Summary of selected articles (N = 12)
| Authors | Conceptual/theoretical framework | Methodology/Methods | Participants/setting | Beliefs and attitudes towards AI | Innovative integration of AI |
|---|---|---|---|---|---|
| Bekiaridis and Attwell (2024) | AI in education: the DigCompEdu | Mixed methods Surveys and interviews | Practicing educators in VET, educational policymakers, AI developers and academic experts in the field in the European Union (specific number not mentioned) | X | – |
| Chang and Hwang (2024) | Robot teaching assistant-supported learning | Mixed methods experimental design Research intervention Survey and interviews | A total of 103 third-year nursing students from two classes at a vocational university in Taiwan | – | X |
| Kong et al. (2024) | AI support for intelligent learning framework; smart learning model | Mixed methods experimental design Entropy weight and fuzzy comprehensive evaluation | A total of 90 students (45 in the experimental class and 45 in the control class) | – | X |
| Moreno and Petko (2024) | AI in education, motivation for teaching and agency | Quantitative Survey | A total of 183 student teachers at two universities in Switzerland | X | – |
| Nyaaba and Zhaı (2024) | Diffusion of innovation theory | Qualitative Professional development Webinar Interviews | A total of 307 teachers from multiple institutions and departments (including polytechnics) in Ghana | X | – |
| Seufert (2024) | Technology acceptance model; AI-based solutions for VET | Quantitative Survey | A total of 111 senior VET experts (e.g. manager, school principals or heads) in Switzerland | X | – |
| Hall et al. (2023) | Unclear | Markov chains and machine learning methodology | Anonymized data from students enrolled in VET programs in Australia | – | X |
| Ridzuan and Junaidi (2023) | Technology acceptance model | Quantitative Survey | A total of 82 students enrolled at a community college within TVET in Malaysia | X | – |
| Liu et al. (2020) | The impact of AI on VET, focusing on both challenges and opportunities | Quantitative Survey | A total of 302 teachers and students (specific number for each group not specified) at a vocational college in China | X | – |
| Ma (2022) | The potential of AI to enhance educational outcomes by leveraging student feedback in course selection | Experimental Data validation and processing; feature extraction and classification; performance metrics | No participants were involved in the study Context not specified | – | X |
| Rott et al. (2022) | The integration of AI into VET | Mixed methods Interviews Survey | 12 vocational school teachers were interviewed, and 476 apprentices were surveyed at vocational schools in Germany | – | X |
| Souza et al. (2022) | Computational thinking; educational robotics | Mixed methods experimental design | A total of 36 students and 18 teachers in the TVE high school in Brazil | – | X |
| Authors | Conceptual/theoretical framework | Methodology/Methods | Participants/setting | Beliefs and attitudes towards AI | Innovative integration of AI |
|---|---|---|---|---|---|
| AI in education: the DigCompEdu | Mixed methods | Practicing educators in VET, educational policymakers, AI developers and academic experts in the field in the European Union (specific number not mentioned) | X | – | |
| Robot teaching assistant-supported learning | Mixed methods experimental design | A total of 103 third-year nursing students from two classes at a vocational university in Taiwan | – | X | |
| AI support for intelligent learning framework; smart learning model | Mixed methods experimental design | A total of 90 students (45 in the experimental class and 45 in the control class) | – | X | |
| AI in education, motivation for teaching and agency | Quantitative | A total of 183 student teachers at two universities in Switzerland | X | – | |
| Diffusion of innovation theory | Qualitative | A total of 307 teachers from multiple institutions and departments (including polytechnics) in Ghana | X | – | |
| Technology acceptance model; AI-based solutions for VET | Quantitative | A total of 111 senior VET experts (e.g. manager, school principals or heads) in Switzerland | X | – | |
| Unclear | Markov chains and machine learning methodology | Anonymized data from students enrolled in VET programs in Australia | – | X | |
| Technology acceptance model | Quantitative | A total of 82 students enrolled at a community college within TVET in Malaysia | X | – | |
| The impact of AI on VET, focusing on both challenges and opportunities | Quantitative | A total of 302 teachers and students (specific number for each group not specified) at a vocational college in China | X | – | |
| The potential of AI to enhance educational outcomes by leveraging student feedback in course selection | Experimental | No participants were involved in the study | – | X | |
| The integration of AI into VET | Mixed methods | 12 vocational school teachers were interviewed, and 476 apprentices were surveyed at vocational schools in Germany | – | X | |
| Computational thinking; educational robotics | Mixed methods experimental design | A total of 36 students and 18 teachers in the TVE high school in Brazil | – | X |
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