Artificial Intelligence in Vocational Education and Training (VET): Evaluating VET Leaders’ Acceptance of AI in Switzerland

Artificial Intelligence in Vocational Education and Training (VET): Evaluating VET Leaders’ Acceptance of AI in Switzerland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Artificial Intelligence in Vocational Education and Training (VET): Evaluating VET Leaders’ Acceptance of AI in Switzerland Sabine Seufert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4628645/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial intelligence (AI) is having a transformative impact on both the labour market and education systems. Vocational education and training (VET) leaders play a crucial role in shaping the future of education and training by guiding strategic decisions and policy directions; without VET leaders, efforts to integrate AI into VET risk facing significant barriers to wider uptake. Research that is focused on harnessing AI capabilities to innovate VET systems and the leadership required are rare. Therefore, the aim of this study is to assess VET leaders' acceptance of AI (TAM, Davis, 1989 ; UTAUT, Venkatesch et al., 2003). An online survey was conducted in February 2023 to assess the general acceptance of AI and AI-based use cases for VET. A total of 111 VET experts participated in the online survey. Each expert is a senior VET manager who leads a specific unit in the VET system. The empirical data suggest that there may be widespread agreement among VET leaders about the ability of personalised competence development to improve equity and inclusivity. However, use cases for increasing the strength of the links between the labour market and education system with data-driven approaches are less widely accepted. The empirical findings of this study provide a situational analysis of the acceptance of AI by VET leaders. Furthermore, the findings can also elucidate existing knowledge gaps of VET leaders regarding AI capabilities, applications, and implications. Artificial Intelligence Technology Acceptance Model Hybrid Intelligence AI Leadership Figures Figure 1 1. Introduction Artificial intelligence (AI) is becoming increasingly important in the lives of humans, as evidenced by the increasing interactions between humans and intelligent machines (Kim, 2022 ; Dang, 2022 ). AI includes systems based on machine learning, logical programming or statistical methods that are capable of generating content, making predictions, providing recommendations, making decisions or taking action based on data analysis (European Commission, 2022, p. 10). Currently available AI methods, unlike previous rule-based approaches, do not simply replicate human rules but rather make decisions through optimization and statistical techniques (High-Level Expert Group on AI, 2019). The emergence of ChatGPT has played a significant role in bringing AI into mainstream public discourse. The capabilities of this AI application have amazed the global public while also raising concerns about its potential to take over tasks that are traditionally performed by humans (Kasneci et al., 2023 ). The impact of AI is simultaneously transforming the employment and education systems. On the one hand, the impact of AI on the labour market could be disruptive (Mass-Mann and Hofstetter 2020; OECD 2020; Zhang et al., 2021 ). The potential for job displacement and new role creation is linked to shifts in workplace tasks and responsibilities (Shiohira 2021 ). On the other hand, the transformative effects of AI could also have an impact on education. The use of AI systems in education offers many opportunities (Miao et al. 2021 ; Adeshola and Adepoju 2023 ). These systems can identify individual learning needs, design personalised learning pathways, monitor progress and provide real-time feedback, thereby improving learning outcomes. This approach is particularly beneficial in vocational education and training (VET) systems that need to respond to changes in the labour market and industry needs, ensuring that the skills that are taught in these systems are up-to-date and in demand (Seufert et al., 2021). The debate surrounding ChatGPT also elucidates the potential risks of AI (Kasneci et al. 2023 , Bao et al., 2022 ). There has been substantial discussion about the risk that young adults might lose necessary skills because of an overreliance on AI (Hamilton et al., 2023 ). Furthermore, concerns about AI include concerns about data privacy violations due to the processing of large amounts of personal data. AI applications could produce biased results due to the use faulty or partial data sets, potentially discriminating against certain groups. In addition, the misuse of AI and concerns about its reliability and ethical application warrant attention (Floridi 2019 , Jobin et al., 2019 ). While previous studies examined the impact of AI on labour markets and education systems separately, research on harnessing the potential of AI to reform VET systems, particularly those at the interface between the labour market and the education system, remains limited. Furthermore, there is little insight into VET leaders’ acceptance of AI. This gap is significant given the rapid evolution of AI and its growing relevance in different occupational sectors. This paper seeks to fill this gap. 2. Background 2.1. Technology Acceptance Model (TAM) In the TAM, “Perceived Usefulness” is defined as the degree to which a person believes that using a particular system will enhance their job performance (Davis, 1989 ). When a user perceives an AI-based solution as being useful, it means that they believe that the solution will provide benefits, such as improving efficiency, accuracy, or decision-making. This belief is a central determinant in the model and is directly related to the intention of a person to use the technology. This concept is similar to “Performance Expectancy” in UTAUT, which is the degree to which an individual believes that using the technology will help them attain gains in job performance (Venkatesch et al., 2023). “Behavioural Intention to Use” is a direct antecedent to “Actual System Use” in both the TAM and UTAUT. It reflects the strength of a user’s intention to perform a specific behaviour, in this case, the use of an AI-based solution. If a user states "I would use the AI-based solution", this response indicates a positive behavioural intention, which is often a strong predictor of actual technology use. Therefore, “Perceived Usefulness” or “Performance Expectancy” is hypothesised to positively influence “Behavioural Intention to Use”. If users believe that the AI-based solution is useful and will improve their performance (high perceived usefulness), they are more likely to have a stronger intention to use it. Understanding these relationships is essential for VET leaders in the development and evaluation of AI-based solutions, as this understanding can inform strategies to increase acceptance and encourage use among educators and students. Furthermore, by focusing on the perceived usefulness of AI applications and actively promoting their benefits, VET leaders can positively influence the implementation strategies that are needed to integrate AI into VET in the medium and long term. 2.2. AI-based Solutions for VET In the current literature, AI is often conceptualised with a dual purpose: to take over simple or routine tasks from humans (often referred to as "automation” or substitution") and to assist humans in performing more complex tasks (collaboration in the form of "augmentation" of work) (Einola & Khoreva 2023 ). Augmentation thus represents a new paradigm for the use of computers. This new paradigm leads to a change in the view of automation; that is, automation is no longer viewed as a threat but rather as an opportunity (Davenport & Kirby 2016 ). Instead of substitution , which has long been the focus of discussion (Frey & Osborne 2013 ), the term augmentation is intended to emphasise the increased interaction and complementarity of humans and machines in task performance (Davenport & Kirby 2016 ). The interaction between humans and intelligent assistance systems has also been referred to as "hybrid intelligence" (HI) for several years (Akata et al., 2020 ). HI refers to the constellations of mixed teams in which humans and machines work together synergistically, proactively and purposefully to jointly achieve goals (set by humans). This idea of hybridising human and machine intelligence is not novel. Doug Engelbart (1962) was influential in this regard with his concept of augmented intelligence. The strengths of humans are seen primarily in areas such as flexibility, empathy, creativity and “common sense”, which involves considering diffuse social, situational, and cultural aspects. Furthermore, the strengths of machines lie mainly in pattern recognition, data processing, probability, speed, and endurance. With the recent developments in ChatGPT and GPT-4, changes have occurred in the attribution of these strengths. As the use of large generative language models, such as ChatGPT, becomes more widespread, not only decision-making tasks but also creative activities are increasingly seen as potential application areas (Bubeck et al., 2023; Lim et al., 2023 ). With AI agents or generative AI systems, highly efficient assistance systems will soon be widely available (Bubeck et al., 2023). These assistant systems can be used in various professions and to perform a multitude of tasks. This raises questions about the interaction between humans and AI and the competencies that are required for successful collaboration with intelligent assistant systems. According to the World Economic Forum (2023), it is anticipated that by 2027, 42% of corporate tasks will be automated, leading to a comprehensive transformation of the work landscape. Generative AI enables automation and augmentation. Even highly skilled professionals, who have previously been less affected by automation, now need to expand and renew their competencies (Candelon et al., 2023, p. 40). Due to these dynamic changes, it is becoming increasingly challenging to address the overarching goal of VET, which is to equip individuals with the necessary competencies—including skills, knowledge, and attitudes (action-oriented competences, Erpenbeck & Rosenstiel, 2007 )—that are required for specific occupations and meet the demands of the labour market. Coordination between the labour market and education system is becoming an increasingly important challenge during the digital transformation (Renold et al., 2015 ). The definition of competences can act as a bridge between the labour market and the education and training systems (see Fig. 1 ): Due to these dynamic developments, additional mechanisms need to be introduced into VET to strengthen the strength of this link on the one hand and to promote the development of more personalised skills in the education system on the other hand. Three main developmental directions for the use of AI can be identified. First, AI can be used to strengthen the linkages between employment and education systems through data-driven approaches from the labour market. In both the EU and the US, AI technologies are used to process labour market data, allowing for a comprehensive representation of the current state of the labour market, the identification of skill profiles that are in demand and the prediction of skills that will be in demand in the future (Mezzanzanica & Mercorio 2019). The ability of AI to process and represent data provides valuable insights into labour market trends. Second, within the education system, a primary goal is to establish personalised learning supported by AI (Kasneci et al., 2023 ). This approach tailors education to individual learning needs, thereby increasing the effectiveness of vocational training. Third, use cases could focus on facilitating new forms of collaboration with AI to facilitate HI as a new required field of competence (Candelon et al., 2023). 3. Methods and Procedures 3.1 Online Survey and Sample The Swiss education system offers a promising framework for our analysis, as it is characterised by its high quality, strong links between education and the labour market, and innovative approaches to the integration of new technologies. These characteristics allow our study to gain insights that may be relevant both for Switzerland and for other countries. The overarching aim of this study is to evaluate VET leaders’ acceptance of AI; the following research questions were formulated: What are the key facets of an AI vision for VET? What is the general acceptance of AI in VET? What is the acceptance of specific AI-based use cases for VET? An online survey was conducted to evaluate the general acceptance of AI and AI-based use cases for VET. The questionnaire was initially piloted with five VET experts to obtain feedback on the clarity of the questions and the applicability of the use cases during the interviews. A total of 111 VET experts participated in the online survey. Each expert is a senior vocational education and training manager who heads a specific unit in the VET system. Most of these experts work in vocational schools (principals, 43%), training companies (head of apprenticeships, 18%), professional associations (13%), employers' associations (6%) and cantonal VET offices (7%). The VET experts are all involved in the digitalization strategy of VET in Switzerland and are decision makers in this strategy process. The survey was conducted between mid-January and the end of February 2023. 3.2 Evaluation of the AI-based Use Cases for VET The AI-based use cases refer to individual problem solutions in the VET system. The use cases were discussed with the Strategic Steering Committee for VET as part of the State Secretariat for Education, Research and Innovation (SERI)-funded research project, and an initial prioritization was carried out (Seufert, 2023 ). Based on the results, 11 use cases were specified for further validation. The following 11 use cases investigate the multifaceted role of AI to provide solutions in each of these cases, which are categorized into three areas (see Fig. 1 ): I) Increasing the Strength of the Link with Data-driven Approaches Use Case 1: Intelligent Labour Market Information Systems AI-based methods are used to observe trends using real-time data. The main function of intelligent technologies is to process large amounts of raw data to support decision-making. Technically, such intelligent support of decision-making can be achieved by so-called Labour Market Information Systems (LMIS). These tools can help policymakers, employers, vocational trainers, teachers, and individuals prepare for changes in jobs and skills due to automation, providing timely and valuable insights that allow efficient adaptation and improvements in the relevance and quality of training programmes. Use Case 2: AI-based Competency Models for Curriculum Design Curriculum development in VET currently relies on expert-curated competency models. These development processes are becoming increasingly complex and resource intensive. The data-driven development of competency models could provide support for decision-making, at least as a complementary measure. For example, in the framework of the European ESCO initiative, a pilot project was carried out to demonstrate the linking of learning outcomes/qualifications with ESCO competences using an AI-based solution, with the aim of improving hierarchical competence taxonomies on the basis of the analyses (CEDEFOP, 2022 ). Data-based competence profiles can be used to support decision-making in career development. The use of job advertisements ensures that the “language of competences” that is used by employers is included. The updating and maintenance of competency models are increasingly (partially) facilitated by automated processes, which also reduces complexity. Use Case 3: AI-based Learning Analytics for the Evaluation of Competency Profiles Evaluations in VET are complex. However, feedback loops for acquired competences are missing. In this context, AI could be used to evaluate competence profiles through learning analytics (Avila et al., 2020 ). Learning analytics can systematically and regularly evaluate learners' competence profiles based on portfolio data. This can provide feedback to efficiently adjust and improve the relevance and quality of training programmes (Dillenbourg 2017 ). The integration of AI in this process is crucial, as it allows for a more nuanced and detailed analysis of learner outcomes in real time while analysing the link to labour market needs. II) Establishing Personalised Learning in Education System Use Case 5: Personalised Learning Management System Unlike “traditional” Learning Management Systems, personalised learning platforms focus on the learner and their (personalised) learning process rather than on managing courses and data. Such systems could serve as an attractive learning portal, using AI capabilities to intelligently automate processes and create personalised learning experiences (e.g., learning recommendations, microcoachings, display of competence progress). These systems provide learners with an attractive and easy-to-use learning solution. AI services can be developed to promote more personalised skills development. Use Case 4: Cross-Educational Support Instruments Further training is often required to close existing gaps in skills (e.g., in mathematics or languages). Adaptive learning technologies identify and understand individual learning patterns. The content and level of difficulty are automatically adjusted to meet the learner's needs in real time. Automated feedback is timely and accurate. Adaptive support tools are particularly suitable for mastery learning. Learners do not move on to new material until they have mastered the previous and foundational content that serves as a prerequisite. Use Case 6: AI-based Validation of Competencies with ePortfolios Working with portfolios to support the development of individual competence is a promising approach in VET. However, the activities that are involved are very time consuming. Many learners find it difficult not only to document what they have learned but also to reflect and communicate about their own resources and weaknesses (Caruso et al., 2021). Additionally, supporting learners in this process is also a major challenge for VET practitioners. The competences that are needed to support and generally coach reflection processes in learners are more pronounced among teachers due to their qualifications. An ePortfolio is used to document and reflect on one's own competences and as a development portfolio to guide the learning process. Such ePortfolios could be used more widely to create a common experience space and to involve teachers to a greater extent in their coaching role (Cattaneo & Aprea, 2018 ). Use Case 7: AI-based Modularised, Integrated Assessment Systems AI capabilities can be used to make open-ended questions in tests more challenging and more competence-oriented (Cope et al. 2021 ). AI could be used to train systems to significantly reduce the workload of examiners by assisting with marking (e.g., by suggesting grades, sorting responses, and marking clear and borderline cases). The proof of acquired competences can be (partially) automated during basic training for continuing professional development. More personalised assessment systems could be developed in a modularised way using AI”.. III) Fostering HI as a new Transversal Skill Use Case 8: AI-based Competence Management AI-based solutions for planning, organising and managing competences can offer potential benefits for addressing dynamic changes. Learning activities can be linked to competency models, and they support the systematic evaluation of competences (learning analytics). In addition, updating existing systems during reform processes is currently a laborious task, which makes a regular update cycle of less than 5 years for the revision of qualification profiles seem unrealistic. Curriculum design and delivery are linked through digital competency models, which can also be integrated into personalised learning platforms. The development of a common language of competences could also be supported, as simplified natural language input could be implemented. Use Case 9: Open Educational Resources for Transversal Skills such as AI Literacy VET systems are increasingly faced with the challenge of equipping learners with transversal skills, which are broad capabilities that apply across various professions and industries. Among these transversal skills, AI literacy has emerged as a crucial skill set given the pervasive integration of AI across sectors. The field of AI evolves rapidly, necessitating continual updates to educational content to remain current. OER and MOOCs can be updated more swiftly and efficiently than traditional textbooks or course materials. This ensures that learners always access the most up-to-date information and learn about the latest developments in AI technology. Use Case 10: AI-based Simulations in the Workplace As workplaces become increasingly digitalised, the integration of technology into daily tasks is becoming more prevalent. Among these technological advancements, AI-based simulation systems stand out as powerful tools for workforce development and training. These systems offer authentic learning environments by replicating real-world work scenarios, enabling employees to gain hands-on experience without the risk associated with on-the-job training. This could include simulating interactions with clients, managing complex projects, or troubleshooting technical issues in a controlled environment. By practising in scenarios that reflect their actual work, employees can better understand the nuances of their roles and the challenges they may face (Aprea et al., 2020 ). Use Case 11: AI as an Assistance System for VET Teachers and Trainers In the VET system, teachers and trainers face many challenges. They are tasked with not only imparting technical skills but also integrating dynamic and educational methods. AI assistance systems for VET teachers and trainers offer solutions that could assist in streamlining content creation, facilitating assessment, and keeping educational materials up to date. As educators begin to collaborate more closely with AI, the potential for creating more adaptive, engaging, and relevant learning environments becomes increasingly tangible (Cope et al., 2021 ). 3.3 Instrument Development The online questionnaire was developed using the Qualtrics system. The online survey was structured into four distinct areas: I) collection of demographic information and background data about the respondents, such as professional role and employment sector; II) evaluation of respondents’ view on the vision of AI in VET; III) evaluation of respondents' general acceptance of AI in VET; and IV) evaluation of specific AI use cases in VET. Regarding the possible vision of AI in VET (section II), we provided the following 5 items on a 5-point Likert scale (from strongly disagree to strongly agree): Increasing equal opportunities for learners through more personalised competency development (Equal Opportunities for Learners) Using human and artificial intelligence complementarily (Hybrid Intelligence) Building an ecosystem to promote educational innovations in a digitally protected educational space (Building Ecosystem), Ensuring 'open AI' for the use of AI in vocational education to protect against the commercial interests and dependencies of large global players (Open AI for AI), Improving decision-making to align the labour market and employment system, ensuring sufficiently qualified professionals (Aligning Labour/Employment System). For general acceptance (section III), we evaluated 4 items (e.g., “AI is useful for VET”, “I support the use of AI in VET”) using the same 5-point Likert scale. The main part of the online survey focused on evaluating the specific acceptance of the 11 AI use cases in VET (section IV). Each use case was presented with the context, the role of AI and the scenario in VET. The case was evaluated using two items, "...seems useful to me (usefulness)" and "...I would use or support its use (intention to use)"; both items were again evaluated with the same 5-point Likert scale, and a space was provided for open comments. An example use case illustrating the approach is shown in the following table: Table 1 Example Use Case Use Case 1: Labour Market Intelligence Context: The digitalisation of the economy is accelerating, resulting in significant transformations in job profiles and the demand for new skills. There is an urgent need to understand these changes to proactively manage the future of work. Role of AI: Development of labour market information: labour market intelligence (LMI) AI technologies can be used to process labour market data to map the current labour market situation, analyse the effects of automation, and identify skills profiles that are in demand. LMI can predict future trends by drawing on a wide range of data sources, including real-time job postings, economic forecasts, and educational output statistics. Scenario in vocational education and training Regular understanding of current and future skill needs and labour market trends can help policy makers, employers, vocational trainers, teachers, and individuals prepare for changes in jobs and skills. They can access dashboards with tailored AI insights. This approach can also provide timely and valuable insights that allow the adaptation and improvement of the relevance and quality of training programmes in an efficient way. Labour market Intelligence (LMI) as an "intelligent" decision-making aid - ... seems useful to me, - ... I would use it or support its use, Open Comments: 4. Results 4.1 AI Vision for VET The values in the following table show the number of respondents who indicated each level of agreement or disagreement, and the mean scores and standard deviations were calculated from these numbers. Table 1 Perceived Facets of AI Vision in VET Statement Strongly Disagree Somewhat Disagree Un decided Somewhat Agree Strongly Agree M (SD) Increasing equal opportunities through personalised competency development 1 (0.9%) 5 (4.5%) 14 (12.6%) 54 (48.6%) 37 (33.3%) 4.09 (0.84) Using human and artificial intelligence complementarily (Hybrid Intelligence) 2 (1.8%) 5 (4.5%) 23 (20.7%) 47 (42.3%) 34 (30.6%) 3.95 (0.92) Building an ecosystem for educational innovations 0 (0%) 7 (6.3%) 18 (16.2%) 62 (55.9%) 24 (21.6%) 3.93 (0.79) Ensuring “Open AI” for AI use in vocational education and training 1 (0.9%) 9 (8.1%) 21 (18.9%) 51 (45.9%) 29 (26.1%) 3.88 (0.92) Improving decision-making for aligning the labour market and employment system 1 (0.9%) 8 (7.2%) 21 (18.9%) 57 (51.4%) 24 (21.6%) 3.86 (0.87) The statement “Equal opportunities” had the highest average score, indicating the strongest support among participants. The “Ecosystem” and “Hybrid Intelligence” statements also had high average scores, indicating a significant positive view of these statements by participants. The statements "Open AI" and "Decision-making" had slightly lower average scores, indicating somewhat less support compared with other statements. 4.1 VET Leaders’ General Acceptance of AI The results of this study reveal insightful perspectives on the general acceptance of AI for integration into VET, as the following table demonstrates: Table 2 VET Leaders’ General Acceptance of AI in VET Statement Strongly Disagree Somewhat Disagree Un decided Somewhat Agree Strongly Agree M (SD) AI is useful in VET 0 (0%) 4 (3.6%) 11 (9.9%) 71 (64.0%) 25 (22.5%) 4.05 (0.68) Overall, I advocate the use of AI in VET 1 (0.9%) 3 (2.7%) 12 (10.8%) 65 (58.6%) 30 (27.0%) 4.08 (0.75) Society is not prepared for the effects of AI 3 (2.7%) 11 (9.9%) 19 (17.1%) 57 (51.4%) 21 (18.9%) 3.74 (0.97) There will be unintended consequences of AI 1 (0.9%) 7 (6.3%) 27 (24.3%) 55 (49.5%) 21 (18.9%) 3.79 (0.85) For the statement “AI is useful in VET”, a large majority of participants (96 out of 111) agree that AI is useful in VET, with 25 participants strongly agreeing. This indicates a strong positive acceptance of the integration of AI in VET. The statement “Overall, I support the use of AI in VET” shows a similar result, with 95 respondents supporting its use. This suggests that respondents not only find AI useful but also support its active implementation in VET. In contrast, Statement 3 reveals scepticism about society's preparedness for the impact of AI. Seventy-eight respondents agreed that society is not prepared for the integration of AI. Furthermore, the responses to Statement 4 show that a significant number of participants (76 out of 111) believe that there will be unintended consequences of AI applications. 4.3. VET Leaders’ Acceptance of Specific AI Use Cases for VET This study examined 11 use cases for AI, each of which was assessed to determine VET leaders’ views of its usefulness and their intention to use or support its use according to technology acceptance models such as the TAM and UTAUT. As the results of the two items were quite similar, the two items (is useful, would use it) were operationally combined into a single technology acceptance construct by using the mean. Table 3 provides an overview of the results. Table 3 Acceptance of AI Use Cases in VET Use Cases Strongly Disagree Somewhat Disagree Un decided Somewhat Agree Strongly Agree M (SD) 1. Labour Market Intelligence 2 (2%) 10 (9%) 12 (11%) 69 (62%) 19 (17%) 3.83 (0.83) 2. AI-based Generation of Competence Models 7 (6%) 17 (15%) 25 (22%) 45 (41%) 38 (16%) 3.44 (1.12) 3. AI-based Learning Analytics to Evaluate Competence Profiles 3 (3%) 9 (8%) 27 (24%) 48 (43%) 25 (22%) 3.74 (0.98) 4. Personalised Learning Management System 2 (1%) 8 (7%) 16 (16%) 50 (45%) 37 (33%) 4.00 (0.93) 5. Adaptive, Cross-educational Learning Instruments 0 (0%) 1 (1%) 14 (14%) 52 (46%) 45 (41%) 4.30 (0.70) 6. AI-based Portfolio System 3 (3%) 5 (5%) 18 (16%) 43 (48%) 38 (34%) 4.00 (0.96) 7. AI-based Modularised, Integrated Assessments 6 ( %) 10 ( %) 17 (1 %) 42 (3 %) 37 (3 %) 3.85 (1.13) 8. AI-based Competence Management 2 ( %) 8 ( %) 21 (1 %) 55 (4 %) 26 (2 %) 3.90 (0.88) 9. OER for Transversal Skills (such as AI Literacy) 0 ( %) 9 ( %) 26 (2 %) 48 (4 %) 29 (2 %) 3.86 (0.89) 10. AI-based Simulations in the Workplace 6 ( %) 8 ( %) 18 (1 %) 53 (4 %) 30 (2 %) 4.02 (0.93) 11. AI Assistance for Teachers/Trainers 3 ( %) 14 (1 %) 31 (2 %) 42 (3 %) 23 (2 %) 3.81 (0.98) First, it is worth noting that all the use cases scored above the neutral threshold. The highest scores were given to Use Cases 4–7 in Domain II, establishing personalised learning. High scores were also achieved in Domain III, supporting collaboration with AI to promote HI (Use Cases 8–11). The lowest scores were given to Use Cases 1–3 in Area I, promoting the strength of links between the labour market and the education system through data-driven approaches. 5. Discussion 5.2 Vision for the Integration of AI in VET The empirical data show that there may be a remarkable consensus among VET leaders on the potential for the development of personalised skills to promote equity and inclusivity. This trend, driven by AI, highlights a shift towards personalised training to meet the diverse needs of learners, thereby promoting equity and inclusivity. This suggests that respondents view AI as a valuable tool for closing learning gaps and tailoring education to individual skills and career goals. Support for the complementary use of human and AI intelligence indicates an understanding of the synergistic potential of combining human expertise with the efficiency of AI. This may stem from the belief that while AI can effectively process and analyse large amounts of data and generate new content, human judgement and experience are crucial for context-sensitive cocreation, decision-making and ethical considerations. The high level of agreement with building ecosystems for educational innovation suggests a consensus on the need for integrated, collaborative platforms that bring together different educational stakeholders and technologies. This may reflect a growing awareness of the interconnected nature of educational challenges and the need for holistic solutions that AI-driven ecosystems could provide. The moderately high mean score, with notable disagreement, for ensuring open AI in VET may reflect concerns about privacy, intellectual property rights and the quality of open source AI tools. While open AI promotes accessibility and collaboration, it also raises questions about the standardisation and regulation of such technologies in educational settings. A lower level of agreement with improving decision-making to adapt the labour market to the employment system through AI suggests uncertainty or scepticism. This could be due to the complexity of predicting labour market trends, the changing nature of work due to automation, and the challenges of rapidly adapting educational curricula to these changes. 5.2 General Acceptance of the Adoption of AI in VET The data reflect a nuanced understanding of AI among respondents. While there is obvious enthusiasm for the beneficial effects that AI can have on VET, there also seems to be a clear recognition of the need for cautious and informed integration of AI into society. This dual perspective highlights the importance of balancing the adoption of AI technologies with thoughtful consideration of their broader societal implications and potential challenges. The strong support for the use of AI may indicate that VET leaders may recognise the added value of AI technologies in educational contexts, such as the development of personalised learning platforms or the ability to acquire practical skills through simulations. Concerns about society's readiness for AI could indicate a perception of insufficient investment in the necessary infrastructure or training as well as a lack of clear guidelines and ethical standards for dealing with AI. The history of technology is full of examples of unintended consequences. Respondents may be aware of this and therefore cautious about the potential negative impact of AI on jobs, privacy, security, and ethical standards. These findings reflect a typical uncertainty about new technologies, where positive possibilities are recognised but there are also concerns, particularly regarding moving from theory to practice (Alshami et al., 2023 ). 5.3 Acceptance of AI Use Cases for VET The use cases that gained the highest acceptance centred on personalised learning and competence development. However, significant concerns were raised in the open comment field about the handling of sensitive personal data, the potential for surveillance, and the creation of individual profiles using AI-generated data. Challenges related to the feasibility and complexity of implementation, such as integrating new personalised learning platforms with existing ones, the potential for overwhelming learners, and limited capacity in SMEs, were also noted. Additionally, doubts about the effectiveness of digital tools in practical, hands-on training environments are among the primary concerns in this area. Use cases to strengthen collaboration with AI (“HI”) continue to receive high levels of support and consensus. Concerns that the use of AI in VET is limited or not broad enough to meet the diverse needs of the sector, concerns about ethical issues, such as concerns about the handling and protection of personal data within AI systems, and concerns about the potential for bias and inequality (e.g., concerns that AI assessments could lead to distortions or biases, especially for learners who are perceived as “difficult”), seem to be reasons for negative assessments. The challenges of AI implementation in different training environments due to the diversity and number of training organisations, especially SMEs, as well as concerns about the complexity of integrating AI into existing training structures could be other barriers that are perceived by VET leaders. Mixed opinions and weaker ratings were given in response to use cases to increase the strength of links and support labour market data-driven decision-making. The data challenges of AI, scepticism about such data-driven approaches, and gaps in knowledge to assess cases appear to be the main concerns in this area. Concerns about the potential risks that are associated with the use of AI, which may include concerns about ethical issues, concerns about data protection and concerns about reliance on technology over human judgement, could be seen as general risks of AI. Furthermore, distrust in the reliability of data (e.g., job advertisements as data sources for AI) and doubts about whether these advertisements accurately reflect market needs or competencies could also be key criticisms. In addition, responses could indicate inadequacies in assessing labour market trends, such as the belief that the human element is more critical in assessing labour market needs, particularly in skilled trades, than in AI-based systems. 6 Conclusion, Limitations and Future Research VET leaders are often at the forefront of strategic decision-making and policy development within educational and training institutions. Therefore, the acceptance of AI technologies by these leaders can result in the allocation of necessary resources, the development of AI-integrated curricula and the adoption of AI-driven teaching methods. Without the support of VET leaders, it may be difficult to gain broader acceptance of initiatives for integrating AI into VET. Their leadership can significantly influence the success of AI initiatives and shape the future in the context of digital transformation. The empirical findings of this study provide a situational analysis of the acceptance of AI by VET leaders. Furthermore, the findings may also identify existing knowledge gaps among VET leaders about AI capabilities, applications, and implications. Identifying these gaps could be the first step towards developing targeted education and training programmes aimed at increasing AI literacy among VET decision makers. The results of the study are subject to several limitations that need to be mentioned. First, this research focused on the adoption and impact of AI within the VET system, specifically in Switzerland, which inherently introduces geographical limitations. Switzerland's unique educational, cultural, and regulatory landscape means that the findings of this study may not be fully transferable to or reflective of the conditions and challenges that are faced in other countries with different VET systems. In addition, this research study primarily captured the perspectives of VET leaders, which could limit the scope of understanding of the impact of AI. The perspectives of students, teachers and other key stakeholders in the education ecosystem are equally important for a comprehensive assessment of the role of AI in VET. The direct experiences and insights of these stakeholders could add additional dimensions to the analysis, particularly in understanding the day-to-day educational implications of AI integration. Furthermore, the field of AI is characterised by rapid and continuous development. Technologies that are considered cutting-edge today may soon be surpassed by new innovations, potentially rendering current perceptions and understandings obsolete. This dynamic nature of AI poses a challenge to the long-term validity of research, as findings may not accurately represent the future capabilities and impacts of emerging AI technologies. Acknowledging these limitations is essential for contextualising the findings of this study and guiding future research directions. This highlights the need for ongoing research that includes a wider range of voices from different regions and educational contexts as well as the need for adaptive research. Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the SERI, the State Secretariat for Education, Research, and Innovation in Switzerland (Case “Zukunftsmodelle Lernortkooperation”). Informed consent was obtained from all individual participants included in the study. Competing interests The author declares that she has no competing interests. Author Contribution one author who wrotes the whole manuscript Acknowledgement Many thanks to all the practitioners in VET who generously gave their time and expertise by participating in this survey. Data Availability Data is provided within the manuscript and as supplementary information files References Adeshola, I., & Adepoju, A. P. (2023). 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Retrieved from: https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1.1 . https://doi.org/10.1162/99608f92.8cd550d1 Fishkin, K., Moran, T., & Harrison, B. (1998). Embodied user interfaces: towards invisible user interfaces. In Proceedings of the Seventh Working Conference on Engineering for Human-Computer Interaction (pp. 1–18). Kluwer. Frey, C. B., & Osborne, M. (2013). The future of employment. Oxford Martin Programme on Technology and Employment, Oxford Martin School [Working Paper]. Retrieved from: https://www.oxfordmartin.ox.ac.uk/downloads/academic/future-of-employment.pdf Garone, A., Pynoo, B., Tondeur, J., Cocquyt, C., Vanslambrouck, S., Bruggeman, B., & Struyven, K. (2019). Clustering university teaching staff through UTAUT: Implications for the acceptance of a new learning management system. British Journal of Educational Technology, 50 (5). https://doi.org/10.1111/bjet.12867 Gottfredson, L. S. (1997). Mainstream science on intelligence: an editorial with 52 signatories, history, and bibliography. Intelligence, 24 (1), 13–23. https://doi.org/10.1016/S0160-2896(97)90011-8 Hamilton, A. & Wiliam, D. & Hattie, J. (2023). The future of AI in education: 13 things we can do to minimize the damage [Working Paper]. https://doi.org/10.35542/osf.io/372vr High-Level Expert Group on Artificial Intelligence (2019). A Definition of AI: Main Capabilities and Disciplines . European Commission. Retrieved from: https://www.aepd.es/sites/default/files/2019-12/ai-definition.pdf Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103 . https://doi.org/10.1016/j.lindif.2023.102274 Kim, S. (2022). Working With Robots: Human Resource Development Considerations in Human-Robot Interaction. Human Resource Management Review, 21 (1), 48–74. https://doi.org/10.1177/15344843211068810 Jarrahi, M. H. (2018). Artificial intelligence and the future of work: human-ai symbiosis in organizational decision making. Business Horizons, 61 (4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007 Jobin, A., Ienca, M. & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1 (9), 389–399. https://doi.org/10.1038/s42256-019-0088-2 Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I. & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21 (2). https://doi.org/10.1016/j.ijme.2023.100790 Massmann, C. & Hofstetter, A. (2020). AI-pocalypse now? Herausforderungen Künstlicher Intelligenz für Bildungssystem, Unternehmen und die Workforce der Zukunft. In R. A. Fürst (Hrsg.), Digitale Bildung und Künstliche Intelligenz in Deutschland (S. 167–220). Springer. Miao, F., Holmes, W., Huang, W., & Zhang, H. (2021): AI and education. Guidance for policymakers. UNESCO . Retrieved from: https://unesdoc.unesco.org/ark:/48223/pf0000376709 Nistor, N., Stanciu, D., Lerche, T., & Kiel, E. (2019). “I am fine with any technology, as long as it doesn’t make trouble, so that I can concentrate on my study”: A case study of university students’ attitude strength related to educational technology acceptance. British Journal of Educational Technology, 50 (5), 2557–2571. https://doi.org/10.1111/bjet.12832 OECD (2019). Transformative competencies for 2030. Conceptual Learning Framework . Retrieved from: https://www.oecd.org/education/2030-project/teaching-and-learning/learning/transformative-competencies/Transformative_Competencies_for_2030_concept_note.pdf Renold, U., Bolli, T., Caves, K. M., Ragetz, L., Agarwal, V., & Pusteria, F. (2015). Feasibility Study for a Curriculum Comparison in Vocational Education and Training. KOF Swiss Economic Institute, ETH Zurich. https://doi.org/10.3929/ethz-a-010713492 Seufert, S., & Guggemos, J. (2021). Neue Formen der Lernortkooperation mithilfe Künstlicher Intelligenz. In: Seufert, S., Guggemos, J., Ifenthaler, D., Ertl, H. & Seifried, J. (Hrsg.), Künstliche Intelligenz in der beruflichen Bildung: Zukunft der Arbeit und Bildung mit intelligenten Maschinen? Zeitschrift für Berufs- und Wirtschaftspädagogik (ZBW), Beiheft 31 (pp. 183–214). Franz Steiner Verlag. Seufert, S. (2023). KI-basierte Anwendungsfälle für die Lernortkooperation: Gestaltung eines digitalen Ökosystems in der Berufsbildung. Zeitschrift für Berufs- und Wirtschaftspädagogik (ZBW), 119 (2), 208–235. https://doi.org/10.25162/zbw-2023-0009 Shiohira, K. (2021). Understanding the impact of artificial intelligence on skills development. UNESCO International Centre for Technical and Vocational Education and Training. https://unesdoc.unesco.org/ark:/48223/pf0000376162.locale=en Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi.org/10.2307/30036540 Widyawati, H. (n.d.). ESCO: A tool to facilitate (online) skills matching throughout Europe [PowerPoint Slides]. SlidePlayer. Retrieved from: https://slideplayer.com/slide/15410823/ Zhang, D., Mishra, S., Brynjolfsson, E., Etchemendy, J., Ganguli, D., Grosz, B., Lyons, T., Manyika, J., Niebles, J. C., Sellitto, M., Shoham, Y., Clark, J., & Perrault, R. (2021). The AI Index 2021 Annual Report. AI Index Steering Committee. Human-Centered AI Institute, Stanford University, CA. Retrieved from: https://aiindex.stanford.edu/wp-content/uploads/2021/11/2021-AI-Index-Report_Master.pdf Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4628645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330188879,"identity":"a3a1ef4a-2d73-4d3c-8030-c88b3cb378b3","order_by":0,"name":"Sabine Seufert","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYFACHoYDEAbjAyBhw8DADOYdIEYLswEDQ0IacVoYkLQchvFwa5FvP3vwcEEFgz2/RDLj48of5xO3s7M/fsFQcwenFoMzeQmHZ5xhSJw5I5nZ8EzC7cSdzTxmFgzHnuHWwpBjcJi3jSHB4Eb+MckGoJYNh3nYDBgbDuPUIt//BqjlH4O9/Y1k9p8NCeeAWtif4dXCcANkSwMD4waJZDbGhoQDQC0Mxg/waTG48S7hMM8xicQZZx4zSzakJRsDHWbGkHAMn8NyD3/mqbGx529PZvzYYGMnu+H88ccfPtTgcRgESKDw2CQSCGlAB8wfSNUxCkbBKBgFwxoAAFckWbvJkW0oAAAAAElFTkSuQmCC","orcid":"","institution":"University of St. Gallen","correspondingAuthor":true,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Seufert","suffix":""}],"badges":[],"createdAt":"2024-06-24 08:31:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4628645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4628645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60929589,"identity":"a03b3abf-3fa3-4707-842c-1df093bff78e","added_by":"auto","created_at":"2024-07-23 16:54:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24819,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrating AI-based Use Cases for VET (adapted from Widyawati n. D.)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4628645/v1/f8d420019996e59a53ea8e23.png"},{"id":78963724,"identity":"daeae776-6925-433a-ae00-d9ecbb56b8e3","added_by":"auto","created_at":"2025-03-21 12:17:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":924579,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4628645/v1/83f91ad7-aac5-41ff-9497-f4ca008070b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence in Vocational Education and Training (VET): Evaluating VET Leaders’ Acceptance of AI in Switzerland","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is becoming increasingly important in the lives of humans, as evidenced by the increasing interactions between humans and intelligent machines (Kim, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dang, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI includes systems based on machine learning, logical programming or statistical methods that are capable of generating content, making predictions, providing recommendations, making decisions or taking action based on data analysis (European Commission, 2022, p. 10). Currently available AI methods, unlike previous rule-based approaches, do not simply replicate human rules but rather make decisions through optimization and statistical techniques (High-Level Expert Group on AI, 2019). The emergence of ChatGPT has played a significant role in bringing AI into mainstream public discourse. The capabilities of this AI application have amazed the global public while also raising concerns about its potential to take over tasks that are traditionally performed by humans (Kasneci et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The impact of AI is simultaneously transforming the employment and education systems.\u003c/p\u003e\u003cp\u003eOn the one hand, the impact of AI on the labour market could be disruptive (Mass-Mann and Hofstetter 2020; OECD 2020; Zhang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The potential for job displacement and new role creation is linked to shifts in workplace tasks and responsibilities (Shiohira \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, the transformative effects of AI could also have an impact on education. The use of AI systems in education offers many opportunities (Miao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adeshola and Adepoju \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These systems can identify individual learning needs, design personalised learning pathways, monitor progress and provide real-time feedback, thereby improving learning outcomes. This approach is particularly beneficial in vocational education and training (VET) systems that need to respond to changes in the labour market and industry needs, ensuring that the skills that are taught in these systems are up-to-date and in demand (Seufert et al., 2021). The debate surrounding ChatGPT also elucidates the potential risks of AI (Kasneci et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Bao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There has been substantial discussion about the risk that young adults might lose necessary skills because of an overreliance on AI (Hamilton et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, concerns about AI include concerns about data privacy violations due to the processing of large amounts of personal data. AI applications could produce biased results due to the use faulty or partial data sets, potentially discriminating against certain groups. In addition, the misuse of AI and concerns about its reliability and ethical application warrant attention (Floridi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Jobin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile previous studies examined the impact of AI on labour markets and education systems separately, research on harnessing the potential of AI to reform VET systems, particularly those at the interface between the labour market and the education system, remains limited. Furthermore, there is little insight into VET leaders\u0026rsquo; acceptance of AI. This gap is significant given the rapid evolution of AI and its growing relevance in different occupational sectors. This paper seeks to fill this gap.\u003c/p\u003e"},{"header":"2. Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Technology Acceptance Model (TAM)\u003c/h2\u003e \u003cp\u003eIn the TAM, \u0026ldquo;Perceived Usefulness\u0026rdquo; is defined as the degree to which a person believes that using a particular system will enhance their job performance (Davis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). When a user perceives an AI-based solution as being useful, it means that they believe that the solution will provide benefits, such as improving efficiency, accuracy, or decision-making. This belief is a central determinant in the model and is directly related to the intention of a person to use the technology. This concept is similar to \u0026ldquo;Performance Expectancy\u0026rdquo; in UTAUT, which is the degree to which an individual believes that using the technology will help them attain gains in job performance (Venkatesch et al., 2023).\u003c/p\u003e \u003cp\u003e\u0026ldquo;Behavioural Intention to Use\u0026rdquo; is a direct antecedent to \u0026ldquo;Actual System Use\u0026rdquo; in both the TAM and UTAUT. It reflects the strength of a user\u0026rsquo;s intention to perform a specific behaviour, in this case, the use of an AI-based solution. If a user states \"I would use the AI-based solution\", this response indicates a positive behavioural intention, which is often a strong predictor of actual technology use. Therefore, \u0026ldquo;Perceived Usefulness\u0026rdquo; or \u0026ldquo;Performance Expectancy\u0026rdquo; is hypothesised to positively influence \u0026ldquo;Behavioural Intention to Use\u0026rdquo;. If users believe that the AI-based solution is useful and will improve their performance (high perceived usefulness), they are more likely to have a stronger intention to use it.\u003c/p\u003e \u003cp\u003eUnderstanding these relationships is essential for VET leaders in the development and evaluation of AI-based solutions, as this understanding can inform strategies to increase acceptance and encourage use among educators and students. Furthermore, by focusing on the perceived usefulness of AI applications and actively promoting their benefits, VET leaders can positively influence the implementation strategies that are needed to integrate AI into VET in the medium and long term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2. AI-based Solutions for VET\u003c/h2\u003e \u003cp\u003eIn the current literature, AI is often conceptualised with a dual purpose: to take over simple or routine tasks from humans (often referred to as \"automation\u0026rdquo; or substitution\") and to assist humans in performing more complex tasks (collaboration in the form of \"augmentation\" of work) (Einola \u0026amp; Khoreva \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eAugmentation\u003c/em\u003e thus represents a new paradigm for the use of computers. This new paradigm leads to a change in the view of automation; that is, automation is no longer viewed as a threat but rather as an opportunity (Davenport \u0026amp; Kirby \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Instead of \u003cem\u003esubstitution\u003c/em\u003e, which has long been the focus of discussion (Frey \u0026amp; Osborne \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the term augmentation is intended to emphasise the increased interaction and complementarity of humans and machines in task performance (Davenport \u0026amp; Kirby \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The interaction between humans and intelligent assistance systems has also been referred to as \"hybrid intelligence\" (HI) for several years (Akata et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). HI refers to the constellations of mixed teams in which humans and machines work together synergistically, proactively and purposefully to jointly achieve goals (set by humans). This idea of hybridising human and machine intelligence is not novel. Doug Engelbart (1962) was influential in this regard with his concept of augmented intelligence. The strengths of humans are seen primarily in areas such as flexibility, empathy, creativity and \u0026ldquo;common sense\u0026rdquo;, which involves considering diffuse social, situational, and cultural aspects. Furthermore, the strengths of machines lie mainly in pattern recognition, data processing, probability, speed, and endurance. With the recent developments in ChatGPT and GPT-4, changes have occurred in the attribution of these strengths. As the use of large generative language models, such as ChatGPT, becomes more widespread, not only decision-making tasks but also creative activities are increasingly seen as potential application areas (Bubeck et al., 2023; Lim et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith AI agents or generative AI systems, highly efficient assistance systems will soon be widely available (Bubeck et al., 2023). These assistant systems can be used in various professions and to perform a multitude of tasks. This raises questions about the interaction between humans and AI and the competencies that are required for successful collaboration with intelligent assistant systems. According to the World Economic Forum (2023), it is anticipated that by 2027, 42% of corporate tasks will be automated, leading to a comprehensive transformation of the work landscape. Generative AI enables automation and augmentation. Even highly skilled professionals, who have previously been less affected by automation, now need to expand and renew their competencies (Candelon et al., 2023, p. 40).\u003c/p\u003e \u003cp\u003eDue to these dynamic changes, it is becoming increasingly challenging to address the overarching goal of VET, which is to equip individuals with the necessary competencies\u0026mdash;including skills, knowledge, and attitudes (action-oriented competences, Erpenbeck \u0026amp; Rosenstiel, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u0026mdash;that are required for specific occupations and meet the demands of the labour market. Coordination between the labour market and education system is becoming an increasingly important challenge during the digital transformation (Renold et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The definition of competences can act as a bridge between the labour market and the education and training systems (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDue to these dynamic developments, additional mechanisms need to be introduced into VET to strengthen the strength of this link on the one hand and to promote the development of more personalised skills in the education system on the other hand. Three main developmental directions for the use of AI can be identified. First, AI can be used to strengthen the linkages between employment and education systems through data-driven approaches from the labour market. In both the EU and the US, AI technologies are used to process labour market data, allowing for a comprehensive representation of the current state of the labour market, the identification of skill profiles that are in demand and the prediction of skills that will be in demand in the future (Mezzanzanica \u0026amp; Mercorio 2019). The ability of AI to process and represent data provides valuable insights into labour market trends. Second, within the education system, a primary goal is to establish personalised learning supported by AI (Kasneci et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This approach tailors education to individual learning needs, thereby increasing the effectiveness of vocational training. Third, use cases could focus on facilitating new forms of collaboration with AI to facilitate HI as a new required field of competence (Candelon et al., 2023).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods and Procedures","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Online Survey and Sample\u003c/h2\u003e\n \u003cp\u003eThe Swiss education system offers a promising framework for our analysis, as it is characterised by its high quality, strong links between education and the labour market, and innovative approaches to the integration of new technologies. These characteristics allow our study to gain insights that may be relevant both for Switzerland and for other countries. The overarching aim of this study is to evaluate VET leaders\u0026rsquo; acceptance of AI; the following research questions were formulated:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eWhat are the key facets of an AI vision for VET?\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWhat is the general acceptance of AI in VET?\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWhat is the acceptance of specific AI-based use cases for VET?\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eAn online survey was conducted to evaluate the general acceptance of AI and AI-based use cases for VET. The questionnaire was initially piloted with five VET experts to obtain feedback on the clarity of the questions and the applicability of the use cases during the interviews. A total of 111 VET experts participated in the online survey. Each expert is a senior vocational education and training manager who heads a specific unit in the VET system. Most of these experts work in vocational schools (principals, 43%), training companies (head of apprenticeships, 18%), professional associations (13%), employers\u0026apos; associations (6%) and cantonal VET offices (7%). The VET experts are all involved in the digitalization strategy of VET in Switzerland and are decision makers in this strategy process. The survey was conducted between mid-January and the end of February 2023.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Evaluation of the AI-based Use Cases for VET\u003c/h2\u003e\n \u003cp\u003eThe AI-based use cases refer to individual problem solutions in the VET system. The use cases were discussed with the Strategic Steering Committee for VET as part of the State Secretariat for Education, Research and Innovation (SERI)-funded research project, and an initial prioritization was carried out (Seufert, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on the results, 11 use cases were specified for further validation. The following 11 use cases investigate the multifaceted role of AI to provide solutions in each of these cases, which are categorized into three areas (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eI) Increasing the Strength of the Link with Data-driven Approaches\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 1: Intelligent Labour Market Information Systems\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAI-based methods are used to observe trends using real-time data. The main function of intelligent technologies is to process large amounts of raw data to support decision-making. Technically, such intelligent support of decision-making can be achieved by so-called Labour Market Information Systems (LMIS). These tools can help policymakers, employers, vocational trainers, teachers, and individuals prepare for changes in jobs and skills due to automation, providing timely and valuable insights that allow efficient adaptation and improvements in the relevance and quality of training programmes.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 2: AI-based Competency Models for Curriculum Design\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eCurriculum development in VET currently relies on expert-curated competency models. These development processes are becoming increasingly complex and resource intensive. The data-driven development of competency models could provide support for decision-making, at least as a complementary measure. For example, in the framework of the European ESCO initiative, a pilot project was carried out to demonstrate the linking of learning outcomes/qualifications with ESCO competences using an AI-based solution, with the aim of improving hierarchical competence taxonomies on the basis of the analyses (CEDEFOP, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Data-based competence profiles can be used to support decision-making in career development. The use of job advertisements ensures that the \u0026ldquo;language of competences\u0026rdquo; that is used by employers is included. The updating and maintenance of competency models are increasingly (partially) facilitated by automated processes, which also reduces complexity.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 3: AI-based Learning Analytics for the Evaluation of Competency Profiles\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eEvaluations in VET are complex. However, feedback loops for acquired competences are missing. In this context, AI could be used to evaluate competence profiles through learning analytics (Avila et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Learning analytics can systematically and regularly evaluate learners\u0026apos; competence profiles based on portfolio data. This can provide feedback to efficiently adjust and improve the relevance and quality of training programmes (Dillenbourg \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The integration of AI in this process is crucial, as it allows for a more nuanced and detailed analysis of learner outcomes in real time while analysing the link to labour market needs.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eII) Establishing Personalised Learning in Education System\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 5: Personalised Learning Management System\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eUnlike \u0026ldquo;traditional\u0026rdquo; Learning Management Systems, personalised learning platforms focus on the learner and their (personalised) learning process rather than on managing courses and data. Such systems could serve as an attractive learning portal, using AI capabilities to intelligently automate processes and create personalised learning experiences (e.g., learning recommendations, microcoachings, display of competence progress). These systems provide learners with an attractive and easy-to-use learning solution. AI services can be developed to promote more personalised skills development.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 4: Cross-Educational Support Instruments\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eFurther training is often required to close existing gaps in skills (e.g., in mathematics or languages). Adaptive learning technologies identify and understand individual learning patterns. The content and level of difficulty are automatically adjusted to meet the learner\u0026apos;s needs in real time. Automated feedback is timely and accurate. Adaptive support tools are particularly suitable for mastery learning. Learners do not move on to new material until they have mastered the previous and foundational content that serves as a prerequisite.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 6: AI-based Validation of Competencies with ePortfolios\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eWorking with portfolios to support the development of individual competence is a promising approach in VET. However, the activities that are involved are very time consuming. Many learners find it difficult not only to document what they have learned but also to reflect and communicate about their own resources and weaknesses (Caruso et al., 2021). Additionally, supporting learners in this process is also a major challenge for VET practitioners. The competences that are needed to support and generally coach reflection processes in learners are more pronounced among teachers due to their qualifications. An ePortfolio is used to document and reflect on one\u0026apos;s own competences and as a development portfolio to guide the learning process. Such ePortfolios could be used more widely to create a common experience space and to involve teachers to a greater extent in their coaching role (Cattaneo \u0026amp; Aprea, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 7: AI-based Modularised, Integrated Assessment Systems\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAI capabilities can be used to make open-ended questions in tests more challenging and more competence-oriented (Cope et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). AI could be used to train systems to significantly reduce the workload of examiners by assisting with marking (e.g., by suggesting grades, sorting responses, and marking clear and borderline cases). The proof of acquired competences can be (partially) automated during basic training for continuing professional development. More personalised assessment systems could be developed in a modularised way using AI\u0026rdquo;..\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIII) Fostering HI as a new Transversal Skill\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 8: AI-based Competence Management\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAI-based solutions for planning, organising and managing competences can offer potential benefits for addressing dynamic changes. Learning activities can be linked to competency models, and they support the systematic evaluation of competences (learning analytics). In addition, updating existing systems during reform processes is currently a laborious task, which makes a regular update cycle of less than 5 years for the revision of qualification profiles seem unrealistic. Curriculum design and delivery are linked through digital competency models, which can also be integrated into personalised learning platforms. The development of a common language of competences could also be supported, as simplified natural language input could be implemented.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 9: Open Educational Resources for Transversal Skills such as AI Literacy\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eVET systems are increasingly faced with the challenge of equipping learners with transversal skills, which are broad capabilities that apply across various professions and industries. Among these transversal skills, AI literacy has emerged as a crucial skill set given the pervasive integration of AI across sectors. The field of AI evolves rapidly, necessitating continual updates to educational content to remain current. OER and MOOCs can be updated more swiftly and efficiently than traditional textbooks or course materials. This ensures that learners always access the most up-to-date information and learn about the latest developments in AI technology.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 10: AI-based Simulations in the Workplace\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAs workplaces become increasingly digitalised, the integration of technology into daily tasks is becoming more prevalent. Among these technological advancements, AI-based simulation systems stand out as powerful tools for workforce development and training. These systems offer authentic learning environments by replicating real-world work scenarios, enabling employees to gain hands-on experience without the risk associated with on-the-job training. This could include simulating interactions with clients, managing complex projects, or troubleshooting technical issues in a controlled environment. By practising in scenarios that reflect their actual work, employees can better understand the nuances of their roles and the challenges they may face (Aprea et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUse Case 11: AI as an Assistance System for VET Teachers and Trainers\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIn the VET system, teachers and trainers face many challenges. They are tasked with not only imparting technical skills but also integrating dynamic and educational methods. AI assistance systems for VET teachers and trainers offer solutions that could assist in streamlining content creation, facilitating assessment, and keeping educational materials up to date. As educators begin to collaborate more closely with AI, the potential for creating more adaptive, engaging, and relevant learning environments becomes increasingly tangible (Cope et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Instrument Development\u003c/h2\u003e\n \u003cp\u003eThe online questionnaire was developed using the Qualtrics system. The online survey was structured into four distinct areas: I) collection of demographic information and background data about the respondents, such as professional role and employment sector; II) evaluation of respondents\u0026rsquo; view on the vision of AI in VET; III) evaluation of respondents\u0026apos; general acceptance of AI in VET; and IV) evaluation of specific AI use cases in VET. Regarding the possible vision of AI in VET (section II), we provided the following 5 items on a 5-point Likert scale (from strongly disagree to strongly agree):\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eIncreasing equal opportunities for learners through more personalised competency development (Equal Opportunities for Learners)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eUsing human and artificial intelligence complementarily (Hybrid Intelligence)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eBuilding an ecosystem to promote educational innovations in a digitally protected educational space (Building Ecosystem),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEnsuring \u0026apos;open AI\u0026apos; for the use of AI in vocational education to protect against the commercial interests and dependencies of large global players (Open AI for AI),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eImproving decision-making to align the labour market and employment system, ensuring sufficiently qualified professionals (Aligning Labour/Employment System).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eFor general acceptance (section III), we evaluated 4 items (e.g., \u0026ldquo;AI is useful for VET\u0026rdquo;, \u0026ldquo;I support the use of AI in VET\u0026rdquo;) using the same 5-point Likert scale. The main part of the online survey focused on evaluating the specific acceptance of the 11 AI use cases in VET (section IV). Each use case was presented with the context, the role of AI and the scenario in VET. The case was evaluated using two items, \u0026quot;...seems useful to me (usefulness)\u0026quot; and \u0026quot;...I would use or support its use (intention to use)\u0026quot;; both items were again evaluated with the same 5-point Likert scale, and a space was provided for open comments. An example use case illustrating the approach is shown in the following table:\u003c/p\u003e\n \u003cp class=\"StandardohneAbstand\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-GB\" style=\"font-family: 'Times New Roman',serif;\"\u003eTable 1\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-GB\" style=\"font-family: 'Times New Roman',serif;\"\u003e\u0026nbsp;Example Use Case\u003c/span\u003e\u003c/p\u003e\n \u003cdiv style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;border:none;border-top:solid windowtext 1.0pt;padding:1.0pt 0in 0in 0in;'\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;line-height:15.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;text-align:justify;border:none;padding:0in;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eUse Case 1: Labour Market Intelligence\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cem\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eContext:\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eThe digitalisation of the economy is accelerating, resulting in significant transformations in job profiles and the demand for new skills. There is an urgent need to understand these changes to proactively manage the future of work.\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;margin-top:6.0pt;'\u003e\u003cem\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eRole of AI: Development of labour market information: labour market intelligence (LMI)\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eAI technologies can be used to process labour market data to map the current labour market situation, analyse the effects of automation, and identify skills profiles that are in demand. LMI can predict future trends\u0026nbsp;\u003c/span\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eby\u0026nbsp;\u003c/span\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003edrawing on a wide range of data sources, including real-time job postings, economic forecasts, and educational output statistics.\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;margin-top:6.0pt;'\u003e\u003cem\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eScenario in vocational education and training\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eRegular understanding of current and future skill needs and labour market trends can help policy makers, employers, vocational trainers, teachers, and individuals prepare for changes in jobs and skills. They can access dashboards with tailored AI insights. This approach can also provide timely and valuable insights that allow the adaptation and improvement of the relevance and quality of training programmes in an efficient way.\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;margin-top:6.0pt;'\u003e\u003cem\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eLabour market Intelligence (LMI) as an \u0026quot;intelligent\u0026quot; decision-making aid\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e- ... seems useful to me,\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e- ... I would use it or support its use,\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eOpen Comments:\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cdiv style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 0in 1.0pt 0in;'\u003e\n \u003cp style='margin:0in;line-height:12.0pt;font-size:13px;font-family:\"Palatino Linotype\",serif;border:none;padding:0in;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 AI Vision for VET\u003c/h2\u003e \u003cp\u003eThe values in the following table show the number of respondents who indicated each level of agreement or disagreement, and the mean scores and standard deviations were calculated from these numbers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerceived Facets of AI Vision in VET\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly Disagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSomewhat Disagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUn\u003c/p\u003e \u003cp\u003edecided\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSomewhat Agree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrongly\u003c/p\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreasing equal opportunities through personalised competency development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003cp\u003e(48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003cp\u003e(0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsing human and artificial intelligence complementarily (Hybrid Intelligence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e(20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003cp\u003e(42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003cp\u003e(30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003cp\u003e(0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding an ecosystem for educational innovations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e(16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e(55.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e(21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003cp\u003e(0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnsuring \u0026ldquo;Open AI\u0026rdquo; for AI use in vocational education and training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003cp\u003e(45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e(26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003cp\u003e(0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproving decision-making for aligning the labour market and employment system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e(21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe statement \u0026ldquo;Equal opportunities\u0026rdquo; had the highest average score, indicating the strongest support among participants. The \u0026ldquo;Ecosystem\u0026rdquo; and \u0026ldquo;Hybrid Intelligence\u0026rdquo; statements also had high average scores, indicating a significant positive view of these statements by participants. The statements \"Open AI\" and \"Decision-making\" had slightly lower average scores, indicating somewhat less support compared with other statements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 VET Leaders\u0026rsquo; General Acceptance of AI\u003c/h2\u003e \u003cp\u003eThe results of this study reveal insightful perspectives on the general acceptance of AI for integration into VET, as the following table demonstrates:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVET Leaders\u0026rsquo; General Acceptance of AI in VET\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly Disagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSomewhat Disagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUn\u003c/p\u003e \u003cp\u003edecided\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSomewhat Agree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrongly\u003c/p\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI is useful in VET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003cp\u003e(64.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003cp\u003e(0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall, I advocate the use of AI in VET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e(10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003cp\u003e(58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e(27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003cp\u003e(0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociety is not prepared for the effects of AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e(17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003cp\u003e(0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThere will be unintended consequences of AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e(49.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the statement \u0026ldquo;AI is useful in VET\u0026rdquo;, a large majority of participants (96 out of 111) agree that AI is useful in VET, with 25 participants strongly agreeing. This indicates a strong positive acceptance of the integration of AI in VET. The statement \u0026ldquo;Overall, I support the use of AI in VET\u0026rdquo; shows a similar result, with 95 respondents supporting its use. This suggests that respondents not only find AI useful but also support its active implementation in VET. In contrast, Statement 3 reveals scepticism about society's preparedness for the impact of AI. Seventy-eight respondents agreed that society is not prepared for the integration of AI. Furthermore, the responses to Statement 4 show that a significant number of participants (76 out of 111) believe that there will be unintended consequences of AI applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3. VET Leaders\u0026rsquo; Acceptance of Specific AI Use Cases for VET\u003c/h2\u003e \u003cp\u003eThis study examined 11 use cases for AI, each of which was assessed to determine VET leaders\u0026rsquo; views of its usefulness and their intention to use or support its use according to technology acceptance models such as the TAM and UTAUT. As the results of the two items were quite similar, the two items (is useful, would use it) were operationally combined into a single technology acceptance construct by using the mean. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides an overview of the results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcceptance of AI Use Cases in VET\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly Disagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSomewhat Disagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUn\u003c/p\u003e \u003cp\u003edecided\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSomewhat Agree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrongly\u003c/p\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Labour Market Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e(9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e(11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003cp\u003e(62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003cp\u003e(0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. AI-based Generation of Competence Models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e(22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003cp\u003e(1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. AI-based Learning Analytics to Evaluate Competence Profiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003cp\u003e(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e(22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003cp\u003e(0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Personalised Learning Management System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003cp\u003e(45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003cp\u003e(0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Adaptive, Cross-educational Learning Instruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003cp\u003e(46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003cp\u003e(0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. AI-based Portfolio System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003cp\u003e(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e(34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003cp\u003e(0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. AI-based Modularised, Integrated Assessments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e(1 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003cp\u003e(3 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e(3 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003cp\u003e(1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. AI-based Competence Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(1 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e(4 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e(2 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003cp\u003e(0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. OER for Transversal Skills (such as AI Literacy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e(2 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003cp\u003e(4 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e(2 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003cp\u003e(0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. AI-based Simulations in the Workplace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e(1 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003cp\u003e(4 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e(2 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003cp\u003e(0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. AI Assistance for Teachers/Trainers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(1 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e(2 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003cp\u003e(3 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e(2 %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003cp\u003e(0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFirst, it is worth noting that all the use cases scored above the neutral threshold. The highest scores were given to Use Cases 4\u0026ndash;7 in Domain II, establishing personalised learning. High scores were also achieved in Domain III, supporting collaboration with AI to promote HI (Use Cases 8\u0026ndash;11). The lowest scores were given to Use Cases 1\u0026ndash;3 in Area I, promoting the strength of links between the labour market and the education system through data-driven approaches.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Vision for the Integration of AI in VET\u003c/h2\u003e \u003cp\u003eThe empirical data show that there may be a remarkable consensus among VET leaders on the potential for the development of personalised skills to promote equity and inclusivity. This trend, driven by AI, highlights a shift towards personalised training to meet the diverse needs of learners, thereby promoting equity and inclusivity. This suggests that respondents view AI as a valuable tool for closing learning gaps and tailoring education to individual skills and career goals. Support for the complementary use of human and AI intelligence indicates an understanding of the synergistic potential of combining human expertise with the efficiency of AI. This may stem from the belief that while AI can effectively process and analyse large amounts of data and generate new content, human judgement and experience are crucial for context-sensitive cocreation, decision-making and ethical considerations. The high level of agreement with building ecosystems for educational innovation suggests a consensus on the need for integrated, collaborative platforms that bring together different educational stakeholders and technologies. This may reflect a growing awareness of the interconnected nature of educational challenges and the need for holistic solutions that AI-driven ecosystems could provide.\u003c/p\u003e \u003cp\u003eThe moderately high mean score, with notable disagreement, for ensuring open AI in VET may reflect concerns about privacy, intellectual property rights and the quality of open source AI tools. While open AI promotes accessibility and collaboration, it also raises questions about the standardisation and regulation of such technologies in educational settings. A lower level of agreement with improving decision-making to adapt the labour market to the employment system through AI suggests uncertainty or scepticism. This could be due to the complexity of predicting labour market trends, the changing nature of work due to automation, and the challenges of rapidly adapting educational curricula to these changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 General Acceptance of the Adoption of AI in VET\u003c/h2\u003e \u003cp\u003eThe data reflect a nuanced understanding of AI among respondents. While there is obvious enthusiasm for the beneficial effects that AI can have on VET, there also seems to be a clear recognition of the need for cautious and informed integration of AI into society. This dual perspective highlights the importance of balancing the adoption of AI technologies with thoughtful consideration of their broader societal implications and potential challenges. The strong support for the use of AI may indicate that VET leaders may recognise the added value of AI technologies in educational contexts, such as the development of personalised learning platforms or the ability to acquire practical skills through simulations. Concerns about society's readiness for AI could indicate a perception of insufficient investment in the necessary infrastructure or training as well as a lack of clear guidelines and ethical standards for dealing with AI. The history of technology is full of examples of unintended consequences. Respondents may be aware of this and therefore cautious about the potential negative impact of AI on jobs, privacy, security, and ethical standards. These findings reflect a typical uncertainty about new technologies, where positive possibilities are recognised but there are also concerns, particularly regarding moving from theory to practice (Alshami et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Acceptance of AI Use Cases for VET\u003c/h2\u003e \u003cp\u003eThe use cases that gained the highest acceptance centred on personalised learning and competence development. However, significant concerns were raised in the open comment field about the handling of sensitive personal data, the potential for surveillance, and the creation of individual profiles using AI-generated data. Challenges related to the feasibility and complexity of implementation, such as integrating new personalised learning platforms with existing ones, the potential for overwhelming learners, and limited capacity in SMEs, were also noted. Additionally, doubts about the effectiveness of digital tools in practical, hands-on training environments are among the primary concerns in this area.\u003c/p\u003e \u003cp\u003eUse cases to strengthen collaboration with AI (\u0026ldquo;HI\u0026rdquo;) continue to receive high levels of support and consensus. Concerns that the use of AI in VET is limited or not broad enough to meet the diverse needs of the sector, concerns about ethical issues, such as concerns about the handling and protection of personal data within AI systems, and concerns about the potential for bias and inequality (e.g., concerns that AI assessments could lead to distortions or biases, especially for learners who are perceived as \u0026ldquo;difficult\u0026rdquo;), seem to be reasons for negative assessments. The challenges of AI implementation in different training environments due to the diversity and number of training organisations, especially SMEs, as well as concerns about the complexity of integrating AI into existing training structures could be other barriers that are perceived by VET leaders.\u003c/p\u003e \u003cp\u003eMixed opinions and weaker ratings were given in response to use cases to increase the strength of links and support labour market data-driven decision-making. The data challenges of AI, scepticism about such data-driven approaches, and gaps in knowledge to assess cases appear to be the main concerns in this area. Concerns about the potential risks that are associated with the use of AI, which may include concerns about ethical issues, concerns about data protection and concerns about reliance on technology over human judgement, could be seen as general risks of AI. Furthermore, distrust in the reliability of data (e.g., job advertisements as data sources for AI) and doubts about whether these advertisements accurately reflect market needs or competencies could also be key criticisms. In addition, responses could indicate inadequacies in assessing labour market trends, such as the belief that the human element is more critical in assessing labour market needs, particularly in skilled trades, than in AI-based systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion, Limitations and Future Research","content":"\u003cp\u003eVET leaders are often at the forefront of strategic decision-making and policy development within educational and training institutions. Therefore, the acceptance of AI technologies by these leaders can result in the allocation of necessary resources, the development of AI-integrated curricula and the adoption of AI-driven teaching methods. Without the support of VET leaders, it may be difficult to gain broader acceptance of initiatives for integrating AI into VET. Their leadership can significantly influence the success of AI initiatives and shape the future in the context of digital transformation. The empirical findings of this study provide a situational analysis of the acceptance of AI by VET leaders. Furthermore, the findings may also identify existing knowledge gaps among VET leaders about AI capabilities, applications, and implications. Identifying these gaps could be the first step towards developing targeted education and training programmes aimed at increasing AI literacy among VET decision makers.\u003c/p\u003e \u003cp\u003eThe results of the study are subject to several limitations that need to be mentioned. First, this research focused on the adoption and impact of AI within the VET system, specifically in Switzerland, which inherently introduces geographical limitations. Switzerland's unique educational, cultural, and regulatory landscape means that the findings of this study may not be fully transferable to or reflective of the conditions and challenges that are faced in other countries with different VET systems. In addition, this research study primarily captured the perspectives of VET leaders, which could limit the scope of understanding of the impact of AI. The perspectives of students, teachers and other key stakeholders in the education ecosystem are equally important for a comprehensive assessment of the role of AI in VET. The direct experiences and insights of these stakeholders could add additional dimensions to the analysis, particularly in understanding the day-to-day educational implications of AI integration. Furthermore, the field of AI is characterised by rapid and continuous development. Technologies that are considered cutting-edge today may soon be surpassed by new innovations, potentially rendering current perceptions and understandings obsolete. This dynamic nature of AI poses a challenge to the long-term validity of research, as findings may not accurately represent the future capabilities and impacts of emerging AI technologies. Acknowledging these limitations is essential for contextualising the findings of this study and guiding future research directions. This highlights the need for ongoing research that includes a wider range of voices from different regions and educational contexts as well as the need for adaptive research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the SERI, the State Secretariat for Education, Research, and Innovation in Switzerland (Case \u0026ldquo;Zukunftsmodelle Lernortkooperation\u0026rdquo;). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe author declares that she has no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eone author who wrotes the whole manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eMany thanks to all the practitioners in VET who generously gave their time and expertise by participating in this survey.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript and as supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeshola, I., \u0026amp; Adepoju, A. P. (2023). The opportunities and challenges of ChatGPT in education. 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Retrieved from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aiindex.stanford.edu/wp-content/uploads/2021/11/2021-AI-Index-Report_Master.pdf\u003c/span\u003e\u003cspan address=\"https://aiindex.stanford.edu/wp-content/uploads/2021/11/2021-AI-Index-Report_Master.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Technology Acceptance Model, Hybrid Intelligence, AI Leadership","lastPublishedDoi":"10.21203/rs.3.rs-4628645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4628645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) is having a transformative impact on both the labour market and education systems. Vocational education and training (VET) leaders play a crucial role in shaping the future of education and training by guiding strategic decisions and policy directions; without VET leaders, efforts to integrate AI into VET risk facing significant barriers to wider uptake. Research that is focused on harnessing AI capabilities to innovate VET systems and the leadership required are rare. Therefore, the aim of this study is to assess VET leaders' acceptance of AI (TAM, Davis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; UTAUT, Venkatesch et al., 2003). An online survey was conducted in February 2023 to assess the general acceptance of AI and AI-based use cases for VET. A total of 111 VET experts participated in the online survey. Each expert is a senior VET manager who leads a specific unit in the VET system. The empirical data suggest that there may be widespread agreement among VET leaders about the ability of personalised competence development to improve equity and inclusivity. However, use cases for increasing the strength of the links between the labour market and education system with data-driven approaches are less widely accepted. The empirical findings of this study provide a situational analysis of the acceptance of AI by VET leaders. Furthermore, the findings can also elucidate existing knowledge gaps of VET leaders regarding AI capabilities, applications, and implications.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence in Vocational Education and Training (VET): Evaluating VET Leaders’ Acceptance of AI in Switzerland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 16:54:12","doi":"10.21203/rs.3.rs-4628645/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d321af0d-e161-4b0c-8b7f-cf6a3e79d382","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-21T12:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 16:54:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4628645","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4628645","identity":"rs-4628645","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}