التاليالدليل التالي
Process-Based Assessment and Writing History
المجتمع
دليل المجتمع
The AI Assessment Scale (AIAS) is a five-level framework developed by Mike Perkins, Leon Furze, Jasper Roe and Jason MacVaugh.
It tells students how much generative AI they may use on a given assignment, from no AI to full AI. It matters because a general "be careful with AI" rule is replaced by a shared vocabulary, so every task comes with a clear label students can check.
The AIAS was first published in 2023 and piloted in higher education. That version had five levels: No AI; AI-Assisted Idea Generation and Structuring; AI-Assisted Editing; AI Task Completion, Human Evaluation; and Full AI. In 2024, the authors released an updated version with simpler names: 1. No AI: work done without AI help. 2. AI Planning: AI for brainstorming, research and structuring, but not the final work. 3. AI Collaboration: AI helps with drafting and feedback, and students critically evaluate and change its output. 4. Full AI: AI may be used throughout, with students directing it. 5. AI Exploration: creative or new uses of AI as part of the task itself. The update made two points clear. First, the levels are not a ladder of quality. A Level 4 task is not easier or less rigorous than a Level 1 task. It assesses different skills. Second, a label cannot guarantee anything by itself. If students do an assignment at home, nobody can confirm that "No AI" was followed. Level 1 only means something when the work is done under supervision. That second point links the scale to wider thinking about assessment security. The University of Sydney's "two-lane" approach, for example, separates secure, supervised assessments from open ones where AI use is expected and taught. To tell students which level applies, teachers usually put it on the assignment brief, explain why that level fits the learning goal, show examples of acceptable and unacceptable use, and require a short disclosure at any level above 1. Common misconceptions are that a whole course must sit at one level, and that the scale is a detection or enforcement tool. It is a communication and design tool.
تعتمد الأضرار الكارثية واليومية التي يسببها الذكاء الاصطناعي على من يفهم المخاطر ومن يستطيع التصرف.
إن المعرفة العامة والمهنية تحدد ما إذا كانت سياسة السلامة القوية ممكنة من الناحية السياسية.
إن التفسيرات الواضحة تقلل من الاستيلاء على الضجيج والعلاقات العامة المعملية والمسرح الأخلاقي الغامض.
Tiered scales like the AIAS have become a common starting point for schools and universities writing AI guidance, and more local adaptations are likely. The main work still to do is in assessment design, not labelling. Institutions are still figuring out how many supervised tasks they can realistically run and how to grade AI-assisted work fairly. As AI tools get built into everyday software, the lines between levels may need clearer examples. Research on how well these scales change student behaviour is still limited.
A teacher labels an in-class timed essay Level 1, No AI, and a take-home research proposal Level 2, AI Planning. Students can brainstorm and outline with AI but must write the proposal themselves.
A science department colour-codes each assignment brief by AIAS level and adds one sentence saying why that level fits the task.
A university business course sets a Level 4 task where students may use AI throughout. The grade is based on how well they check, critique and improve the output.
A computing teacher sets a Level 5 exploration project. Students test creative uses of AI and report what worked and what failed.
التعامل مع المخاطر الوجودية باعتبارها خيالًا علميًا ومركبات القدرة.
الخلط بين سلامة المنتج السطحي والمحاذاة في ظل الاستقلالية العالية.
ترك الجماهير غير الإنجليزية وغير الخبراء مع مصادر منخفضة الجودة فقط.
فصل أضرار المنتج، وسوء الاستخدام، ومخاطر فقدان السيطرة/اختلال المحاذاة.
اسأل عن الأدلة التي من شأنها أن تغير وجهة نظرك بشأن الجداول الزمنية وشدتها.
تفضيل المصادر الأولية والتقييمات الملموسة على المطالبات التسويقية.
حدد مسار عمل واحد: المهنة، أو السياسة، أو التمويل، أو المهارات - وليس الوعي فقط.
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The AI Assessment Scale (AIAS) is a five-level framework developed by Mike Perkins, Leon Furze, Jasper Roe and Jason MacVaugh. It tells students how much generative AI they may use on a given assignment, from no AI to full AI. It matters because a general "be careful with AI" rule is replaced by a shared vocabulary, so every task comes with a clear label students can check.
The AIAS gives each assignment a level that says how much generative AI is allowed. It is a communication and design tool.
Level 2, AI Planning, allows AI for brainstorming, research and structuring, but not for the final work.
A label alone guarantees nothing. No AI can only be trusted when the work is done under supervision.
The authors stress that the levels are not a hierarchy. A Full AI task can be as demanding as a No AI task.
Drafts, transcripts and reflections show how AI was used and what the student contributed.
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التاليالدليل التالي
Process-Based Assessment and Writing History
المجتمع