Date
Publisher
arXiv
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.
Study at a glance
- Question: How can an educational assessment framework ethically integrate generative artificial intelligence across different levels of schooling?
- Setting and data: Not stated in the paper.
- Method: Conceptual framework development based on a literature review.
Why it matters
Generative AI brings novel opportunities and challenges to education. Integrating these tools into educational assessments requires structured frameworks to uphold academic integrity, ensure ethical use, and account for different educational levels.
Key findings
- School bans on generative AI are easily circumvented, and AI text detection tools show poor accuracy that drops to 22% against adversarial techniques.
- The Comprehensive AI Assessment Framework expands existing guidance into six levels, adding advanced real-time interactions, personalized tutoring, and placeholders for future AI advancements.
- The framework incorporates five core ethical principles—transparency, equity, pedagogical alignment, accountability, and privacy—differentiated across primary, secondary, undergraduate, and graduate education.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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