Hybrid Intelligence Feedback: A Model Proposal and Preliminary Study

Abstract

ABSTRACT Background Although existing studies suggest that AI chatbots hold potential as feedback tools in educational settings, there remains a significant gap in the literature regarding pedagogically grounded frameworks or guidelines for their effective implementation. Therefore, there is very little experimental research on how to use an AI chatbot based on a pedagogical approach. Objectives This study proposed a conceptual framework grounded in a hybrid intelligence feedback model for the use of AI chatbots as pedagogical feedback tools and aimed to examine its applicability and effectiveness within a higher education programming course. Method An experimental pre‐test/post‐test study involving 36 undergraduate students was conducted to evaluate the effectiveness of the proposed model and its underlying design principles for integrating AI chatbots as pedagogically grounded feedback tools in educational settings. Results and Conclusions Theory‐grounded AI chatbots based on the proposed hybrid intelligence feedback model and its design principles significantly improved learning more than the general‐purpose AI chatbot in programming class. Theory‐grounded AI chatbots can be an effective feedback tool in improving students' skills in self‐evaluation, critical thinking, and regulating learning strategies. General‐purpose AI chatbot feedback may cause “metacognitive laziness,” harming learning performance. Implications The article indicates the need for further research and validation to ensure effective implementation and address challenges associated with AI's potential inaccuracies.

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