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Open research questions in Vocational Education and Training

26 unresolved questions extracted from the limitations and future-work sections of 162 Vocational Education and Training papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • for Researchers This study contributes empirical evidence on the adoption of GenAI in a South Asian tertiary education context, enriching the body of knowledge on technology acceptance, digital pedagogy, and GenAI in education policy. By revealing the pictures of relevant variables of Generative Artificial Intelli- gence in Education (GenAIEd) in a unique context, such as Bangladesh, the findings have implications for similar situations. They can inform others about possible challenges and the usefulness of GenAIEd. Teachers and students are both moderately familiar with GenAI. The teach- ers primarily use it to prepare courses and materials, while students sporadi- cally engage with GenAI, mainly for academic problem-solving, and they em- phasize its role in personalized, learner-centered learning. GenAI familiarity is found to be a strong predictor of usage frequency. However, teachers ex- press concerns about the reliability of GenAI, ethical implications, and the potential for deskilling. While the benefits and usefulness dominate, possible challenges and threats are marginally associated with the future adoption and use of GenAI. This finding is unique because, despite the overpowering ‘ease of use’ of the TAM model, ‘benefits or usefulness’ of the TTF model, chal- lenges, and threats have been found as catalysts for GenAI adoption. Practitioners are to utilize GenAI to support, rather than replace, their teach- ing expertise. They should also encourage students to strike a balance be- tween GenAI-assisted learning, critical thinking, and independent work. Fur- thermore, the institutions should introduce guidelines to ensure the ethical use of GenAI and academic integrity. Researchers should explore the longitudinal effects of GenAI adoption on learning outcomes and skill development. They can also conduct compara- tive studies across different universities and disciplines. Investigating the role of GenAI in inclusive education and support for learners from disadvantaged backgrounds also demands research focus. Impact on Society The findings highlight how GenAI can transform higher education in Bang- ladesh and similar contexts. It shows the importance of addressing the risks of overreliance and the unethical use of GenAI for effective learning. A bal- anced adoption could strengthen human–technology collaboration in educa- tion. On the other hand, it has revealed the aspects of GenAI, preferred by educators, that AI developers should consider.

    Generative Artificial Intelligence in Tertiary Level Education in Bangladesh: Practices, Benefits, Challenges, and Prospects · 2026 · DOI

  • Keywords Further studies should examine hybrid learning models that integrate GenAI with human expertise. Cross-cultural perspectives on GenAI in education remain another area of study. Furthermore, studies should be carried out to develop frameworks for maintaining academic authenticity while GenAI is being used in education. GenAI in education, challenges and prospects of GenAI in tertiary education, adoption of AI in education, Technology Acceptance Model, task-technology fit 2 Al Mamun & Walid INTRODUCTION The advancement of technology has significantly influenced higher education worldwide. Artificial Intelligence (AI) is a technology that has had a notable and innovative impact on educational change. AI is not a new concept, but its use in education, popularly known as Artificial Intelligence in Education (AIEd), has gained unprecedented attention following the public release of Generative Artificial Intelligence (GenAI) models. These models can generate descriptive, narrative, and analytical content instantly. The basic versions are easily accessible, which could positively influence traditional teaching and learning practices (Mulyani et al., 2025). For example, this can make class preparation easier and faster and provide more understandable, student-friendly, and individualized (Hrastinski et al., 2019) instructions, thereby engaging students more effectively in learning activities. Bangladesh is not an exception; its tertiary education has been expanding rapidly, facing challenges and incorporating the benefits of ever-growing technological tools, including GenAI. GenAI platforms, such as ChatGPT, OpenAI, Gemini, and Deepseek, are now utilized by billions of users worldwide due to their widespread adoption and educational benefits. These tools respond to questions and queries, reduce the time spent preparing assignments and materials, and provide access to relevant data sources (Pavlenko & Syzenko, 2024). They reduce the time and effort required for academic preparation, provide feedback, analyze data, support problem-solving, translate texts, and offer personalized learning experiences (Oranga, 2023). Overall, these abilities enable academic work to be completed faster, more efficiently, and at a lower cost. However, the integration of GenAi in education (GenAIEd) is still in its early stages (K. Zhang & Aslan, 2021). To understand the effectiveness and potential of GenAI in education, exploring contextual challenges and constructs is recommended in technology adoption models, such as the Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) Model. The integration of Generative AI in education (GenAIEd) presents several challenges, particularly a lack of localized evidence on its effectiveness and a need to better understand the perceptions of key users, such as educators and learners.

    Generative Artificial Intelligence in Tertiary Level Education in Bangladesh: Practices, Benefits, Challenges, and Prospects · 2026 · DOI

  • First, although the convenience sample of 332 teachers provides valuable insights, it was drawn from a single region (Al Ain) and therefore may not be generalizable to all teachers in the UAE or internationally.

    AI transformation in education: Examining teachers’ perceptions using an integrated TAM-TPACK-GenAI framework · 2026 · DOI

  • While existing literature has primarily focused on the labor market impacts of automation, few studies have investigated its direct effects on VET curricula.

    Estimating the Impact of Industry 4.0 Automation on Curricular Competence Indicators in Brazilian Vocational Education and Training: A Mixed-Methods AI-Supported Analysis · 2026 · DOI

  • The relevance of our argument here is that in an automated economy, since repetitive work which degrades and trivialises human life will increasingly be undertaken by machines, such work as remains to be done by men will be highly technical in character, or of a professional or quasi-professional nature which makes more demands on native human abilities. How far these shifts of duties from highly educated professional workers can be pushed down the scale of ability remains to be seen.

    Vocational education in an automated economy · 1963 · DOI

  • The comparison between social shaping and institutional support produced results that need to be weighed against genuinely mixed evidence in the literature.

    Appropriating AI Tools in the Academic Workplace: Evaluation, Adaptation, and Incorporation among College Teachers · 2026 · DOI

  • However, through the lens of the Technology Acceptance Model, this suggests that Perceived Ease of Use is high, but the Perceived Usefulness of GenAI remains mostly limited to administrative tasks rather than pedagogical delivery.

    Generative Artificial Intelligence in Tertiary Level Education in Bangladesh: Practices, Benefits, Challenges, and Prospects · 2026 · DOI

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26 open questions have been extracted from the limitations and future-work passages of 162 Vocational Education and Training papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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