Poster Session 2, 11:30 AM - 12:15 PM: Campus Center Auditorium [A8]

Assessment Redesign and Ethical Governance for Generative Artificial Intelligence in Higher Education

Presenter: Vlad Renkas

Faculty Sponsor: Reena Randhir

School: Springfield Technical Community College

Research Area: Artificial Intelligence

ABSTRACT

The educational potential of ChatGPT and other generative artificial intelligence tools are significant. However, these systems can encourage students to produce assignments that appear authentic without requiring genuine understanding or learning. As a result, concerns have emerged regarding academic authorship, institutional trust, and the ethical balance between learning support and monitoring in the generative AI era. This creates a need for educational institutions to develop new approaches to maintain academic integrity and manage artificial intelligence use. 

This study reviews existing research on AI-based academic misconduct to evaluate how institutions are addressing these challenges through assessment design, AI detection systems, organizational policies, and staff development programs. The findings indicate that redesigning assessment systems is more effective than relying on automated detection tools. The effectiveness of AI-generated text decreases when assessments require critical thinking, contextual application, and original problem-solving. Requiring students to demonstrate their work across multiple stages, such as drafts, reflections, and evidence of the learning process, further limits misuse.

AI detection tools are limited by reliability issues and false positives, which can undermine trust and raise concerns about fairness and due process. These systems require human oversight and should be implemented cautiously. The study identifies three key elements of effective AI governance: clear guidance on acceptable AI use and disclosure requirements, training programs for students and educators, and improved assessment practices that prioritize the evaluation of authentic student learning.

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