Structured Freedom: How AI, Teacher Support, and Periodic Testing Shape Engagement in Informal Vocabulary Learning

The rapid advancement of artificial intelligence (AI) has transformed informal language learning, providing learners with flexible, self-directed opportunities for vocabulary acquisition outside formal classroom instruction. This study examined how AI use, teacher support, and periodic testing influence Chinese EFL learners’ engagement in informal English vocabulary learning. A sequential mixed-methods design was employed, combining a three-group pretest-posttest quantitative intervention over three months with follow-up qualitative interviews involving 15 participants. Quantitative results showed significant increases in all three engagement dimensions across conditions. At posttest, the teacher-guided AI with testing condition produced the highest behavioral and cognitive engagement, whereas the teacher-guided AI condition produced the highest emotional engagement; the AI-only condition showed the lowest levels across all dimensions. Qualitative accounts suggested that teacher support facilitated goal-setting, strategic scaffolding, and emotional reassurance, helping learners use AI more purposefully, while participants described testing as providing structured feedback for strategic adjustment and self-monitoring. The AI tool supported deep lexical processing and personalized learning, while learners appeared to use these affordances more strategically when combined with teacher guidance and structured feedback. These findings are broadly consistent with Self-Determination Theory and provide practical guidance for designing learner-centered vocabulary programs that balance autonomy, guidance, and accountability while sustaining strategic and emotional engagement.