Article: AI-POA dual-engine framework: enhancing English speaking teaching through multimodal assessment Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.35 pp.55 - 73 Abstract: To address critical bottlenecks in production-oriented approach (POA) English speaking instruction - including high feedback delays, inefficient contextual task generation, and suboptimal resource allocation - this study proposes an AI-augmented POA framework. We developed a dual-engine architecture integrating dynamic task generation, multimodal resource recommendation, and multidimensional assessment to optimise POA's 'drive-facilitate-evaluate' closed loop. In a 12-week quasi-experiment with 120 computer science graduates, the experimental group (AI-POA) demonstrated significantly higher oral proficiency gains versus traditional POA controls (36.1% vs. 19.2%, <i>p</i> < 0.001), with content elaboration increasing by 22.6%. The framework reduced instructor feedback time per task from 8.2 to 0.3 minutes (27-fold improvement) and lowered cognitive load (NASA-TLX: 42 vs. 65, <i>p</i> < 0.001). Task acceptance reached 92% through cognitive-contextual difficulty adaptation. This work establishes an AI-POA synergy that enhances pedagogical outcomes while substantially alleviating instructor workload. Inderscience Publishers - linking academia, business and industry through research

Title: AI-POA dual-engine framework: enhancing English speaking teaching through multimodal assessment

Authors: Minmin Kong

Addresses: Public Foundational Courses Department, Yancheng Polytechnic College, Yancheng, 224000, China

Abstract: To address critical bottlenecks in production-oriented approach (POA) English speaking instruction - including high feedback delays, inefficient contextual task generation, and suboptimal resource allocation - this study proposes an AI-augmented POA framework. We developed a dual-engine architecture integrating dynamic task generation, multimodal resource recommendation, and multidimensional assessment to optimise POA's 'drive-facilitate-evaluate' closed loop. In a 12-week quasi-experiment with 120 computer science graduates, the experimental group (AI-POA) demonstrated significantly higher oral proficiency gains versus traditional POA controls (36.1% vs. 19.2%, p < 0.001), with content elaboration increasing by 22.6%. The framework reduced instructor feedback time per task from 8.2 to 0.3 minutes (27-fold improvement) and lowered cognitive load (NASA-TLX: 42 vs. 65, p < 0.001). Task acceptance reached 92% through cognitive-contextual difficulty adaptation. This work establishes an AI-POA synergy that enhances pedagogical outcomes while substantially alleviating instructor workload.

Keywords: production-oriented approach; POA; AI collaboration; English speaking teaching; multimodal assessment; adaptive learning.

DOI: 10.1504/IJICT.2025.149048

International Journal of Information and Communication Technology, 2025 Vol.26 No.35, pp.55 - 73

Received: 05 Jul 2025
Accepted: 16 Aug 2025
Published online: 10 Oct 2025 *

Title: AI-POA dual-engine framework: enhancing English speaking teaching through multimodal assessment

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