Responsible AI in Teaching and Learning

Authors

Asa B. Stone, University of Nebraska-LincolnFollow
Mark C. Stone, University of Nebraska-LincolnFollow
Derek M. Heeren, University of Nebraska - LincolnFollow
Santosh Pitla, University of Nebraska-LincolnFollow

Date of this Version

2-2026

Document Type

Article

Citation

Stone, A. B., Stone, M. C., Heeren, D. M., & Pitla, S. K. (2026). Responsible AI in teaching and learning: Guiding principles and practical guardrails for higher education. White paper. Available at: https://digitalcommons.unl.edu/prairie-ai

Comments

Open access

License: CC BY-NC 4.0

Abstract

The rapid adoption of generative artificial intelligence (AI) in higher education presents both transformative opportunities and significant pedagogical risks. While AI tools are becoming embedded in academic and professional environments, their integration into teaching and learning raises critical questions about cognitive engagement, academic integrity, equity, and skill development. This white paper proposes a principled framework for the responsible integration of AI in higher education, grounded in the dual commitment to AI literacy and the cultivation of durable skills.

The framework articulates six core principles: purposefulness; transparency; integrity and attribution; critical AI literacy; equity and access; and privacy and data protection. Concrete guidance is provided for syllabus policies, AI use statements, assessment design, and critical evaluation practices. By centering human judgment, ethical reasoning, and intellectual ownership, this framework supports the development of graduates who can work effectively alongside AI while retaining the distinctly human competencies that define educated professionals.