Definition: A strategic approach to teaching that tailors the pace, methods, and content to the individual needs, strengths, and goals of each student.

Before (Traditional Model): The entire class covers the same material at the same time. A student who doesn't understand something falls behind. A student who masters the material quickly gets bored and disengaged.

After (Personalized Model): Education becomes a flexible journey. Students focus on mastering specific competencies (mastery learning), not just "getting through" the material.

Definition: AI is a field of computer science that creates systems capable of performing tasks that typically require human intelligence, such as learning from data, reasoning, problem-solving and adaptation.

Analogy: Think of a recommendation system on a streaming platform. It analyzes what you watch and, based on that, suggests new movies you might like. AI in education works similarly it analyzes your progress and suggests the next educational steps for you

This isn't science fiction: We are not talking about conscious robots. We are talking about advanced algorithms.

1. Enhanced Skill Mastery: AI precisely identifies which specific skill (e.g. TIG welding, diagnosing an injection system) a student is struggling with and provides them with additional, targeted resources (videos, simulations, exercises).
2. Increased Engagement: Students work on tasks that are challenging but still within their reach. This builds motivation and a sense of agency. Content can be tailored to student interests (e.g. designing parts for motorcycles vs. for agricultural machinery).
3. Realistic and Safe Simulations: AI powers virtual simulators that allow for practicing dangerous or expensive procedures without risk. A student can make mistakes and learn from them in a controlled environment (e.g. a virtual patient in nursing school, a simulator for operating an excavator).
4. The Teacher as a Mentor: AI automates administrative tasks (e.g. grading tests, reporting progress), freeing up the teacher's time. The teacher can devote this time to one-on-one consultations, mentoring and developing students' soft skills.

The Student - From Passive Recipient to Active Architect of Their Knowledge:

• Then: A recipient of information.
• Now: An active participant who co-designs their learning path, uses AI tools to identify their knowledge gaps and actively seeks feedback.

The Teacher - From Lecturer to Facilitator and Analyst:

• Then: The main provider of knowledge.
• Now: A curator of content, a guide through the learning process, an analyst of data from AI systems (e.g. "I see that 70% of the class is struggling with module 3, we need to review this"), a mentor supporting students through challenges.

Step 1: Baseline Assessment:
The student completes a test or a practical task so the AI can assess their initial level of knowledge and skills.

Step 2: Content Recommendation:
Based on the diagnosis, the system suggests appropriate materials this could be an article to read for one student, a tutorial video for another and a practical exercise for a third.

Step 3: Real-time Analysis:
The AI monitors the student's interactions with the material how quickly they solve problems, where they pause, what mistakes they make.

Step 4: Dynamic Adaptation:
The system continuously adjusts the difficulty and type of subsequent tasks. If a student is doing great, they receive more advanced challenges. If they are struggling, the system offers remedial materials.

The Goal: Each student follows a unique path to achieve the same objective mastery of a given competency.

Formative Assessment 2.0: Instead of a test once a month, AI can ask short questions after each lesson, instantly identifying knowledge gaps.

Practical Skills Assessment:

• Mechanics: An AI simulator analyzes the sequence of steps taken by a student while diagnosing an engine fault.
• Programming: The system automatically checks the code written by a student, pointing out not only errors, but also suggesting more efficient solutions.
• Healthcare: A virtual patient reacts to the student's actions and the AI assesses the correctness of the diagnosis and proposed treatment.

Feedback:

• Instant: "I noticed you confused the order of connecting the wires. The correct sequence is..."
• Specific and Actionable: Instead of "wrong," the student receives information like, "Your cut is 2mm too deep. Try reducing the pressure on the tool."

• Intelligent Tutoring Systems (ITS): Platforms like Khan Academy for math or Moodle guide students step-by-step through problem-solving.
• Virtual and Augmented Reality (VR/AR):
  • Surgery/Medicine: Applications like Engage VR allow for practicing surgical procedures.
  • Manufacturing/Engineering: AR tools (e.g. Blender) can overlay digital instructions onto real machinery, guiding a worker through a repair process.
• Personalized Content Platforms: Coursera or edX use AI to recommend courses. Similar mechanisms can be implemented in school LMS systems.
• Career Pathing Tools: Platforms like Burning Glass analyze labor market data to show students which skills are in demand and what career paths are available to them.

What data does AI collect?

• Performance Data: Correct and incorrect answers, time taken to complete a task, scores achieved.
• Interaction Data: Which videos did the student watch? Did they pause? Which articles did they read? What did they click on?
• Goal-Oriented Data: What career goals has the student declared? What skills do they want to develop?

Important: This is not spying. This is collecting information to better tailor the learning process. Transparency and student consent are key.

1. Identify Needs (Diagnosis): Where do we lose the most time? Which skills are the hardest to teach? Where will personalization have the greatest impact?
   Example: Our students struggle with electrical troubleshooting in cars.
2. Research and Select Tools: Look for tools that address the identified need. Read reviews, ask for demos.
   Selection Checklist: Is it aligned with the curriculum? Is it easy to use? What support does the vendor offer?
3. Start Small (Pilot): Implement one tool with one group. Create a small, controlled experiment.
   Example: We will use an electrical diagnostics simulator with a group of 15 students for 4 weeks.
4. Gather Feedback and Evaluate: Create surveys for students and teachers. Talk to them.
   What were the pros and cons? Did learning outcomes improve?
5. Scale and Integrate: If the pilot was successful, plan for a wider rollout. Integrate the tool with the grading system and lesson plans. Provide training for other teachers.

Algorithmic Bias:

AI learns from data. If historical data contains biases (e.g. related to gender or ethnicity), the AI can perpetuate them.

Example: If historically more men have succeeded in a given profession, the AI might unconsciously promote that path for male users. This is why human oversight is crucial.

Human in the Loop:

The final assessment and key decisions about a student's education MUST always belong to the teacher. AI is an assistant, not a judge.

Transparency:

Students and parents must know what data is being collected and for what purpose. The privacy policy must be clear and understandable.

Data Security:

Where is the data stored? Who has access to it? The school must ensure compliance with GDPR and the highest security standards.

Problem: Teacher Resistance ("We've always done it this way")

• Solution: Involve teachers in the tool selection process from the very beginning. Show them how AI can save them time and help struggling students. Start with the tech enthusiasts.

Problem: Costs and Budget

• Solution: Start with free tools or demos. Apply for educational innovation grants. Present a cost-benefit analysis (ROI) to the management, e.g. lower material consumption thanks to simulators.

Problem: The Digital Divide

• Solution: Ensure the tools work on a variety of devices, including less expensive ones. Provide access to equipment at school. Choose solutions that do not require a constant, high-speed internet connection.

Hyper-personalization: AI will create unique educational projects for each student, based on their interests, learning style, and career goals. Example: The AI designs a task for a student to create a replacement part for their favorite drone model.

AI as a Professional Mentor: AI systems will accompany graduates in their first years of work, suggesting upskilling courses and helping them solve real-world professional problems.

Predictive Analytics: AI will be able to predict with high probability which students are at risk of not completing a course and will suggest early intervention strategies to the teacher.

Generative AI in Content Creation: Tools like ChatGPT or Gemini will help teachers create lesson plans, assignments and tests tailored to different proficiency levels.
 

For Teachers:

• Within a week: Find and read one article or watch one video about the use of AI in your industry.
• Within a month: Test one free AI tool (e.g. for creating quizzes or presentations).
• Within six months: Talk to your administration or other teachers about the possibility of running a small pilot project.

For Students:

• Within a week: Ask your teacher about the possibility of using new technologies to learn your trade.
• Within a month: Search YouTube for channels that use simulations or modern methods to teach your field.
• Always: Be open to new learning methods, ask questions and share your feedback on the tools you use.