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A METHODOLOGICAL FRAMEWORK FOR DEVELOPING STUDENTS' PROFESSIONAL ENGLISH SPEAKING SKILLS THROUGH DIGITAL EDUCATIONAL TECHNOLOGIES: THE CASE OF ECOLOGY EDUCATION

Graduates of ecology and environmental science programmes increasingly work in internationalised professional settings in which English is the working language of environmental impact assessment, transboundary water management, climate finance and scientific collaboration. Yet the oral component of their language preparation remains the weakest link: conventional English for Specific Purposes (ESP) instruction offers each learner only minutes of authentic speaking time, rehearses decontextualised topics rather than professional genres, and provides feedback that is delayed, impressionistic and rarely discipline-sensitive. This article develops, on theoretical grounds, a methodological framework for forming the professional English speaking skills of ecology students through digital educational technologies. Drawing on ESP needs-analysis theory, Content and Language Integrated Learning (CLIL), sociocultural and output-based accounts of second language development, and technology-integration models (SAMR, TPACK), the study proposes the Digitally Mediated Professional Speech Development (DMPSD) model. The model comprises four interlocking components — a genre-based content architecture, a three-tier taxonomy of digital tools differentiated by didactic function, a five-stage instructional cycle, and a multi-source assessment system anchored in CEFR mediation descriptors — governed by seven design principles. The article specifies nine professional oral genres for the ecology field, maps each to appropriate digital mediation, and analyses the conditions, risks and teacher-competence requirements of implementation. The framework is offered as a theoretically motivated design that is directly operationalisable in curricula and empirically testable; the limitations of a purely conceptual study and an agenda for experimental validation are set out in the conclusion.

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Graduates of ecology and environmental science programmes increasingly work in internationalised professional settings in which English is the working language of environmental impact assessment, transboundary water management, climate finance and scientific collaboration. Yet the oral component of their language preparation remains the weakest link: conventional English for Specific Purposes (ESP) instruction offers each learner only minutes of authentic speaking time, rehearses decontextualised topics rather than professional genres, and provides feedback that is delayed, impressionistic and rarely discipline-sensitive. This article develops, on theoretical grounds, a methodological framework for forming the professional English speaking skills of ecology students through digital educational technologies. Drawing on ESP needs-analysis theory, Content and Language Integrated Learning (CLIL), sociocultural and output-based accounts of second language development, and technology-integration models (SAMR, TPACK), the study proposes the Digitally Mediated Professional Speech Development (DMPSD) model. The model comprises four interlocking components — a genre-based content architecture, a three-tier taxonomy of digital tools differentiated by didactic function, a five-stage instructional cycle, and a multi-source assessment system anchored in CEFR mediation descriptors — governed by seven design principles. The article specifies nine professional oral genres for the ecology field, maps each to appropriate digital mediation, and analyses the conditions, risks and teacher-competence requirements of implementation. The framework is offered as a theoretically motivated design that is directly operationalisable in curricula and empirically testable; the limitations of a purely conceptual study and an agenda for experimental validation are set out in the conclusion.

聚变人工智能代理模型智能控制professional speech skills; digital educational technologies; English for Specific Purposes; ecology education; CLIL; artificial intelligence in language learning; CEFR mediation; TPACK.
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适用任务状态重建、快速预测、代理计算、参数扫描与设计优化
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