Exploring Iraqi EFL Learners' Attitudes Towards Technology-Enhanced Personalised Language Learning in Developing Productive Skills Through the Lens of Self-Determination Theory

Abstract

Developing oral and written proficiency remains a persistent challenge for English as a Foreign Language (EFL) learners in Iraq, where large class sizes and traditional curricula often limit opportunities for individualized feedback and communicative output. While technology-enhanced personalized language learning (TEPLL) offers potential solutions to these structural constraints, little empirical research explores how digital personalization influences learners' internal psychological motivation. This study examined Iraqi university EFL learners’ attitudes toward TEPLL tools for productive skills development through the lens of Self-Determination Theory (SDT). An explanatory sequential mixed-methods design (QUAN -> qual) was implemented across five public and private universities in Iraq. In Phase 1, a survey administered to N = 342 undergraduate students measured basic psychological need satisfaction (autonomy, competence, relatedness) and attitudes toward personalized speaking and writing technologies. Structural Equation Modeling (SEM) demonstrated that Competence Satisfaction (β = .46, p < .001) and Autonomy Satisfaction (β = .38, p < .001) were the primary positive predictors of learner attitudes, accounting for 58% of the total variance (R² = .58). Participants exhibited significantly more favorable attitudes toward personalized speaking platforms (M = 4.31) than writing tools (M = 4.18), t(341) = 3.84, p < .001, citing reduced foreign language classroom anxiety in private digital environments. In Phase 2, semi-structured interviews (n = 18) revealed that while automated feedback loops satisfied competence and self-directed pacing enhanced autonomy, fully autonomous applications introduced a relatedness gap when disconnected from human interaction. Furthermore, infrastructural instability (power cuts and network drops) surfaced as a prominent environmental threat to learner autonomy. The study concludes that TEPLL tools operate most effectively within blended learning frameworks that pair automated skill personalization with instructor mentorship and peer collaboration.