Designing Critical Human–AI Learning in Higher Education: The SC-AIM–FAVIC Framework for Analytical and Logical Reasoning
Abstract
Generative artificial intelligence (AI) is widely used in university study, yet unstructured use may substitute for the reasoning that courses aim to develop. This article reports a conceptual model-development study that produced the Symbiotic Critical Human–AI Learning Model (SC-AIM) and its five-stage FAVIC framework (Formulation, Augmentation, Verification, Integration, Creation). Of the five ADDIE phases, Analysis and Design were completed through a targeted literature-based needs analysis, and Development was completed only to the level of draft artefacts: a stage specification, an activity protocol, a process-sensitive rubric, and an instrument blueprint. Expert review, prototype testing, implementation, and evaluation are proposed and have not been conducted. The design outputs are an evidence-to-design matrix that separates empirical findings from theoretical propositions and author’s interpretations, a comparison with existing frameworks, a FAVIC specification with return rules and scaffold-fading criteria, and a prospective multi-class evaluation protocol. Symbiosis is defined as role complementarity in which AI contributes breadth while the human retains problem framing, verification, and judgement. No expert-validation, pilot, or learner-outcome data are reported, so the effectiveness, feasibility, and generalizability of SC-AIM remain untested. The model is intended for pilot use in Indonesian higher education after expert judgement and prototype testing.
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