Project Partners

Universitatea Transilvania din Brașov

Univerza V Mariboru

Universita degli Studi di Udine

Sveučilište Jurja Dobrile u Puli

What
is the project all about?

The modernization of FL acquisition through digital tools and AI integration, empowering practitioners (students, teachers, researchers) with contemporary methods, specifically targeting the ESP field

  • 4 Work packages
  • 18 dedicated activities
  • 15 outputs

Project Objectives

  • to review literature on AI integration in education, mainly in FL classes and especially regarding ESP environments

  • to develop an AI solution database for ESP instruction

  • to create online educational resources

  • to implement AI tools across teaching, learning, and assessment levels

  • to integrate AI into language acquisition methodologies

  • to identify best practices

  • to develop training materials

  • to evaluate their impact on teachers, students and researchers

    Project Results

    90%

    Handbook on AI integration in FL/ESP education

    10%

    Repository of AI solutions for ESP

    0%

    Online lesson plan for AI in ESP language acquisition

    0/3

    3 workshops for stakeholders in applied linguistics

    0/6

    6 research articles

    0/3

    3 observation sheets databases

    0/2

    2 manuals for quality assurance and ethics

    0%

    Method guide on AI enhanced language acquisition

    Why is the project innovative?

    How does the project add value at the European level?

    A European-level approach to empowering specialized language acquisition through integrated AI maximizes the impact of the project:

    1

    by leveraging linguistic diversity. Diverse linguistic and cultural perspectives are integrated, tapping into a wealth of linguistic diversity, enabling the pooling of linguistic resources from various member states, creating a more comprehensive and varied language learning experience, and allowing for the aggregation of data from diverse linguistic contexts. The scale of data collection provides more robust insights into the effectiveness of AI-driven language learning methods across different languages and cultural backgrounds, forming best practices and contributing to the continuous improvement of language education strategies.

    2

    by harmonizing educational standards - by aligning methodologies, assessment criteria, and learning outcomes, according to a cohesive European approach to language education. This standardization ensures consistency and facilitates cross-border recognition of language proficiency, gaining broader transnational recognition, enhancing the credibility and acceptance of the project's results, such as standardized language proficiency assessments and innovative language learning methodologies.

    3

    by generating comprehensive data insights

    4

    by optimizing resource allocation

    5

    by facilitating transnational validation