This page summarizes the pedagogical framework used in the AI-SMART platform for training pre-service informatics teachers through the development of AI-based educational resources. The methodology integrates an AI-lector, practical learning activities, and AI-assisted assessment.
Four-Stage Model
The training process follows a structured sequence from pedagogical design to implementation and evaluation.
Pedagogical Design
- learning outcomes
- lesson objectives
- instructional planning
AI-Assisted Development
- AI-lector explanations
- task & content generation
- examples and hints
Practical Implementation
- hands-on sessions
- mini-projects
- resource creation
Assessment & Feedback
- AI-assisted test questions
- rubrics / criteria
- formative feedback
Role of Artificial Intelligence
Artificial intelligence is used as a pedagogical support tool (not a replacement for the teacher), enhancing explanation, practice, and assessment.
- AI-lector: short explanations, examples, and guidance.
- Practice support: task generation and adaptive hints.
- Assessment support: AI-assisted test items and feedback.
Learning Activities
Learners work through practical tasks and projects to design, develop, and test educational digital resources.
- hands-on practice sessions
- mini-projects and portfolio artifacts
- resource development using AI tools
Assessment Strategy
Assessment combines AI-assisted testing with evaluation of practical outputs to continuously monitor learning outcomes.
- AI-assisted test questions (item bank)
- rubric-based evaluation of practical tasks
- automated and instructor feedback loops
Expected Learning Outcomes
Upon completion, learners demonstrate readiness to design and apply AI-based educational resources in informatics teaching.
- ability to design AI-based educational resources
- development of digital and pedagogical competencies
- readiness to use AI tools in classroom practice