AI-supported visual-semantic word teaching from the perspective of educational psychology: instant gains in Turkish acquisition, computational choice, and learners' psychological experience.

BACKGROUND: Teaching Turkish to A1-A2 learners relies heavily on word selection and multimodal presentation. While traditional curricula prioritize frequency-based lists, they often underestimate the visual-semantic dimension critical for novice learners. Computational tools such as Word2Vec and CLIP offer a data-driven alternative by quantifying word-image alignment, yet their pedagogical efficacy in authentic classrooms remains empirically underexamined. Furthermore, the comparative effectiveness of AI-generated versus authentic photographs in vocabulary instruction constitutes an unresolved issue in computer-assisted language learning research. AIM: This pilot study investigated whether high visual-semantic coherence, computed using CLIP and Word2Vec, enhances immediate vocabulary gains compared with low-coherence lists. It further examined the non-inferiority of AI-generated images relative to real photographs and explored the educational-psychological mechanisms-motivation, self-regulation, cognitive load, and achievement goals-that underlie stakeholders' experiences. Phase 2 was designed to explain and contextualize Phase 1 results by identifying the psychological mechanisms underlying observed learning outcomes, thereby achieving integration through the connecting strategy. MATERIALS AND METHODS: An explanatory sequential mixed-methods design was employed. Phase one assigned 60 A1-A2 learners to three quasi-experimental conditions: (a) high-CLIP words with AI-generated images (n = 15), (b) high-CLIP words with real photographs (n = 15), and (c) low-CLIP words with textbook images (n = 30). The use of non-equivalent word sets across conditions constrains causal interpretation. Vocabulary knowledge was assessed via the Vocabulary Knowledge Scale at pretest, post-test, and week four, though the absence of delayed testing limits conclusions to immediate learning. Phase two administered validated instruments-TAM, MSLQ, AGQ, Paas cognitive load scale, and SDT basic needs scale-to 60 students and 15 instructors. RESULTS: Both experimental conditions significantly outperformed the control (p < 0.001, d = 1.42). No significant difference emerged between AI and real images (p = 0.678), though the small per-group sample (n = 15) provided adequate power only for large effects (d ≥ 0.75). Students reported moderate motivation (M = 3.52) and perceived fairness (M = 3.61), whereas instructors exhibited lower motivation (M = 2.89, p = 0.005) and higher perceived effort (M = 3.67 vs. 2.94, p = 0.003). Mastery-approach goals correlated positively with vocabulary gains (r = 0.48, p < 0.01). Cognitive load was moderate (M = 4.2/9) and uniform across conditions. Basic-needs satisfaction significantly predicted intrinsic motivation (β = 0.62, p < 0.001). CONCLUSION: AI-driven visual-semantic word selection demonstrates preliminary promise for concrete vocabulary instruction. Nevertheless, the quasi-experimental design, confounding word sets, absence of delayed post-tests, and limited statistical power necessitate cautious interpretation. Replication with standardized word pools, delayed assessments, and objective proficiency measures is essential to establish causal efficacy and to determine whether AI-generated images offer genuine equivalence-or merely undetected non-inferiority-relative to authentic photographs. The non-significant difference between AI-generated and authentic photographs should be interpreted as the absence of evidence for a difference, not as evidence of absence of difference.