers of Non-Disclosure of Generative AI Tools by Employees Author: Fangfang Zhang; Curtin University - Perth

The integration of Artificial Intelligence (AI) into English as a Foreign Language (EFL) education marks a significant transformation in pedagogical practices, particularly in higher education. In many European countries, such as Germany, Estonia, Finland, and Ukraine, this transformation is accelerating as education systems increasingly adopt AI technologies that replicate human cognitive processes like problem-solving and decision-making. They provide unprecedented opportunities for personalizing language learning based on individual student needs. This study explores current trends, pedagogical models, and empirical findings related to the implementation of AI in EFL classrooms, with particular attention to its impact on the development of language skills, learner autonomy, and affective factors such as motivation and engagement. Within the European context, these issues are especially relevant due to the widespread status of English as a foreign language and the strategic emphasis on digital innovation in national education systems. Drawing on both international and Ukrainian research, the study examines the application of widely used AI tools, such as chatbots, writing assistants, translation platforms, and intelligent tutoring systems, in language teaching. Empirical evidence, including findings from European-based studies, indicates significant improvements in students’ receptive (reading, listening) and productive (speaking, writing) language skills, lexical acquisition, and intercultural competence. Furthermore, AI enhances the affective dimension of learning by boosting motivation, promoting engagement, and alleviating foreign language anxiety through personalized, adaptive feedback and autonomous learning pathways. Despite these benefits, the integration of AI into EFL education is characterized by some challenges, including students’ over-reliance on technology, insufficient digital literacy among users, ethical concerns regarding data privacy, and a lack of comprehensive pedagogical frameworks. The study emphasizes the need for pre-service teacher training programs that effectively incorporate AI competencies. Overall, the study results indicate a positive impact of integrating AI into the teaching of English as a foreign language in European counties (including Ukraine), demonstrating how technological innovations can complement traditional pedagogical approaches to support accessible, effective, and learner-centered foreign language education in the digital era.

Importance. Artificial intelligence technologies can significantly expand the opportunities for students to form professional competencies during the study in higher education. At the same time, the effectiveness of teaching methods depends on a number of conditions. The purpose of the article is to identify and substantiate the list of organizational and pedagogical conditions of training law students to legal drafting in a foreign language through artificial intelligence tools. Materials and Methods. The research is conducted on the basis of analyzing scientific literature on psychology, pedagogy and methods of teaching foreign languages. The sample of sources included domestic scientific journals of the Higher Attestation Commission of the Russian Federation list (category 1 and 2), as well as other modern sources: monographs and educational and methodical works. Results and Discussion. The author’s list of organizational and pedagogical conditions for training law students to legal drafting in a foreign language through artificial intelligence tools is identified and substantiated: a) the level of foreign language communicative competence – from Intermediate Mid to Advanced Mid; b) differentiation of law students’ study groups at the classes on foreign language; c) taking into account the peculiarities of specialist’s professional competence in the field of jurisprudence; d) the use of legal document constructors; e) the use of exercises system and tasks based on the following criteria. Conclusion. The novelty of the research consists in the development and substantiation of organizational and pedagogical conditions list of training law students to legal drafting in a foreign language through artificial intelligence tools. The obtained results can be used in the methods designing of teaching students of non-linguistic training direction in the development of professional competences based on the use of artificial intelligence technologies.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

The use of artificial intelligence (AI) technologies has been indicated to influence various aspects of second or foreign language (L2) education across diverse contexts. Nevertheless, how the adoption of AI may influence the professional wellbeing of English as a foreign language (EFL) teachers in under-resourced settings has received limited empirical attention. To address this gap, the present study implemented an experimental design to investigate the impact of AI technologies on Turkish EFL teachers’ professional wellbeing during a semester-long intervention. A total of 76 teachers completed the professional wellbeing scale before and after participating in a course mediated by AI tools. The participants were assigned to control and experimental groups. The results of ANCOVA revealed a significant improvement in the experimental group teachers’ professional wellbeing from the pretest to post-test, indicating the effectiveness of the intervention course. The study highlights the need for professional development programs powered by AI technologies to enhance under-resourced EFL teachers’ AI literacy and wellbeing.

This article deals with the problem of introducing innovative computer technologies (artificial intelligence) in teaching a foreign language (Russian as a foreign language). Although the use of artificial intelligence in foreign language teaching is currently being actively discussed in the methodological community, some aspects require multilateral consideration and further reflection. The relevance of the problem is determined by the need to optimise the process of forming foreign language communicative competence with the use of high-tech learning tools. The wide spread of innovative technologies innovative technologies, which have a number of advantages (high speed of data processing, provision of information in any format, cost-effectiveness, etc.) makes methodologists look for ways to integrate them into the educational environment. The article systematises and clarifies the possibilities of using artificial intelligence in Russian as a foreign language classes on the basis of scientific literature. The areas of application of artificial intelligence tools as a means of teaching different aspects of the learnt language and types of speech activity are described. The authors prove that artificial intelligence has wide prospects for managing the learning process, improving teaching methods and techniques and increasing the efficiency of learning a foreign language.

This study explores teachers’ readiness and perceptions regarding the integration of artificial intelligence (AI) tools in education. The study was conducted among 3,848 Estonian teachers. Given AI’s transformative potential in enhancing teaching effectiveness, automating administrative tasks, and supporting personalized learning, it is critical to assess whether teachers are equipped to effectively utilize these technologies. Utilizing a comprehensive framework informed by human-centered approaches, this research investigates teachers’ familiarity, attitudes, perceived usefulness, and readiness to implement AI-driven tools in educational settings. A survey conducted among teachers reveals a balanced perspective characterized by interest, openness, and awareness of both opportunities and potential risks associated with AI. Findings indicate significant predictive relationships between perceived usefulness and readiness, as well as attitudes toward AI tools and actual adoption intentions. Teachers’ confidence and perceived relevance of AI in educational contexts emerge as key factors facilitating their willingness to engage with AI technology. Conversely, factors such as AI anxiety and limited training opportunities are identified as barriers to effective implementation. The study underscores the need for targeted professional development programs, ethical guidelines, and policy support to enhance teachers’ readiness and facilitate the responsible integration of AI into educational practices.

In recent years, generative artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize collaborative learning experiences. This study investigates the role of generative AI in facilitating innovative collaboration among Saudi university students engaged in group projects within digital learning environments. As higher education institutions increasingly adopt digital tools and platforms, understanding how generative AI can enhance creativity, teamwork, and overall learning outcomes becomes paramount.Utilizing a mixed-methods approach, this research employs a comprehensive questionnaire distributed to a diverse sample of Saudi university students. The questionnaire is designed to assess participants’ experiences with generative AI tools, their perceptions of these tools' effectiveness in enhancing collaboration, and the impact on their creativity and problem-solving abilities during group projects. The survey includes both closed-ended questions for quantitative analysis and open-ended questions for qualitative insights, allowing for a nuanced exploration of the role of generative AI in educational settings.The findings reveal that a significant majority of participants (approximately 78%) reported that generative AI tools, such as AI-driven brainstorming applications and collaborative writing platforms, have had a positive influence on their group project experiences. Students highlighted that these tools fostered a more inclusive environment, enabling team members to contribute ideas more freely and creatively. Moreover, 65% of respondents noted improved communication and coordination among team members, attributing this enhancement to the real-time collaborative features provided by generative AI tools.Additionally, qualitative feedback from open-ended questions indicated that students appreciated the ability of generative AI to generate diverse perspectives and ideas, which enriched the brainstorming process and led to more innovative project outcomes. Participants expressed that the use of AI tools reduced the cognitive load associated with project management, allowing them to focus more on creative problem-solving and critical thinking. However, some concerns were raised regarding the potential for over-reliance on AI-generated content, emphasizing the need for a balanced approach that combines human creativity with AI assistance.This study contributes to the growing body of literature on the integration of artificial intelligence in education, particularly in the context of collaborative learning. The implications of these findings suggest that incorporating generative AI tools into the curriculum can significantly enhance the collaborative capabilities of Saudi university students, fostering a culture of innovation and creativity in digital learning environments. As educational institutions continue to navigate the complexities of digital transformation, understanding the potential of generative AI as a catalyst for collaboration will be essential for fostering effective and engaging learning experiences.In conclusion, this research highlights the transformative potential of generative AI in enhancing group projects among Saudi students, offering valuable insights for educators and policymakers aiming to leverage technology for improved collaborative learning outcomes.

Background. In the context of the continuous shifts in the modern labour market and the escalating competitiveness among students of maritime specialties, there is an increasing demand for the training of highly qualified specialists in the maritime industry, who are able to interact effectively with international partners at a professional level. In this respect, the use of generative artificial intelligence (AI) to improve foreign language teaching methods for students of maritime subjects is especially relevant. This is fully justified under the conditions of globalisation and the digital transformation of the educational sphere. Purpose. The purpose is to determine the effectiveness of generative AI application in the process of building foreign language professional communicative competence of maritime students by modelling realistic scenarios of job interviews in shipping companies. Materials and methods. The present study adopted a comprehensive approach, integrating theoretical and empirical methods of analysis. The theoretical basis was established through a comprehensive review of normative and educational documents, with the aim of identifying the fundamental requirements for the educational process. This was complemented by a thorough examination of scientific and educational literature, as well as methodological literature, on the application of AI in education and linguodidactics, thus enabling the identification of current trends and the potential of technologies for teaching maritime students. The empirical element of the study involved modelling, describing and analysing professional interview scenarios using generative AI models, the observation of students' learning activities, and the conduction of interviews to obtain feedback on the perception and effectiveness of the new techniques. Results. The research yielded an innovative methodology for training students of maritime specialities at Sevastopol State University for professional interviews in international shipping companies. The developed three-stage training model, combining the creation of a database of questions, simulation of interviews using generative (AI) and practising skills through business games, demonstrated high efficiency in the formation of both linguistic and professional competences of students, in addition to soft skills deemed integral for a successful career in the maritime industry. The integration of generative AI within the training framework of maritime students has been found to expand the scope of opportunities for effective preparation for professional interviewing. The implementation of this methodology has been identified as a significant contributor to the modernisation of the educational process, rendering it more personalised, effective and aligned with the precise requirements of the contemporary labour market.

As generative artificial intelligence (AI) tools and large language models (LLMs)-powered applications develop rapidly in the era of algorithms, it should be integrated thoughtfully to enhance English as a Foreign Language (EFL) teaching and learning without replacing learners’ critical thinking (CT). This study systematically analyzes the impact of generative AI tools and LLMs on language learners’ CT in EFL education using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies from 2022 to 2025. A thorough review of 15 selected studies focuses on generative AI tools and LLMs’ dual nature, research methods, main focuses, theory and models, limitations and challenges, and future directions in the field based on Web of Science (WoS), SCOPUS, ERIC, ProQuest, and Google Scholar. The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. Furthermore, 3 types of research methods, 3 key themes of research focus, and 4 groups of theoretical perspectives were examined. However, 4 kinds of limitations in this field remain, including research scope, user dependency, generative AI reliability, and pedagogical integration. Future research can focus on assessing long-term effects, broadening research scope, promoting responsible AI use, and refining pedagogical strategies. Finally, Limitations, implications and future direction of this study were discussed.

The advent of Artificial Intelligence (AI) has brought transformative changes across various sectors, including education. In Library and Information Science (LIS), AI tools hold significant potential for enhancing teaching, research, and administrative functions. This study investigates the awareness, perception, and utilization of AI tools by LIS lecturers in Nigerian higher institutions. Data were collected using questionnaires and analysed with the Statistical Product and Service Solution (SPSS), with hypotheses tested via Pearson Product Moment Correlation (PPMC). The findings reveal a high degree of awareness and positive perception towards AI tools among LIS lecturers. Commonly used tools for teaching include ChatGPT, Socrative, ChatPDF, Turnitin, and Gamma. Despite recognizing AI's potential benefits for improving information retrieval, data management, and personalized learning, actual usage remains limited due to challenges such as rapid technological advancement, lack of infrastructure, and resistance to change. All hypotheses were rejected, indicating a significant relationship between awareness, perception, and the use of AI tools in teaching. If measures such as having enhanced AI literacy and training programs for LIS educators, integration of AI into the LIS curriculum, development of institutional policies on AI adoption, and incentives for AI integration, then the challenges observed could be mitigated.

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

The advent of artificial intelligence (AI) presents an opportunity to enhance the management of multiple medications for patients. Despite the growing body of research on the clinical impact and accuracy of AI in healthcare, current literature is limited in addressing AI’s role in medication management for older adults. The goal of this scoping review was to assess the extent and applicability of AI applications in managing multiple pharmacological therapies among adults aged 50 and older. Relevant articles were searched in EMBASE, Ovid MEDLINE, and Web of Science databases. Search phrases included “polypharmacy/polytherapy”, “artificial intelligence in healthcare”, “machine learning/language learning”, and “elderly populations”. The search initially identified 58 citations. After a systemized and rigorous screening process, 12 articles were further screened for eligibility and critically appraised for bias and appropriateness. Of those, a total of five articles were retained for the final analysis. They focused on the application of AI and web-based applications to reduce inappropriate medication use and drug interactions, how machine learning and audio-based activity recognition systems could improve medication adherence among older adults, and recognizable patterns between multimorbidity and polypharmacy in elderly populations with chronic illnesses. The main findings of this review suggested that AI tools have demonstrated efficiency and accuracy in eliminating drug-drug interactions, assisting in the detection of potentially inappropriate medications (PIMs), and identifying patterns of multimorbidity due to polypharmacy in older adults. Moreover, AI tools were considered easy to use and helped enhance medication adherence. Results suggest that AI has potential in managing polypharmacy, particularly in enhancing medication safety, improving adherence, and predicting risk factors for medication-related errors in older adults. Future investigations are needed that focus on AI’s long-term effect on patient outcomes, its role in personalized pharmacotherapy, and ongoing challenges related to algorithmic transparency, bias mitigation, and regulatory oversight. As AI continues to evolve, its integration into healthcare would benefit from methodical evaluation, stringent oversight, and interdisciplinary collaboration to ensure its safe and effective deployment in patient care.

This study explores the integration of artificial intelligence (AI), specifically ChatGPT, as a self-directed learning tool in an undergraduate physiology-related course within a Speech Therapy program. The aim was to introduce first-year Speech Therapy students (n = 30) to AI technologies, assess their prior knowledge and perceptions, and foster critical thinking regarding AI-generated content, with a focus on evaluating AI literacy both generally and within the context of physiology education. A preliminary survey revealed that 73.3% of students (22 out of 30) had previously used AI, while 93% expressed interest in learning about its academic applications. Students participated in a structured 2-h classroom session to understand how generative AI works, followed by a group task comparing traditional and AI-generated responses to academic questions. Notably, students engaged in a comparative analysis of their own completed work with the AI-generated version. A postintervention survey indicated that 82% of students learned new aspects of AI use, 86% found the experience helpful, and 95.5% emphasized the importance of verifying AI output with other sources. These findings reflect students' views on both general AI use and physiology-related academic tasks, suggesting that, with proper guidance, undergraduates can critically engage with AI tools and recognize their strengths and limitations. The intervention promoted self-regulated learning, digital literacy, and ethical awareness in the use of AI, laying the groundwork for broader implementation in physiology and biomedical education. Furthermore, the findings suggest that additional faculty training and institutional support could facilitate meaningful integration of AI into higher education programs.NEW & NOTEWORTHY This study is among the first to integrate ChatGPT as a self-directed learning tool in undergraduate physiology education within Speech Therapy. It demonstrates that first-year students, with minimal prior academic artificial intelligence (AI) experience, can critically engage with AI-generated content, enhancing digital literacy and ethical awareness. The research highlights the importance of guided interventions to promote critical thinking and the need for institutional support to effectively incorporate AI in health sciences curricula.