Rewiring FLL: Evidence-Informed Strategies in the AI Era
Recently, foreign language learning has been improving due to better understanding of the mind and the rise of new technologies, and the Erasmus+ project AI as a way to enhance evidence-informed foreign language learning has been launched with the explicit aim of preparing teachers to utilize Artificial Intelligence (AI) for the systemic implementation of evidence-informed teaching and learning strategies.
The foundation of the project rests on key evidence-informed strategies that maximize learning and retention. Let’s focus on two of these which are also addressed in the project.
Retrieval Practice and Pure Question-Based Learning (pQBL)
The most effective learning often occurs when students are challenged to retrieve information from memory. This process, known as retrieval practice (or the testing effect), strengthens neural pathways and dramatically improves long-term retention compared to passive re-reading (Roediger & Karpicke, 2006).
We teach what we preach! The blended training course we are developing uses Pure Question-Based Learning (pQBL), a practical way to put the testing effect into practice. It transforms learning into a continuous cycle of retrieval. Instead of reading first, students are confronted with the question first. The core learning material is strategically repositioned into the immediate, explanatory feedback following the student’s attempt. This forces active engagement and ensures that every interaction is a potent act of learning (Bälter et al., 2024).
Beyond enhancing memory, pQBL offers crucial cognitive benefits. By making the content part of the feedback, it ensures that students are immediately exposed to the necessary information in a context where they are cognitively ready to receive it, having just experienced a moment of cognitive struggle (the question). This structured approach reduces cognitive load associated with sifting through lengthy, passive texts, leading to more efficient learning (Bälter et al., 2024).
Effective Feedback
The second evidence-informed strategy we address in our project is effective feedback. In order to be effective, feedback must be timely, targeted, and focused on helping the learner bridge the gap between their current performance and the learning goal (Hattie & Timperley, 2007).
In foreign language learning (FLL), this means moving beyond simply marking an answer as “wrong” to explaining why the answer was incorrect and how to fix it. Feedback, feed up and feed forward!
Why We Need AI to Scale These Strategies
While integrating retrieval practice and high-quality feedback is powerful, implementing it consistently and individually for every student is a tremendous workload. AI becomes the essential tool for scaling these strategies.
- Generative AI (GenAI) can automate the creation and delivery of high-quality, individualized, and timely feedback (Vlčková, 2024).
- AI tools can generate an endless supply of varied retrieval questions and tasks, adapting difficulty based on student performance (Sayici & Aydın, 2025).
- Recent analyses also show that integrating AI into language teaching can significantly enhance learner engagement and instructional efficiency when used with clear pedagogical purpose (Mananay, 2024).
Teacher Anxiety and AI Literacy
However, teachers are not born AI-literate. The rapid rise of AI has generated significant teacher anxiety, often rooted in psychological fears. Many educators fear they lack the specific Technological Pedagogical Content Knowledge (TPACK) needed to use AI effectively (Koehler & Mishra, 2009). This feeling of inadequacy lowers their belief in their ability to succeed with the new tools (Bandura, 1997). The worry that AI will diminish the teacher’s value or replace job functions can also lead to resistance (Sujatna et al., 2024), (Zhukevych & Spiricheva, 2024).
The project’s structure is specifically designed as a direct intervention against these sources of teacher anxiety by transforming fear into competence.
Empowering Foreign Language Teachers
We want to empower (oncoming) foreign language teachers and their educators with the knowledge and skills to effectively use GenAI to support evidence-informed FLL strategies.
How the Project Helps
The blended training course focuses on building technological self-efficacy by providing mastery experiences (Bandura, 1997).
By focusing AI on evidence-informed strategies, the project reframes AI not as a replacement, but as a sophisticated assistant that handles low-level cognitive tasks. This allows teachers to devote time to high-level communicative activities, affective student support, and cultural nuance—areas where human expertise is irreplaceable.
By empowering teachers with the internal conviction that they can effectively, ethically, and responsibly integrate these complex tools, the project ensures the durable adoption of evidence-informed FLL, preparing a confident and critically engaged professional workforce capable of leading pedagogical transformation in the age of generative AI.
Ready to implement evidence-informed learning with AI for real classroom success?
Explore the testimonials from teachers who are already boosting Foreign Language Learning (FLL) using these strategies and sign up to the project community.
References
- Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention.
Psychological Science, 17(3), 249–255.
https://doi.org/10.1111/j.1467-9280.2006.01693.x - Bälter, O., Glassey, R., Jemstedt, A., & Bosk, D. (2024). Pure question-based learning.
Education Sciences, 14(8), 882.
https://doi.org/10.3390/educsci14080882 - Hattie, J., & Timperley, H. (2007). The power of feedback.
Review of Educational Research, 77(1), 81–112.
https://doi.org/10.3102/003465430298487 - Vlčková, I. (2024). The use of artificial intelligence in teaching foreign languages.
ACC Journal, 29(3), 124–136.
https://doi.org/10.2478/acc-2023-0020 - Sayici, S., & Aydın, S. (2025). Using artificial intelligence in foreign language teaching: Teachers’ perspectives.
In Proceedings of the World Conference on Education and Teaching, 4(1), 1–10.
https://doi.org/10.33422/etconf.v4i1.1016 - Mananay, J. A. (2024). Integrating artificial intelligence (AI) in language teaching: Effectiveness, challenges, and strategies.
International Journal of Learning, Teaching and Educational Research. - Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)?
Contemporary Issues in Technology and Teacher Education, 9(1), 60–70. - Bandura, A. (1997).
Self-efficacy: The exercise of control.
W. H. Freeman. - Sujatna, M. L., Astarina, A. N., & Heryono, H. (2024). AI for language learning: Friend or foe?
LEEA Journal. - Zhukevych, I., & Spiricheva, O. (2024). Transformation of foreign language learning: Artificial intelligence as a tool for developing students’ language skills.
International Science Journal of Education & Linguistics, 3(3), 45–55.
https://doi.org/10.46299/j.isjel.20240303.06
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