Artificial Intelligence (AI) is not just a technological trend but a transformative force in vocational education and training (VET). It introduces new ways to design, deliver, and monitor learning, making teaching more adaptive and student-centered.

In VET contexts, AI is particularly relevant because it can respond to the diverse profiles of learners, adjusting activities to their skills, pace, and professional needs. This makes learning paths more flexible, inclusive, and effective.

AI is also connected to European frameworks, such as the EQF (European Qualification Framework) and EQAVET (European Quality Assurance in VET). These references highlight how AI can support both the recognition of competences and the quality assurance cycle in training.

For teachers and trainers, AI is both an opportunity—automating repetitive tasks and enriching lessons—and a challenge, requiring new competences and careful attention to ethics and accessibility.

• Chatbots: Applications such as ChatGPT or AI writing assistants interact with learners, answer questions, or generate texts on demand
• Virtual teaching assistants: Systems like Squirrel AI or Knewton guide learners through structured learning paths and monitor their progress
• Content adaptation systems: These platforms dynamically adjust materials according to learners’ performance and needs
• AI for assessment and quizzes: Tools such as Quizalize or Kahoot AI provide interactive testing, instant feedback, and adaptive progression

• Dynamic questioning: Adaptive tests do not follow a fixed sequence but change according to the student’s answers
• Targeted reinforcement: If a learner struggles with a topic, the system provides additional exercises and explanations
• Progressive challenge: When learners answer correctly, the system raises the difficulty level, keeping them engaged
• Personalized learning: Each learner follows an individualized path based on their actual needs and pace

• Personalization: AI enables differentiated pathways that respond to individual learner profiles
• Targeted reinforcement: If a learner struggles with a topic, the system provides additional exercises and explanations
• Progressive challenge: When learners answer correctly, the system raises the difficulty level, keeping them engaged
• Personalized learning: Each learner follows an individualized path based on their actual needs and pace

• Ethics and bias: Algorithms may reproduce biases contained in their training data, creating unfair results
• Privacy protection: Handling of student data must comply with the GDPR and institutional data protection policies
• Over-reliance: Excessive dependence on AI may reduce the active role of both teachers and learners
• Digital divide: Not all learners have equal access to devices and connectivity, which risks reinforcing inequalities

Evaluating and selecting AI tools is a critical step for teachers and trainers.

Not every tool is suitable for every context, and decisions should not be based only on novelty or popularity.

In vocational education and training (VET), the selection process must ensure that tools are aligned with pedagogical goals, accessible to diverse learners, and compliant with data protection and ethical standards.

This unit introduces key features to evaluate, such as personalization, online/offline operability, and integration with other platforms. It also explores how to choose tools according to different learning modes—online, offline, or blended—and how to match them to student profiles.

The goal is to equip educators with a clear methodology for selecting tools that truly enhance both learning outcomes and quality assurance, in line with frameworks like EQAVET.

• Personalization: The tool should adapt learning content to different learner profiles, providing customized support
• Online/offline operability: It is essential to consider whether the tool requires constant connectivity or can function locally in low-bandwidth environments
• Data usage and privacy: The system must comply with GDPR and handle student data transparently and securely
• Integration with platforms: Tools that connect with LMS (like Moodle or Teams) are more effective because they centralize learning data and simplify monitoring

• Moodle with AI plugins: Combines classroom teaching with adaptive digital support, creating a seamless blended pathway
• Smart classroom dashboards: Give trainers real-time analytics about participation and performance during face-to-face sessions

Blended approaches are highly effective in VET, as they mirror real professional environments that combine digital tools with hands-on practice.

• Age: Younger learners may need simpler interfaces, while adults may handle more complex tools
• Digital skills: Trainers must assess students’ readiness according to the DigComp framework before selecting advanced AI systems
• Access to technology: If students only have smartphones, mobile-first tools are preferable
• Learning difficulties: Voice-based AI or text-to-speech can support learners with dyslexia or other special needs, ensuring inclusivity

Combining AI tools is where their full potential emerges.

A single application may improve one specific aspect of teaching, such as assessment or tutoring, but when tools are integrated into a coherent lesson flow, they can transform the whole learning experience.

In vocational education and training (VET), this approach is particularly powerful, because it allows teachers to simulate professional scenarios, track competences, and adjust activities dynamically.

This unit will present scenarios and examples of tool combinations, explain how to design an effective lesson flow, and emphasize the importance of continuous feedback and quality improvement, fully aligned with the EQAVET quality cycle.

• AI-based pre-assessment: Start with a short diagnostic test to measure learners’ initial knowledge or skills
• AI-guided content delivery: Use adaptive systems or virtual tutors to introduce new material at the right level
• Interactive practice: Engage students in chatbot roleplays, simulations, or scenario-based tasks
• Adaptive feedback: End the lesson with quizzes or analytics dashboards that show individual progress and provide recommendations

In a language for hospitality course:

• Learners take an AI-driven vocabulary pre-test to identify their level
• They practice conversations with a chatbot simulating a hotel guest
• They complete an adaptive quiz that reinforces vocabulary and phrases they struggled with

This creates a continuous learning loop: assessment, practice, reinforcement, all within one coherent flow.

• Analytics: Teachers should regularly review data from AI platforms to identify trends and gaps
• Learner feedback: Beyond analytics, it is essential to ask students how they perceive the tools and their effectiveness
• Cycle of improvement: Apply the Plan–Do–Check–Act model from EQAVET to refine tool combinations, ensuring they meet both pedagogical and quality objectives