Among the most effective ways to develop individuals' skills and competences in the twenty first century is to use Design Thinking (DT) as a problem-solving approach. Several fields of study have employed this approach to facilitate students' problem-solving skills through Information and Communications Technologies (ICT). Nevertheless, this skill has been glaringly overlooked in Computer-assisted Language Learning (CALL). For this sake, the researchers developed the Design Thinking Skills in Artificial Intelligence Language Learning (DEAILL) scale to address this gap in Artificial Intelligence Language Learning (AILL) and examined its role in shaping Artificial Intelligence L2 motivational self-system (AIL2MSS) and L2 grit among 92 Spanish English as a Foreign Language (EFL) students. Having validated the factorial structures of DEAILL, AIL2MSS and L2 grit in the study context, the Partial Least Structural Equation Modeling (PLS-SEM) showed that the more often language learners collected AI feedback based on their areas of needs and applied it to their target learning context, the more positive their future image and current L2 identity in AILL became. Additionally, the sign of authenticity gap was observed in this context, demonstrating participants' perception of AILL and DEAILL as more authentic contexts and skills compared with their previous learning experiences and skills. This mediated the relationship between learners' DEAILL skills and their L2 grit to devote more interest and persistence to AILL problem-solving. Consequently, the study proposes a new theoretical framework for CALL and the Common European Framework of Reference for Languages (CEFR) and recommends that language teachers prioritize the development of learners' problem-solving skills rather than learning outcomes. Furthermore, it is advisable for pedagogical experts to offer targeted teacher training programmes that equip language instructors with the skills and knowledge to foster DEAILL skills through CALL tools. Such professional development would ensure that teachers are prepared to integrate technology effectively into their language teaching. Additionally, including DEAILL competence as a mandatory criterion for language instructors could further enhance the quality of technology-enhanced language education.