This work-in-progress examines how AI-supported multimodal instruction, aligned with the Common European Framework of Reference for Languages (CEFR), influences linguistic development and learner engagement in English-Medium Instruction (EMI) humanities learning among international engineering students in the College of Engineering at Shibaura Institute of Technology (SIT) in Japan. Two cohorts of students enrolled in a 14-week “History of Japan” module within an undergraduate engineering curriculum were compared: a pre-AI baseline cohort (Fall 2022, n = 22) and an AI-supported cohort (Spring 2025, n = 18). The 2022 cohort completed weekly reflective essays without AI assistance, while the 2025 cohort engaged with level-stratified multimodal resources developed using Claude 3.5 Sonnet and ElevenLabs. These multimodal resources consisted of highly detailed C2-level lecture slides paired with B2-level narrated summaries designed for accessibility; this combination was designed to reduce extraneous cognitive load while preserving conceptual challenge. English-language lexical development among the students was measured using the CEFR-based Vocabulary Level Analyzer (CVLA) v3.0, intercultural sensitivity was measured using the Miville-Guzman Universality-Diversity Scale–Short Form (MGUDS-S), and learner perceptions were recorded through surveys carried out in Week 14. CVLA results indicated significant lexical improvement in both cohorts (Fall 2022: t = −2.244, p = .036; Spring 2025: t(17) = 2.72, p = .015, Cohen’s d = 0.64), with reduced post-test variance in the AI-supported cohort suggesting convergence toward higher proficiency. MGUDS-S analysis for the 2025 cohort (n = 12) showed a small but not statistically significant change in overall intercultural sensitivity (p = .635), with the MGUDS-S subscale 'Diversity of Contact' showing a medium effect size (Cohen's dz = 0.52). A correlational analysis yielded a positive but non-significant association between lexical development and increasing intercultural sensitivity (r = +0.485, p = .110). Survey responses (n = 15) indicate that learners generally experienced the multimodal scaffolding as supportive, with notably higher ratings for visual slides than for audio-only narrations, and a majority preference for the instructor’s voice over an AI-generated one. This study offers empirically informed design implications for ethically integrating AI as strategic scaffolding in EMI humanities learning within engineering education.
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