Tuning in With Technology: AI-Enhanced Listening Instruction in the Jordanian EFL Classroom

Aim/Purpose: To evaluate whether a coordinated, multi-tool AI instructional design, chatbots, LingQ gamification, Google Speech-to-Text, and AI-driven virtual reality improve listening comprehension for Jordanian ninth-grade learners. Background: Listening is underdeveloped across many EFL primary and lower-secondary classrooms in the MENA region, where classrooms rarely sustain theory-informed, technology-rich scaffolding; this study responds by pairing Vandergrift’s metacognitive model and Vygotsky’s sociocultural lens with practicable AI tools. Methodology: A quasi-experimental comparison of two intact ninth-grade sections (experimental n = 24; control n = 24) contrasted AI-enhanced lessons with textbook activities; analyses used ANCOVA to control for pre-test differences while lesson logs and platform analytics monitored fidelity. Contribution: The study provides experimental evidence that an integrated, multi-tool AI design can produce meaningful listening gains and models how affective, cognitive, motivational, and situational scaffolds function together in classroom practice. Findings: The AI-enhanced group demonstrated higher adjusted post-test listening scores than the control group after controlling for pre-test performance, F(1, 45) = 42.39, p < .001, partial η² = .49. Adjusted means favored the experimental group (M = 16.56, SE = .29) over the control group (M = 13.94, SE = .29), indicating a substantial advantage for learners receiving coordinated, multi-tool AI instruction. These gains were observed within the B1 range of the assessment. Recommendations for Practitioners: Sequence and blend multiple affordable AI tools (e.g., chatbots, LingQ, automated transcription, short VR scenarios) in short in-class rotations, and use platform analytics to tailor difficulty and feedback rather than relying on a single app or textbook. Recommendation for Researchers: Replicate and scale the design across multiple schools and larger samples; use mixed methods to isolate which tool components drive gains; examine moderators (proficiency, digital literacy, teacher training); and, when possible, apply formal standard-setting to link outcomes to CEFR levels. Impact on Society: Effective, cost-sensitive AI scaffolding can broaden access to international media, higher education, and employment for secondary learners in under-resourced MENA settings, thereby supporting educational inclusion and social mobility. Future Research: Pursue longitudinal, multi-site trials to test durability and transfer to spontaneous spoken interaction, compare single-tool versus multi-tool architectures, evaluate teacher professional development needs, and assess cost-effectiveness across diverse Jordanian and regional contexts.