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The AI Assessment Scale for Classrooms

The AI Assessment Scale (AIAS) is a five-level framework developed by Mike Perkins, Leon Furze, Jasper Roe and Jason MacVaugh.

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En esta pagina4 minutos de lectura
  1. Descripción general
  2. Buceo profundo
  3. Impacto Estratégico
  4. The Future of The AI Assessment Scale for Classrooms
  5. Implementación en el mundo real
  6. Riesgos y barandillas
  7. Hoja de ruta de implementación
  8. Sigue explorando
  9. Preguntas frecuentes

Descripción general

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.

Buceo profundo

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.

Impacto Estratégico

Riesgo y seguridad

Los daños catastróficos y cotidianos de la IA dependen de quién comprende los riesgos y quién puede actuar.

Decisiones más claras

La alfabetización pública y profesional determina si es políticamente posible una política de seguridad sólida.

Cortando el bombo

Las explicaciones claras reducen la captación por la exageración, las relaciones públicas de laboratorio y el vago teatro de ética.

The Future of The AI Assessment Scale for Classrooms

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.

Implementación en el mundo real

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.

Riesgos y barandillas

  • Tratar el riesgo existencial como ciencia ficción mientras que la capacidad se agrava.

  • Confundir la seguridad del producto superficial con la alineación en condiciones de alta autonomía.

  • Dejando a las audiencias que no hablan inglés ni a expertos solo con fuentes de baja calidad.

Hoja de ruta de implementación

  1. Separe los riesgos de daños al producto, mal uso y pérdida de control/desalineación.

  2. Pregunte qué evidencia cambiaría su opinión sobre los plazos y la gravedad.

  3. Prefiera fuentes primarias y evaluaciones concretas a afirmaciones de marketing.

  4. Identifique un camino de acción: carrera, política, financiamiento o habilidades, no solo concientización.

Sigue explorando

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Preguntas frecuentes

What is The AI Assessment Scale for Classrooms?

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.

What is the AI Assessment Scale mainly used for?

The AIAS gives each assignment a level that says how much generative AI is allowed. It is a communication and design tool.

In the updated 2024 AIAS, what is Level 2 called?

Level 2, AI Planning, allows AI for brainstorming, research and structuring, but not for the final work.

Why does a Level 1 (No AI) label need supervised conditions?

A label alone guarantees nothing. No AI can only be trusted when the work is done under supervision.

Which statement about the AIAS levels is correct?

The authors stress that the levels are not a hierarchy. A Full AI task can be as demanding as a No AI task.

What evidence best supports a Level 2 or Level 3 task?

Drafts, transcripts and reflections show how AI was used and what the student contributed.