Optimization of Research Framework Driven by HumanAI Collaboration: An Empirical Study on AI Training and Vulnerable Employment Groups

Abstract

Taking the improvement of skills and literacy among vulnerable employment groups through AI practical training as a case, this study illustrates how to achieve iterative optimization of a research hypothesis framework based on first-hand interview data via the collaborative use of DeepSeek and ChatGPT. Grounded in the Technology Acceptance Model (TAM), the study establishes an initial framework, collects interview data from vulnerable employment group volunteers who completed AI practical training, and uses AI-based transcription to form analytical texts. By inputting the initial framework and interview transcripts into the two AI models for exploratory optimization analysis, comparing and integrating their suggestions, and combining the researcher’s in-depth interpretation of raw data, the research framework is ultimately reconstructed. The findings clarify the deficiencies of the initial framework and provide a human-AI collaborative research paradigm of dual-model cooperation plus researcher judgment, offering a replicable methodological example for the iterative optimization of research frameworks. This work contributes to advancing SDG 4 (Quality Education) by enhancing access to skill development, supports SDG 8 (Decent Work and Economic Growth) through empowering vulnerable workers, and promotes SDG 9 (Industry, Innovation and Infrastructure) by demonstrating innovative applications of AI in social research.

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