Generative artificial intelligence tools offer transformative potential for English language learners, yet the factors driving their adoption remain insufficiently understood, particularly in Arab higher education contexts. This study examined AI tool adoption among undergraduate English learners at the University of Hail, Saudi Arabia, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the theoretical framework. A quantitative survey of 318 students tested nine hypothesized relationships between seven UTAUT2 constructs and two outcomes: behavioral intention and use behavior. Partial least squares structural equation modeling (PLS-SEM) revealed exceptional explanatory power, accounting for 80.1% of the variance in behavioral intention and 80.9% in use behavior. Seven of nine hypotheses were supported. Habit emerged as the dominant predictor, exerting the strongest total effect on use behavior (β = 0.518), operating through both conscious intention and automatic behavioral pathways. Price value, reconceptualized to capture time and effort costs rather than monetary ones, was the second-strongest predictor of intention, followed by hedonic motivation and performance expectancy. Contrary to expectations, effort expectancy and social influence were non-significant. These findings extend UTAUT2 to generative AI in language learning, challenge assumptions about universal predictor applicability, and offer practical guidance for educators and policymakers seeking to promote sustained, effective AI integration in English language education.