Research on Technology-Enhanced English Writing Instruction: Practice, Effects, and Teachers' Perceptions from a Multi-Method Integration Perspective

This article provides an in-depth interpretation of a multi-study doctoral thesis on technology-enhanced writing instruction for EFL learners. Using three methods—meta-analysis, quasi-experimental research, and qualitative interviews—it systematically examines the effects of technological tools in English writing instruction, the innovative application of infographic teaching methods, and teachers' perceptions and attitudes towards the integration of generative AI.

English writing has long been a difficult point in EFL instruction. Traditional teaching has problems such as low participation, long feedback cycles, and insufficient personalized guidance. Technological tools (from word processing to generative AI) bring new possibilities to writing instruction, but the effect of integration is affected by multiple factors. The study aims to answer three core questions: What is the overall effect of technology-enhanced writing instruction? Can infographic-assisted writing improve effectiveness? How do teachers view the application of generative AI in teaching?

The study adopts a three-research integration framework: 1. Meta-analysis: Includes 111 studies (1085 effect sizes) to verify the overall effect of technology-enhanced instruction; 2. Quasi-experiment: The experimental group uses infographic-assisted writing while the control group uses traditional methods, to examine writing quality and self-efficacy; 3. Qualitative interviews: Conducts semi-structured interviews with 7 college English teachers to explore their perceptions and attitudes towards technology integration (especially generative AI).

1. Effect of technology integration: Meta-analysis shows that technology-enhanced writing instruction has a moderate positive effect (Hedges'g=0.51), with collaborative tools and automatic feedback systems having more significant effects; 2. Value of infographics: Significantly improves writing performance (text structure, content organization, etc.) and students' writing self-efficacy; 3. Teachers' perceptions: Recognize the value of technology, but hold a cautious attitude towards generative AI, worrying about issues such as academic integrity, student dependence, and their own insufficient capabilities.

1. Principles for technology selection: Alignment with teaching (serving goals), cognitive load management (simplifying learning), and gradual integration (starting with low risk); 2. Teacher development: Cultivate Technological Pedagogical Content Knowledge (TPACK), strengthen AI ethics and academic integrity education, and establish collaborative learning communities; 3. Institutional support: Ensure infrastructure, time resources, supporting evaluation systems, and technical support services.

Limitations: Sample representativeness (quasi-experiment at a single university, 7 teachers in qualitative research), short intervention time, and rapid technology iteration. Future directions: Longitudinal tracking studies, cross-cultural comparisons, special studies on generative AI, and research on learning process mechanisms (learning analytics, eye-tracking, etc.).