Education in the AI era - Part #3 : AI in VE
Vocational education stands apart from K12 and higher education by focusing on practical skills that lead directly to employment. While K12 follows standardised curricula aimed at broad, long-term knowledge and competencies, and higher education balances academic and research priorities, vocational programs tend to prioritise hands-on, practical training for specific careers.
Another key difference is the immediacy of outcomes of vocational education relative to the other areas: K12 education fosters intellectual development over 12+ years, and higher education often spans three or more years, emphasising a primary focus on deep theoretical study and future career preparation. In contrast, vocational education is streamlined for near-term job readiness, typically lasting just a few months to a couple of years, offering targeted training aligned with specific industry needs.
Vocational training is also closely tied to industry certifications and standards (both formal and informal, in part shaped by the training delivered by the provider), ensuring that students acquire skills that are immediately applicable in the workplace. Technology adoption in vocational settings is driven by a strong focus on return on investment and measurable outcomes, such as improved job placement rates and reduced time-to-competency. Furthermore, industry partnerships play a critical role, with institutions working closely with businesses and trade associations to keep curricula aligned with evolving trends. Finally, vocational programs serve a diverse audience, including young adults entering the workforce and older individuals seeking career changes, requiring a flexible and adaptable learning approach. In this context, personalised pathways and broader AI-driven solutions have the potential to make a significant impact.
The blog is split into the following sections:
Let's dive in!
Is AI gaining ground in Vocational Education?
Similar to other educational sectors, AI in vocational education is still in its infancy. This is particularly true in traditional vocational education fields like carpentry, plumbing and nursing, where hands-on skills require practical, real-world applications. In Europe, data on AI adoption rates within these sectors remains sparse and infrequently reported, likely reflecting the slow pace of AI integration in these fields. Nevertheless, a recent 2023 survey among Swiss vocational education leaders provided valuable insights into how AI is being perceived in these more traditional sectors. AI is generally viewed as having the potential to support activities like personalised learning and grading assessments, both of which could improve efficiency in vocational training in ways that are similar to possible applications in higher education.
However, there is still a significant knowledge gap on how AI applications could directly support the desired practical, hands-on learning that is at the core of traditional vocational programs. For example, while AI could optimise the delivery of theoretical parts of courses, integrating AI-enabled experiences into the manual, skills-based aspects remains a challenge.
While traditional vocational education lags in AI adoption due to these practical constraints, fully online vocational schools like YouSchool (FR), Ucademy (ES), and various coding bootcamps, such as Ironhack (ES), BloomTech (US) and Le Wagon (FR), are paving the way with faster, more seamless integration of AI tools.
These online platforms are inherently more adaptable to AI-driven solutions because they operate entirely in digital environments, making them fertile ground for implementing personalised learning paths, real-time assessments, and data-driven insights. For instance, YouSchool is pioneering the use of AI to transform vocational education by enhancing both learning and job placement processes. YouSchool utilises AI tools to convert written course materials into audio, making learning more accessible, especially for students who may struggle with traditional text-heavy formats. Additionally, interactive AI-powered exercises replace standard multiple-choice questions, offering a more engaging and practical learning experience suited to fields like nursing, where hands-on skills are essential. Add to that, AI-driven bots play a central role in YouSchool’s job placement services. These bots guide students through every step of the job application process, from creating resumes and writing motivation letters to identifying relevant job listings. By automating these tasks, YouSchool ensures that students not only complete their vocational training but are also supported in their transition into the workforce.
Although AI adoption may be more prominent in fully digitised vocational schools, vocational learners in traditional settings may also be independently using AI tools (e.g., adaptive learning platforms, AI-driven simulations, and personalised study aids) to enhance their learning experience. They may be doing this even more actively than their peers in higher education or K-12 environments due to the shorter duration of vocational programs and the reduced time available to achieve competency, which encourages learners to maximise efficiency through AI tools.
Moreover, vocational education is intricately tied to industries that are rapidly integrating AI into their operations, making it essential for students to stay ahead by using these technologies as part of their training.
Where are AI startups focusing their time and attention within Vocational Education?
The landscape of AI in vocational education exhibits both similarities to and divergences from trends seen in higher education. While tools designed for automated grading and exam integrity—used in higher education—find their place in the more theoretical components of vocational training, the real distinction lies in the application of AI in interactive and immersive learning experiences.
Many of the AI tools utilised in higher education for tasks like automated grading and exam integrity are equally applicable in vocational learning environments, particularly in fields that require a blend of theoretical knowledge and practical application. Startups such as Graide (UK), Edtake (CH), and Digiexam (SE) provide solutions that vocational educators can leverage to efficiently create training content, manage assessments and maintain the integrity of certification processes. These tools are particularly valuable in vocational programs where theoretical understanding underpins practical skills—such as in healthcare or technical trades where exams are crucial for licensure.
sAInaptic (UK) addresses key challenges within apprenticeship and wider training models, focusing closely on knowledge assessments and progress tracking within these programmes. Dr Rajeshwari Iyer, sAInaptic's co-founder and CEO, explained their focus:
"We focus on the number one issue within apprenticeships and similar schemes: drop-out rates. We address learner motivation by helping students get live feedback on their progress via automated feedback on text-based assessments that form part of e-portfolio submissions, combined with easy communication of ongoing learning priorities, staying closely aligned with learning goals and standards. This helps students understand what they need to do to progress in their course, in real-time, rather than needing to wait 4-6 weeks for their assessor to provide feedback."
Rajeshwari Iyer
Founder and CEO at Sainaptic
However, the most significant difference in AI application between higher education and vocational learning emerges in the realm of interactive and immersive technologies. Vocational education uniquely benefits from AI-driven personalised learning paths and immersive simulations, which are essential for providing hands-on experience in a controlled, risk-free environment. Platforms like Virti (UK) use immersive, AI-driven virtual reality to simulate complex work environments, helping learners practice and refine skills in high-stakes fields like healthcare while another platform, Interplay Learning (US), offers AI-powered simulations for hands-on trades like HVAC, electrical work, and plumbing—enabling students to practise real-world skills in a safe, controlled virtual environment.
Though AI integration is varied within such learning experiences, it is likely that the coming wave of VR and AR content will be AI-generated, adaptive to learner’s priorities and responsive to areas on which learners need to focus. And what’s more, these tools are not merely supplementary; they can become essential components of vocational training, reducing costs of access to previously high-cost, practical learning experiences, easily updated to reflect the latest developments in relevant fields. Indeed, the practices delivered could dynamically vary based on the employer or pathways being pursued by the student- for example, if Jaguar Land Rover is hiring mechanics and want to make sure learners are trained in ways that align with their practices and standards, they could integrate within simulation experiences for both assessment and training purposes. They could also lean on assessments of learners over time when making their hiring decisions. The potential of AI in these fields reinforces its importance and motivates vocational learners to familiarise themselves with these technologies, ultimately boosting their employability and preparing them for the increasingly technology-driven industries they will enter.
Does AI make financial sense for Vocational Education providers?
As AI continues to evolve and integrate into various educational sectors, vocational learning environments face unique incentives and constraints when it comes to adopting this technology. Unlike traditional educational institutions, vocational education providers tend to have more direct and flexible funding models, often being paid per student enrolled or per course completed. This relative flexibility allows vocational programs to commit to technology spending without some of the bureaucratic hurdles that often slow down public or higher education institutions. However, the question remains: are these schools willing to invest in AI-driven solutions, and if so, what are the incentives driving that decision?
One of the key advantages vocational schools have is less reliance on long-term, constrained funding models, allowing them to make faster, more agile decisions. For platforms like OpenClassrooms (FR), which are fully online, it makes sense to use AI to streamline their operations, reducing the student-to-teacher ratio and optimising learning paths for students. By leveraging AI for tasks such as grading, personalised content delivery, and even career advice, these schools are able to lower operational costs while maintaining—or even improving—education quality. The ability to reduce the number of human teachers while still providing individualised attention makes AI an attractive cost-saving measure, particularly in online environments.
However, for vocational programs that rely on the completion of specific skills-based certifications, the financial incentive to adopt AI extends beyond cost reduction. In traditional schools, there’s often a balancing act between keeping students enrolled and moving them toward graduation. But vocational schools operate differently: the faster a student can complete their training and become employable, the better it is for both the school and the industry (assuming fixed course fees). AI enables these schools to accelerate learning through adaptive content and simulations, meaning students can finish their programs faster. For the schools, this means they can enrol more students over a given period and cycle more learners through certification, leading to increased revenue potential.
Ucademy’s (ES) approach is a prime example of this. As Ramiro, the founder and CEO of Ucademy, explains: “The AI allows us to dynamically adjust the study plan and generate thousands of practice tests, which the teacher only needs to review and approve. This has allowed us to scale without a significant increase in teaching staff, maintaining efficiency and speeding up the learning process.”
This capability not only optimises each student’s learning path but also accelerates training completion. As a result, the platform can handle a higher volume of students, thereby increasing throughput and revenue without compromising educational quality. In more hands-on fields like automotive repair or plumbing, AI-powered tools like Interplay Learning can provide immersive simulations that allow students to practise hands-on skills in a virtual environment. By helping learners gain competencies faster and more efficiently, even traditional vocational schools can support a larger volume of students with less strain on physical resources. For both traditional schools and online platforms, the ability to use AI to deliver high-quality education at scale should become a significant part of their strategy.
“The AI allows us to dynamically adjust the study plan and generate thousands of practice tests, which the teacher only needs to review and approve. This has allowed us to scale without a significant increase in teaching staff, maintaining efficiency and speeding up the learning process.”
Ramiro Zandrino
Founder and CEO at Ucademy
While some may argue that accelerating student graduation could result in reduced revenue from tuition fees, vocational schools stand to gain from lower dropout rates and higher job placement rates. Both of these metrics improve the school's reputation and allow it to attract more students in the long run. Additionally, many vocational programs are performance-based, meaning funding often hinges on how effectively they can equip students with industry-ready skills. Ucademy’s decision to invest in AI reflects not just a focus on immediate cost savings, but also a broader strategic vision. “By focusing on developing these tools, we position ourselves to offer even greater value to our students in the future,” he says. For vocational learning companies like Ucademy, AI is more than just a tool– it’s a critical investment in future competitiveness and operational sustainability.
How are AI startups succeeding in Vocational Education?
AI startups are making strides in selling to vocational education, reshaping how students acquire skills and how institutions deliver training. The success of these startups hinges on their ability to address specific industry needs, provide scalable solutions, and ultimately enhance both educational outcomes and employability. Being ‘AI-driven’ will not suffice - they will need to prove impact and efficacy like other startups looking to support learners.
Here’s a closer look at the strategies that are helping AI startups thrive in the vocational learning space:
Co-sponsoring Certifications with Certification Bodies: AI startups can significantly boost their success in selling to vocational learning schools by partnering with certification bodies or industry licensing agencies to co-develop or align their tools with recognised industry standards. This strategy not only ensures that students are trained according to the latest industry requirements but also enhances their employability by preparing them for essential certifications. For vocational schools, this partnership provides a clear value proposition—they can offer programs that directly align with industry-recognised credentials, increasing both student enrollment and graduation rates. Startups like MindEdge (US) have leveraged this strategy by partnering with certification bodies to integrate their AI-driven tools into courses that prepare students for industry exams. By co-sponsoring certifications, these startups become essential partners for schools, helping students meet the qualifications necessary to enter high-demand fields like plumbing, HVAC, or electrical work, while also positioning their tools as integral to achieving student success and improving school outcomes.
Customising AI Solutions for Niche Vocational Fields: Startups can tailor their AI tools to meet the specific needs of niche or underserved vocational sectors, such as welding, automotive repair, or culinary arts. By addressing the unique training challenges in these fields, startups can position themselves as essential partners for schools looking to modernise and differentiate their programs with specialised tools. For instance, FischerTechnik (DE), known for its educational engineering kits, has developed AI-enhanced training modules for mechatronics and industrial automation. These modules are specifically designed to provide hands-on learning experiences in complex technical fields. FischerTechnik’s AI-driven simulations allow students to engage with industry-standard equipment and scenarios in a controlled, educational environment, making the tools indispensable for vocational schools that aim to equip students with specialised skills in emerging tech fields like robotics and industrial automation.
Aligning with Government Initiatives: AI startups can greatly enhance their chances of success in the vocational education sector by aligning their offerings with government-funded initiatives aimed at workforce development, upskilling, and reskilling. By positioning their AI tools as solutions that help vocational schools meet government objectives—such as reducing unemployment, improving digital literacy, or addressing skills shortages—startups can tap into government funding streams and secure large-scale contracts that might otherwise be difficult to achieve. For example, Coursera (US) partnered with several governments during the COVID-19 pandemic to offer free online courses aimed at reskilling displaced workers. Although not exclusively focused on vocational training, Coursera’s approach demonstrates how aligning with government initiatives can help educational platforms reach a broader audience. For vocational schools, this alignment ensures that their students gain skills that are directly supported by government programs, increasing employability and industry relevance.
Targeting Individual Learners through B2C Models: While many AI startups might focus on selling their solutions directly to institutions, targeting individual learners through B2C models can be an effective strategy. By offering AI-driven vocational training tools directly to consumers, startups can tap into a market of career-changers, upskillers, and individuals seeking flexible, self-paced education options. For instance, Domestika (ES) is a platform that offers online courses in creative and vocational fields such as design, photography, and crafts. Domestika primarily operates on a B2C model, where individuals can purchase courses directly. Their AI-powered platform offers personalised learning experiences and real-time feedback, making it particularly appealing to individuals who want to develop new skills at their own pace.
Securing Competitive Advantage through Accreditation: For AI startups, partnering with government bodies to secure accreditation offers a strong competitive edge in vocational education. This approach allows them to become the exclusive provider of AI-driven tools for specific government-funded programs. Cécile Hazan, co-founder of YouSchool (FR), emphasised how winning accreditation enabled them to be the sole platform allowed to offer certain courses. "Once you win, you’re the only provider," she explained, highlighting the potential for startups to secure steady revenue from public contracts while reinforcing their role as essential partners in government-backed vocational training. This exclusive position not only ensures consistent demand but also builds credibility with vocational schools and industry partners.
At the end of the day, the success of AI startups in vocational education will ultimately come down to the willingness to pay of vocational schools. Many traditional institutions, especially those in hands-on trades, remain cautious about adopting new technology. To capture these schools, startups must provide clear economic value—showing how AI tools can enhance student outcomes, reduce training costs, or improve job placement rates. By focusing on tangible ROI and aligning with the unique needs of vocational programs, AI companies can overcome resistance and drive adoption in even the most traditional settings.
What's next for AI in Vocational Education?
As AI technology advances, vocational education has the potential to become more efficient, adaptive, and cost-effective, presenting new opportunities for AI startups to deliver high-value solutions that increase willingness to pay. Here are some future AI innovations that could unlock cost savings and operational improvements in vocational education:
AI-Powered Apprenticeship Placement Matching: Vocational programs often rely on apprenticeships and job placements to provide students with real-world experience. In the future, AI could play a crucial role in seamlessly matching students with apprenticeship programs based on their specific skills, progress, location, and employer needs. Imagine a scenario where an AI platform continuously monitors a welding student’s performance, analysing their strengths and areas for improvement. The AI then uses this data to match the student with an apprenticeship that perfectly aligns with their skill level and career goals, even taking into account local industry demands. By streamlining the placement process and improving the quality of matches, schools can enhance their apprenticeship success rates, leading to stronger industry partnerships and higher job placement metrics.
Providing AI-Generated Work Simulations for Real-World Readiness: While AI-driven simulations already exist, future AI tools will go beyond basic virtual environments by creating hyper-realistic, AI-generated scenarios that mimic unexpected, real-world job challenges. For example, in a construction program, AI could simulate unpredictable equipment malfunctions or safety hazards, forcing students to think critically and adapt. This advanced problem-solving practice would prepare students better for the workforce and reduce the need for expensive physical mockups or setups. Schools will be more willing to pay for these dynamic simulations as they provide a high return on investment by offering realistic training without the need for extensive physical resources.
Reducing Dropout Rates with AI-Powered Student Support: AI can be used to identify at-risk students early in their programs by monitoring engagement, attendance, and performance data. For example, an AI system could flag a student in a culinary arts program who has missed several key assessments or who consistently underperforms in practical exams. The system could then trigger an alert to the instructor or automatically offer the student additional resources, such as tutorials or counselling services. By intervening before a student drops out, schools can retain more students, securing their tuition revenue and reducing the need for costly recruitment efforts to replace those who leave. This proactive approach to student support makes AI a crucial tool for maintaining high enrollment and completion rates in vocational programs.
AI-Powered Learning Analytics for Curriculum Optimisation: AI could provide deep learning analytics to help online vocational schools continuously optimise their curricula. By analysing student performance, engagement levels, and feedback, AI systems could recommend curriculum adjustments in real time. Schools could use this data to streamline or modify courses based on what is working and where students are struggling, ensuring that online programs are always aligned with job market demands and student needs. This type of optimisation would reduce curriculum development costs and ensure high student success rates, making the investment worthwhile.
AI is proving to be a powerful catalyst in vocational education, enhancing how students learn and how schools operate. From personalised learning paths to immersive simulations, AI accelerates training, making it more efficient and accessible. Institutions like Ucademy and YouSchool show that AI not only reduces costs but improves outcomes, ensuring students are better prepared for the workforce. As AI continues to evolve, its ability to improve student success, reduce dropout rates, and streamline job placement will solidify its role in shaping the future of vocational education.
As ever, if you'd like to discuss this work or you are a founder working on a solution in this space, we would love to hear from you, so please do get in touch with rs@brighteyevc.com or via the deck submission form on our homepage.
We are grateful to Sabrina Bukenya, from Stanford's Graduate School of Business, for her support on this project.
NEXT UP: Summary of AI in K12, Higher Education and Vocational Education
Sources:
Seufert (2024): Artificial Intelligence in Vocational Education and Training (VET): Evaluating VET Leaders’ Acceptance of AI in Switzerland - https://www.researchgate.net/publication/382519503_Artificial_Intelligence_in_Vocational_Education_and_Training_VET_Evaluating_VET_Leaders'_Acceptance_of_AI_in_Switzerland