Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration

TL;DR

This paper introduces the Comprehensive AI Assessment Framework (CAIAF), a new tool designed to ethically evaluate AI integration in education, emphasizing real-time, personalized AI capabilities and user-friendly design to improve learning and uphold integrity.

Contribution

The paper presents CAIAF, an enhanced, ethically grounded assessment framework for AI in education, incorporating real-time interaction, personalization, and a user-friendly color gradient interface.

Findings

Framework supports ethical AI integration in diverse educational settings.

Enhances learning outcomes and academic integrity.

Provides a practical, intuitive tool for educators.

Abstract

The integration of generative artificial intelligence (GenAI) tools into education has been a game-changer for teaching and assessment practices, bringing new opportunities, but also novel challenges which need to be dealt with. This paper presents the Comprehensive AI Assessment Framework (CAIAF), an evolved version of the AI Assessment Scale (AIAS) by Perkins, Furze, Roe, and MacVaugh, targeted toward the ethical integration of AI into educational assessments. This is where the CAIAF differs, as it incorporates stringent ethical guidelines, with clear distinctions based on educational levels, and advanced AI capabilities of real-time interactions and personalized assistance. The framework developed herein has a very intuitive use, mainly through the use of a color gradient that enhances the user-friendliness of the framework. Methodologically, the framework has been developed through the huge support of a thorough literature review and practical insight into the topic, becoming a dynamic tool to be used in different educational settings. The framework will ensure better learning outcomes, uphold academic integrity, and promote responsible use of AI, hence the need for this framework in modern educational practice.