The Read-Reflect-Respond (R3) Framework: Designing AI-resilient Assignments to Promote Authentic Learning and Academic Integrity
The emergence of AI in education has created both opportunities and challenges, especially in students’ examinations and assessments. Today, AI can solve almost any problem in seconds and provide answers in any style. This is useful in education, but its lead to misuse of AI in assignments and examinations, where students can solve the questions through AI, without independently thinking about the questions. AI provides simpler solutions, according to the prompt and can mimic human-like responses. Using AI to solve assignment questions has posed a challenge to the development of creative and critical thinking. Recently, students are directly copying AI-generated texts and pasting or writing in their answer sheets. Although AI has the potential to solve any problem, it has become a challenge for educators to evaluate ethically. There are several tools available, like plagiarism detection tools and AI-content detectors. This paper proposes the idea of AI-resilient assignments maintaining academic integrity with product- and process-based evaluation approach. These AI-resilient assignments are similar to the open-book system, where they require human intelligence, independent thinking, and personal understanding to solve them correctly.