Thanks to the prompt reactions and contributions by the invited partners to the ideas on exploring the potential of Artificial Intelligence for Language Education developed by TELLConsult we managed to redefine and duly submit a project proposal, this time with a focus on AI-supported pedagogical approaches to Reading & Writing

Let us know here if interested in being notified and receiving the project summary if we get granted and the project website goes live.

In line with EU policies the project aims to enhance teacher competences to promote students’ skills related to communication and language & cultural awareness, which consequently also are increasingly elements that national curriculum reforms related to L1 and MFL subject teaching in EU countries share.

Why DAPLE?
The DAPLE project aims at strengthening the profile of professionals in modern language education that want to comply with the EU AI Act[1] and at supporting the implementation of the recommendations of the Council of Europe Companion Volume (2020) by contributing to the professional development of language educators and (student) teachers targeting the integration of AI & data-driven technologies and pedagogies in modern language education.

In that context and as part of its forward looking approach the project will focus on the integration of AI and corpus-based tools in reading and writing, the more since various EU countries scored (well) below average on the PISA 2022 assessments.

One of the conclusions of a number of analysis studies of the PISA 2022 results for reading is that these are mainly due to the limited ability of the 15-year-olds students to deal with multiperspectivity and critically compare (and communicate about) multiple written (online) documents.

Apparently, with the current common format of national reading exams (standardised, paper-based MC tests on individual texts) and their teaching-to-the-test impact on actual school practices, real life skills such as multiple text comprehension that are needed in today’s global and digital world and are also expected in Higher Education are not adequately developed at secondary school level in a number of EU countries.

Evidence informed guidelines for improvement for (L1/L2) language education include balanced literacy programs that combine reading and writing instruction to improve overall literacy and an integrated approach to reading & writing (Graham et al., 2020; Vis et al., 2021) and explicit training of L2 reading strategies (Yapp et al., 2021).

As such a comprehensive approach is also part of developing MFL curriculum reforms (favouring a focus on language & culture related content and deeper learning) and GenAI offers functionalities to support a range of related steps in didactic interventions both for reading and writing, the project aims to support MFL teachers to adapt/improve/extend their L2 reading and writing instruction pedagogies so as to better prepare students for the world beyond the classroom and to meet competence requirements related to national current and future curriculum reforms and/or CEFR-level ambitions.

To this end the project will:
a) produce modular training materials, including classroom research-based use cases, to support modern language teacher educators to update/enhance their courses on the use of educational technology for initial and in-service teacher training

b) provide training to language school teachers on how and when to use GenAI- & corpus-based applications responsibly, co-design and develop teaching & learning activities and support action research of their effectiveness.

c) empower MFL departments in schools to contribute to the development and/or implementation of 1) cross-curriculum reading/language competence promotion local school policies including subject teaching in diverse and plurilingual classrooms and 2) the local 21st century, in subject teaching integrated, school curriculum, digital literacy and global citizenship for MFL education in particular.

d) produce academic papers based on the development and evaluation of the project’s use cases.