Open Preparation, Human Explanation, and Instructor Synthesis: A Human-Scale Methodology for AI-Rich Higher Education

Abstract

In AI-rich higher education, polished written mathematics has become easier to produce than trustworthy evidence of understanding. This article develops a human-scale methodology for service mathematics, with informatics as its main running case. Its central move is not prohibition of tools but relocation of evidential trust. Students prepare openly, often with digital assistance, but grade-relevant evidence shifts toward live explanation, contingent questioning, and cumulative observation against course outcomes. The design is guided by Realistic Mathematics Education, question-first task construction, short human-scale mathematical tasks, and instructor synthesis after student attempt. It contributes a weekly operational cycle, a realism framework distinguishing professional, disciplinary, and experiential realism, a middle-out white-box / black-box stance on tools, a bounded role for retrieval-grounded AI assistants for students and teachers, and a cumulative oral-evidence model for small and medium cohorts. The paper also provides concrete implementation artifacts: process figures, an ecology of problem types, time-budget estimates, an evidence hierarchy, and a five-grade oral rubric. This is a methodology paper rather than an effectiveness study. Its claim is that the proposed design is pedagogically coherent, operationally plausible for human-scale teaching settings, and responsive to current concerns about AI, oral evidencing, and active learning in undergraduate mathematics education.