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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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In questa pagina4 minuti di lettura
  1. Panoramica
  2. Immersione profonda
  3. Impatto strategico
  4. The Future of The AI Assessment Scale for Classrooms
  5. Implementazione nel mondo reale
  6. Rischi e guardrail
  7. Tabella di marcia per l'implementazione
  8. Continua a esplorare
  9. Domande frequenti

Panoramica

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.

Immersione profonda

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.

Impatto strategico

Rischio e sicurezza

I danni catastrofici e quotidiani dell’IA dipendono entrambi da chi comprende i rischi e da chi può agire.

Decisioni più chiare

L’alfabetizzazione pubblica e professionale determina la possibilità politica di una forte politica di sicurezza.

Tagliare il clamore

Spiegazioni chiare riducono la cattura da parte di montature pubblicitarie, PR di laboratorio e vaghi teatrini etici.

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.

Implementazione nel mondo reale

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.

Rischi e guardrail

  • Trattare il rischio esistenziale come fantascienza mentre le capacità si aggravano.

  • Confondere la sicurezza del prodotto superficiale con l'allineamento in condizioni di elevata autonomia.

  • Lasciando il pubblico non inglese e non esperto solo con fonti di bassa qualità.

Tabella di marcia per l'implementazione

  1. Separare i rischi di danni al prodotto, uso improprio e perdita di controllo/disallineamento.

  2. Chiedi quali prove cambierebbero la tua opinione sulle tempistiche e sulla gravità.

  3. Preferire fonti primarie e valutazioni concrete alle affermazioni di marketing.

  4. Identifica un percorso d’azione: carriera, politica, finanziamenti o competenze, non solo consapevolezza.

Continua a esplorare

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Domande frequenti

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.