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2025/26 Policy Framework

Artificial Intelligence (AI) Policy

Establishing a framework for the ethical and effective integration of generative AI within Grŵp Llandrillo Menai, ensuring responsible use in teaching, learning, and administration.

RESPECT / PARCH EQUALITY / CYDRADDOLDEB TRUST / YMDDIRIEDAΕΤΗ FAIRNESS / TEGWCH HONESTY / GONESTRWYDD AMBITION / UCHELGAIS

Introduction & Purpose

The Grŵp recognises that AI skills will increasingly become an essential skill for learners within the modern workplace. We are committed to the responsible use of generative AI to improve teaching and learning practice and to enhance the learning experience.

  • Promote responsible, ethical, and effective use
  • Enhance educational experiences for all
  • Comply with GDPR and JCQ guidelines
  • Support strategic vision for a modern world

Key Principles

Workload Reduction

Training resources to support staff in maximizing AI value.

Academic Integrity

Upholding honesty while helping learners develop AI literacy.

Safe & Ethical Use

Only approved 'Grŵp supported AI tools' are recommended.

Roles & Responsibilities

Clear guidelines for staff and learners to ensure ethical AI integration.

Staff Responsibilities

  • • Take part in AI benefit/risk training
  • • Integrate AI understanding into curriculum
  • • Monitor submissions for possible AI misuse
  • • Check all AI output for accuracy and bias

CRITICAL: Never upload sensitive data or personal details into generative AI tools.

Learner Responsibilities

  • • Never present AI content as original work
  • • Reference all AI-derived content appropriately
  • • Retain evidence of AI tool usage
  • • Protect sensitive/personal information
The fundamental creative and analytical elements of submitted work must represent the learner's own original thought and effort.

Opportunities & Challenges

Understanding the landscape of Generative AI in education.

Benefits

24/7 Availability

Support whenever learners need it.

Personalised Learning

Content tailored to individual needs.

Workload Reduction

Efficient creation of materials.

Risks

Inaccuracy

AI can produce false information (hallucinations).

Inherent Bias

Reflecting prejudices from training data.

Data Privacy

Risk of user data being compromised.

Impact Assessments

Commitment to fair, inclusive, and sustainable AI integration.

Equality Impact Assessment

Potential Risks

Selection bias and stereotyping in training data.

Controls

Adaptive interfaces and human oversight for identification of bias.