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 artificial intelligence be ethically integrated into educational assessments across different learning levels?
- Setting and data: Not stated in the abstract
- Method: Framework development based on a literature review and practical insights
Why it matters
Integrating generative artificial intelligence tools into education has been a game-changer for teaching and assessment practices, bringing new opportunities along with novel challenges that need to be addressed.
Key findings
- Across different educational settings, the Comprehensive AI Assessment Framework guides ethical evaluation by providing strict guidelines tailored to distinct educational levels.
- The assessment framework incorporates advanced generative artificial intelligence capabilities, including real-time interactions and personalized learning assistance.
- A visual color gradient enhances the framework's usability, making it an intuitive tool for upholding academic integrity and responsible technology use.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Study design
