Policy Guidance

Case Studies

1. Case Studies from Schools Around the World

Taliaferro County Schools

K-12

The Taliaferro County Schools' AI Policy outlines guidelines for the ethical use of AI in K-12 education, addressing both educators and students. It emphasises the potential of AI to enhance learning while safeguarding student privacy and well-being. The policy prohibits inappropriate uses, such as violating data security, perpetuating bias, and replacing essential human interactions in teaching. It mandates training for educators, thorough evaluation of AI tools, and regular compliance audits. In addition, it also outlines consequences for students who disobey the policy. The policy will be reviewed annually to adapt to evolving technologies and educational needs.

Yokohama International School (YIS)

K-12

YIS has implemented several specific initiatives in their AI Policy to promote responsible and ethical use of artificial intelligence in education. They actively foster a culture of ethical AI practices by engaging in discussions about bias, privacy, and transparency, while also facilitating ongoing professional learning opportunities for staff to enhance their understanding of AI tools. Additionally, YIS encourages critical evaluation of AI outputs among students, teaching them to distinguish between their own thoughts and AI-generated content . The policy mandates the use of transparency statements and citations for any AI involvement in academic submissions, ensuring clear disclosure of AI contributions. Furthermore, YIS promotes collaboration among teachers and administrative staff to share best practices regarding AI use and maintains open communication with parents about the role of AI in their children's education.

Queen's College Taunton

K-12

Queen's College's AI policy establishes clear guidelines for students regarding the appropriate use of AI tools, emphasising that students must indicate which parts of their submissions were created by AI, and that students cannot submit AI-generated work as their own to avoid plagiarism.. The policy permits the use of AI tools for generating content unless specifically prohibited by teachers, but students must verify the accuracy and completeness of AI-generated information. Misuse of AI includes copying or paraphrasing AI-generated content to the point that it does not reflect the student's own work, as well as failing to acknowledge AI sources properly. Additionally, students are prohibited from using AI to produce harmful or biased content, with the possibility of consequences for irresponsible us. Any suspected instances of AI misuse must also be reported.

Policy for AI in Education

All levels of education

Professor Chan's AI policy principles outline essential considerations for integrating AI into education, focusing on equitable access to resources for all students and teachers, as well as the need for robust security measures and data security. It emphasises the importance of AI literacy among educators and students to navigate these principles effectively. Additionally, it encourages a rethinking of pedagogical strategies to incorporate AI flexibly into the curriculum. The policy principles also highlight the need for carefully designed assessment practices that account for potential biases in AI evaluations, and stresses the importance of integrity, holding students accountable for the ethical use of AI-generated content.

References:

[1] Chan, C. K. Y. & Colloton, T. (2024). Generative AI in Higher Education: A ChatGPT Effect. Routledge.

[2] Chan, C.K.Y. (2023). A Comprehensive AI Policy Education Framework for University Teaching and Learning. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-023-00408-3

HKSAR Government Principles/ Frameworks

Ethical Artificial Intelligence Framework

The Hong Kong SAR drew reference from other countries and regions to develop the Ethical AI Framework for adoption within the Government, as well as to serve as general reference for other organisations when implementing AI or big data analysis (Digital Policy Office, 2024).

The Ethical AI Framework includes two main components:

A tailored AI Framework, comprising of:
- Ethical AI Principles to define the design and development of AI;
- An AI Governance Structure outlining roles and responsibilities over AI implementation processes;
- An AI Lifecycle to define a model to structure practices in the AI Practice Guide and assessment questions in the AI Assessment; and
- An AI Practice Guide, detailing a set of recommended practices for each AI lifecycle stage, based on the ethical AI principles.
AI Assessment, featuring an AI Application Impact Assessment template with questions aligned across stages of the AI Lifecycle to ensure that ethical principles are considered throughout AI implementation. (Refer to Figure 2.2)

Ethical Artificial Intelligence Framework

Tailored AI Framework

Ethical AI Principles

Transparency and Interpretability
Reliability, Robustness and Security
Fairness
Diversity and Inclusion
Human Oversight
Lawfulness and Compliance
Data Privacy
Safety
Accountability
Beneficial AI
Cooperation and Openness
Sustainability and Just Transition

AI Governance Structure

AI Lifecycle

AI Practice Guide

AI Assessment

AI Application Impact Assessment

Figure 2.2

References:

[1] Education Bureau. (2024). “Information Literacy for Hong Kong Students” Learning Framework (2024). Education Bureau, The Government of the Hong Kong Special Administrative Region. https://www.edb.gov.hk/attachment/en/edu-system/primary-secondary/applicable-to-primary-secondary/it-in-edu/Information-Literacy/IL_learningFramework/InformationLiteracyforHongKongStudentsLearningFramework%282024%29_20241029_ENG.pdf

[2] Digital Policy Office. (2024). Ethical Artificial Intelligence Framework. The Government of the Hong Kong Special Administrative Region of the People’s Republic of China. https://www.digitalpolicy.gov.hk/en/our_work/data_governance/policies_standards/ethical_ai_framework/doc/Ethical_AI_Framework.pdf

Hong Kong Generative Artificial Intelligence Technical and Application Guideline

The Hong Kong Generative Artificial Intelligence Technical and Application Guideline (Digital Policy Office, 2025) provides a comprehensive framework for the responsible development and use of GenAI technologies across various sectors, including education. It offers practical guidance for technology developers, service providers, and end users, to ensure ethical and secure AI development and implementation. Schools are key users of AI technologies in education, relying on these tools to support teaching, learning, and administration, falling into the stakeholder category of ‘end users’ or ‘service users'. The Digital Policy Office highlights four key responsibilities of end users as stakeholders, including ethical usage, awareness and control, community protection, and content verification. Service users of GenAI play a vital role in using these services securely and responsibly to maintain a compliant and secure AI ecosystem. As such, the document offers seven recommendations for users of GenAI services: legal and regulated use; maintain independent discretion; understand responsibilities and obligations; citation and attribution; privacy protection; prudent dissemination; and respect for intellectual property rights.

Service users of GenAI

Users should use GenAI in compliance with guidance and requirements from service providers and regulatory authorities, and avoid illegal or inappropriate use. Users must take into account the legal and regulatory frameworks and laws of Hong Kong, and promptly report any illegal or inappropriate content to service providers to ensure proper use of GenAI.

To support a healthy GenAI ecosystem and promote lawful content creation, users must respect intellectual property rights by adopting technical measures to avoid generating content that copies copyrighted works, regularly checking for potential infringements of rights, and promptly deleting or modifying any content found to violate others’ intellectual property rights in accordance with legal and/or administrative measures.

[1] Digital Policy Office. (2025). Hong Kong Generative Artificial Intelligence Technical and Application Guideline. The Government of the Hong Kong Special Administrative Region of the People's Republic of China. https://www.digitalpolicy.gov.hk/en/our_work/data_governance/policies_standards/ethical_ai_framework/doc/HK_Generative_AI_Technical_and_Application_Guideline_en.pdf

References:

UNESCO – A policy framework for the use of generative AI (GenAI) in education and research
The UNESCO policy framework for the use of GenAI in education and research provides measures to regulate applications of AI in educational settings, including teaching and research (Miao & Holmes, 2023). The guidance addresses policy considerations, pedagogical strategies, and the importance of teacher training to ensure that AI tools are used responsibly and inclusively within classrooms. Structured around eight key measures, the framework offers detailed considerations and suggested actions for each measure, aiming to support policymakers, educators, and stakeholders in the ethical and effective integration of GenAI into education and research. While GenAI is a valuable tool to support education and research, the framework acknowledges its potential to disrupt existing systems and reshape foundational principles.

Measures and Considerations/ Suggested Actions

- Inform learners about the data GenAI may collect, how it is used, and its potential impact
- Protect learners’ intrinsic motivation and reinforce human autonomy in teaching, learning, and research when using GenAI
- Prevent uses of GenAI that limits development of cognitive and social skills in learners
- Ensure appropriate social interaction and exposure to creative human output to prevent addiction and overreliance on GenAI
- Use GenAI to reduce pressure of homework and exams, rather than to increase it
- Consult researchers, teachers and learners about GenAI and use the feedback to decide whether and how specific tools should be adopted within institutional contexts
- Maintain human accountability in high-stakes decision making

- Build validation mechanisms to ensure GenAI systems used are free of biases and trained on diversified data
- Address the issue of informed consent
- Audit for fake, inaccurate, false, inappropriate, or hateful output from GenAI, and take swift and robust action to mitigate or eliminate such issues
- Exercise rigorous ethical validation before adoption of GenAI
- Before making decisions on institutional implementation, ensure GenAI applications cause no predictable harm to students, are educationally effective and appropriate for learners’ ages and abilities, and align with sound pedagogical principles

- Commit to provision of government-approved AI curricula, encompassing the impact of AI, relevant ethical issues, and age-appropriate understandings and skills for use of AI tools
- Enhance programmes to develop local AI talent
- Promote gender equality in developing AI competencies and create a gender-balanced pool of professionals
- Forecast job shifts from GenAI automation and enhance future-ready skills at all education levels based on prospective demands
- Provide specific programmes for older individuals who may need to acquire new skills and adopt to new environments

- Develop or adjust guidance from local trials to effectively use GenAI tools and guide new designs
- Protect rights of teachers and researchers and the value of their practices
- Define the value orientation, knowledge and skills needed for teachers to understand and use GenAI effectively and ethically
- Dynamically review teacher competencies to understand and use AI for teaching, learning and for professional development, and integrate emerging sets of values, understanding and skills on AI into competency frameworks and programmes for teacher training

- Promote equitable and inclusive access to AI applications
- Develop validation criteria to prevent bias, discrimination, and hate speech in GenAI systems
- Develop and implement standards and safeguards for GenAI systems to protect diversities and prevent discrimination

- Ensure GenAI design and adoption are strategically planned
- Encourage GenAI designers to aim at open-ended, exploratory, and diverse learning options
- Test and scale up evidence-based AI use cases in accordance with educational priorities
- Guide the use of GenAI to trigger innovation in research and outputs to inform and improve research methodologies
- Review social and ethical implications of incorporating GenAI into research processes
- Establish pedagogically grounded criteria and build an evidence base for the effectiveness of GenAI in supporting provision of inclusive learning opportunities, meeting learning and research objectives, and promoting linguistic and cultural diversities
- Take iterative steps to strengthen evidence on the social and ethical impact of GenAI
- Analyse the environmental costs of deploying AI technologies widely, and develop sustainable targets for AI providers

- Collaborate with AI providers, educators, researchers, and representatives of parents and students to adapt curriculum frameworks and assessments to maximise the benefits and minimise the risks of GenAI
- Convene intersectoral and interdisciplinary expertise to examine long-term implications of GenAI for education, research, human collaboration and social dynamics
- Offer timely advice to inform updates of policies and regulations

Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693

Reference:

UNESCO – A policy framework for the use of generative AI (GenAI) in education and research

The UNESCO policy framework for the use of GenAI in education and research provides measures to regulate applications of AI in educational settings, including teaching and research (Miao & Holmes, 2023). The guidance addresses policy considerations, pedagogical strategies, and the importance of teacher training to ensure that AI tools are used responsibly and inclusively within classrooms. Structured around eight key measures, the framework offers detailed considerations and suggested actions for each measure, aiming to support policymakers, educators, and stakeholders in the ethical and effective integration of GenAI into education and research. While GenAI is a valuable tool to support education and research, the framework acknowledges its potential to disrupt existing systems and reshape foundational principles.

Measures and Considerations/ Suggested Actions

Measures:

Protect human agency.

Considerations/ Suggested Actions:

Measures:

Monitor and validate GenAI systems for education.

Considerations/ Suggested Actions:

Measures:

Develop AI competencies including GenAI-related skills for learners.

Considerations/ Suggested Actions:

Measures:

Build capacity for teachers and researchers to make proper use of AI.

Considerations/Suggested Actions:

Measures:

Test locally relevant application models and build a cumulative evidence base.

Considerations/Suggested Actions:

Measures:

Review long-term implications in an intersectoral and interdisciplinary manner.

Considerations/Suggested Actions:

Policy Guidance

1. Case Studies from Schools Around the World

Taliaferro County Schools

Yokohama International School (YIS)

Queen's College Taunton

2. Professor Cecilia Chan's AI Policy Principles

Policy for AI in Education

HKSAR Government Principles/ Frameworks

Ethical Artificial Intelligence Framework

Service users of GenAI

References:

Measures and Considerations/ Suggested Actions

- Promote equitable and inclusive access to AI applications
- Develop validation criteria to prevent bias, discrimination, and hate speech in GenAI systems
- Develop and implement standards and safeguards for GenAI systems to protect diversities and prevent discrimination

- Understand GenAI is a fast but frequently unreliable source of information
- Encourage learners and researchers to critique GenAI output
- Provide learners ample opportunities to learn from trial and error, empirical experiments, and real-world observations

Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693

Reference:

UNESCO – A policy framework for the use of generative AI (GenAI) in education and research

Measures and Considerations/ Suggested Actions

References:

[1] Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693