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A Human-Centered Framework from Cross-Disciplinary Faculty Experimentation to AI Integration in the Classroom

A Human-Centered Framework from Cross-Disciplinary Faculty Experimentation to AI Integration in the Classroom

Proceedings (IEEE International Conference on Advanced Learning Technologies), pp.337-339
07-06-2026
AI literacy Artificial intelligence Design methodology Displays Education Educational institutions faculty development Friction generative AI higher education Indexes Indexing Learning (artificial intelligence) pedagogical framework Printing productive friction smart learning environments
The rapid proliferation of generative AI in higher education has outpaced evidence-based pedagogical frameworks. This paper presents findings from a structured faculty fellowship in which ten instructors across seven disciplines integrated AI into their courses and documented their practices. Fellows were selected through a competitive application process evaluated by an internal committee using a formal 100-point rubric. The program ran throughout the 2025-2026 academic year with monthly cohort meetings and a common nine-dimension teaching note template. Inductive cross-case analysis of seven completed Fall 2025 teaching notes, validated through iterative team review, identified four convergent pedagogical patterns: framing AI as a thinking partner rather than an answer generator; prioritizing productive friction over frictionless automation; treating prompt literacy as a core competency; and identifying verification as a persistent student weakness. These patterns informed a six-pillar Teaching with AI (TwAI) Framework and its companion A-V-E-R design pattern. The framework is offered as a practice-grounded contribution to AI-driven pedagogy and smart learning environments, with ongoing validation planned for Spring 2026.
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