The Smile Model
SMILE is an innovative model for the assessment and development of competences, based on artificial intelligence and the competence-based approach.
Designed to meet the training needs of organisations and, in particular, Higher Education Institutions (HEIs), the model enables the mapping, monitoring and personalised improvement of the skills of students and career counsellors, fostering continuous, flexible and strategic learning.
SMILE is an advanced competence management system designed to:
- assess digital, green, entrepreneurial, transversal and specialist competences;
- identify skills gaps and provide personalised learning recommendations;
- support HEIs and SMEs in defining targeted training plans aligned with sustainable development and internationalisation strategies.
Integrated with the TaiLENT platform, the SMILE model uses machine learning algorithms and generative artificial intelligence to:
- automatically generate hetero-evaluation questionnaires, adapted to the professional profiles of the European ESCO Framework;
- analyse responses and identify, in real time, strengths and areas for improvement;
- propose tailor-made multimedia learning content.
The SMILE skills assessment system is based on a dual-level approach:
- Macro-aggregation – collects and synthesises assessment scores, providing a clear overview of the overall competence level;
- Micro-retention – analyses each response in detail, allowing a granular and dynamic evaluation of individual abilities.
Responses are processed through a scoring system from 1 to 4, defining four levels of competence:
- Basic (1–25 points): elementary knowledge, to be strengthened through introductory training;
- Intermediate (26–50 points): functional competences, with room for further development;
- Advanced (51–75 points): operational autonomy and problem-solving ability;
- Specialised (76–100 points): full mastery and leadership in the competence area.
For each level, the system provides a detailed description of abilities and targeted training suggestions.
Based on the results of self-assessments, the model automatically connects each user to the most relevant training resources.
Recommendations are drawn from validated repositories of educational materials, available in multiple languages to ensure international accessibility.
Each self-assessment questionnaire is unique, designed around the real learning and development needs of the student.
