An Empirical Study on Human-AI Interaction Patterns and System Design in Open University English Education
DOI:
https://doi.org/10.70088/6p36t409Keywords:
Human-Computer Interaction, AI-Assisted Teaching Systems, Generative AI, Retrieval-Augmented Generation, Intelligent Tutoring Systems, Recommender Systems, Affective ComputingAbstract
The integration of Generative AI (GenAI) into educational software is reshaping human-computer interaction (HCI), yet most AI-assisted teaching systems function as automation tools rather than genuine collaborative partners. This paper presents the design, optimization, and evaluation of the Collaborative AI Teaching Assistant (CAITA) , specifically architected for open university English education. Through a mixed-methods user study with 108 teachers, we identify key limitations of current systems. We then propose a novel architecture integrating four core modules: an Interactive Co‐Design Interface powered by a Retrieval-Augmented Generation (RAG) engine; a Data‐Driven Analytics Dashboard with a multi‐dimensional learner performance model; an Ethical AI Stewardship Module with bias detection; and an Affective Computing Module using sentiment analysis. Our technical contributions include a Dynamic Prompt Optimization (DPO) algorithm that iteratively refines outputs via user feedback, and a Collaborative Filtering‐based Recommendation (CFR) mechanism for pedagogical resources. Evaluations (technical benchmarks + A/B test with 60 teachers) show a 25.6% improvement in user interaction maturity and 21.3% increase in task completion efficiency over a baseline. This study provides a robust technical framework for next‐generation human‐AI collaborative educational software.References
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