AI-enhanced informal digital learning of English: Effects on EFL students’ cognitive, non-cognitive and oral proficiency skills

Abstract

This study examined the effects of integrating artificial intelligence (AI) tools into informal digital learning of English (IDLE) to enhance cognitive and non-cognitive skills, as well as listening and speaking proficiency among English as a Foreign Language students. A sample of 120 Egyptian university students participated in a mixed-methods design that consisted of a questionnaire, pretests and post-tests for listening and speaking skills and semi-structured interviews. Quantitative data were analysed using descriptive statistics, t tests and mixed analysis of variance, while qualitative responses were thematically explored. The findings revealed significant advancements in cognitive skills, including the regulation of attitudinal needs, goal commitment, resource allocation and metacognitive skills, as well as enhanced non-cognitive skills. However, social connections via AI were found to be less impactful, with many students reporting limited authentic interactions. While AI-driven IDLE significantly enhanced speaking proficiency, listening skills showed more modest gains, suggesting differential effects of AI on productive versus receptive skills. Despite technical challenges, AI-based IDLE demonstrated potential for personalising learning. Future research should address these challenges while focusing on bridging the gap between informal digital learning and real-world language use.

Implications for practice or policy:

• Educators should integrate AI tools into blended learning models, combining AI-driven practice with real-world communicative opportunities to bridge the gap between simulations and authentic language use.
• Developers must prioritise customisation in AI tools, such as adaptive learning paths and realistic conversation practice, to address diverse learner needs effectively.
• Policymakers and administrators should invest in resolving technical barriers (e.g., speech recognition accuracy, Internet reliability) to optimise AI tool effectiveness and user experience.