AI Policy Development in Education

When dealing with newer technologies, like generative AI, it is imperative to act quickly to ensure any use can occur in a manner, which not only protects the users, but also aligns with core values and objectives of the respective local and national environments.

Whilst navigating new situations and demands, the various steps necessary to develop comprehensive regulation and implementation structures can become blurred with actions occurring before fundamental discussions have taken place, which in turn can shape new actions. This becomes confusing both for those developing guidelines and those who are waiting for their support.

A framework for comprehensive AI policy development in education 

The following AI Policy Development Framework for Education supports exactly this work and make getting from ideas to practical implementation easier. A handy checklist and ideas for each section should provide further help. We have broken down the decisions and questions necessary to develop a comprehensive regulatory structure into three distinct areas of action. Central to this development structure is the regular revision and iteration of each area with knowledge learned through the development of the other areas.

1. Foundational Discussion: these outline core topics which must be clarified covering societal and cultural values, objectives for teaching and learning environments and education ecosystems, the governance and organisational structures required and key issues for prioritisation.

2. Activation: Starting with formulating core principles from the key issues and values identified in the foundational discussion, this section continues with understanding and prioritising areas of risk before seeking to align and leverage existing regulatory practices in order to streamline the process and provide clarity regarding outstanding issues. 

3. Implementation: This section outlines practical requirements for the sustainable implementation of newer technologies starting with the development of resources to support all stakeholders within the ecosystem. This is followed by identifying the organisational responsibility and key areas and roles to cover these as well as supportive implementation mechanisms. The topic of enforcement measures explores necessary accountability structures and stakeholder engagement investigates both supporting the wider community and international connections, which are so important with tools that can so rapidly cross borders and oceans.

FOUNDATION

1. Values

Each nation, cultural group, and region are guided by sets of societal and cultural norms. These are strongly reflected in local education practice. Defining these core values in relation to newer technologies can ensure that any policies created support and are guided by these values.

2. Objectives (benefits to be realised)

It is important to identify what bigger picture goals you have with the use of new technologies and resources. These can also encompass the benefits of use that you wish to leverage. 

3. Organisational structures: 

To ensure that implementation and enforcement mechanisms for regulation and guidelines can work, it is essential to develop organisational and governance structures which can align with existing national or regional structures and provide both an overview of responsibilities and contacts for all stakeholders. 

4. Key issues[2] 

In the process of understanding to which topics special attention needs to be paid or for which regulation mechanisms need to be developed, we first must identify which topics are seen as important, e.g., . 

• Data privacy

• Bias & fairness

• Transparency

• Accountability

• Ethical use and development

• Accessibility

• Teacher training

• Cybersecurity 

• Evaluation and validation

• Informed consent practices (particularly for students)

• Equity (digital equity)

• Diversity

• Interoperability of systems

• Using data of minors for training of systems

These can also be divided into areas of priority (e.g., urgent, emerging, long-term). 

ACTIVATION

If activation begins before the foundational discussions have started, it can be difficult to finalise work in the activation topics as important information will be missing, or it can require reworking of the activation mechanisms as decisions at the foundational level progress. 

1. Principles[3]

Hundreds of documents have been developed in the past years determining guiding principles for the development, governance, and implementation of AI. Although there are few, which deal directly with education, the vast majority of these documents cover similar topics and scopes. AI principles together with our objectives can give context to how we view the additional value that AI can bring and the methods necessary to ensure this occurs in a safe and appropriate manner. 

2. Understanding risk[4]

In determining the best manner of ensuring AI Safety[5], it is necessary to assess key areas of risk, areas requiring prioritised attention, and topics, pivotal to education environments such as youth development, quality of life and reputation and identity integrity, but which may not be included in otherwise strongly technical risk assessments. If an activity has a direct impact on learning possibilities, for example, it is important to assess associated risks. 

3. Alignment with and leveraging of existing regulations

Leveraging existing legal frameworks or tools and guidelines can reduce duplication, and increase the understanding of the topics, an acceptance of the needs and clarity around actions to be taken.                                                             

IMPLEMENTATION

1. Resources

​Resources must be aligned with the key foundational discussions and activation ideas. The following are examples of resources, which should be considered as part of the implementation process;

• Guidelines  [6]

• Professional development aides

• Information about testing for AI tools

• Public awareness campaigns to educate entire education communities

• Promotional tools educating on the topic of equity 

• Practical primers promoting understanding of key issues 

• Procurement support

2. Organisational Responsibility

• Establish any necessary regulatory or governance bodies and structures.

• Determine international collaboration: engage to harmonise AI regulations, ensuring global consistency.

• Institute long-term impact assessment bodies and engagement methods

3. Implementation Mechanisms

• Standards: develop accessibility standards, cyber security standards

• Rigorous evaluation process for AI tools in education

• Clear requirements and practices for obtaining informed consent

• The promotion of equitable access 

• Mechanisms for continuous assessment of AI’s impact

• Periodic review

• Promotion of research and AI development to foster innovation

• National and international interoperability

• AI Testbeds in Education 

• Change management methodologies

• Education training data stores

4. Enforcement mechanisms

• Enact laws and regulations that align with known standards

• Mandate transparency in AI algorithms

• Mitigate Bias (e.g. through regular auditing of AI systems)

• Define enforcement mechanisms and penalties for non-compliance

• Develop a flexible regulatory framework that can adapt to evolving AI technologies 

5. Stakeholder engagement

• Engagement with the public

• International collaborations and knowledge exchange

• Promoting the agency of learners, educators, and administration

• Promoting plural opinions and expressions of ideas[7]​

[1] https://news.microsoft.com/source/features/digital-transformation/how-nyc-public-schools-invited-ai-into-its-classrooms

See. also, EdSAFE AI Alliance Policy Guidance (https://www.edsafeai.org), Education Services Australia Principles to Policy (https://www.esa.edu.au), for methods of prioritising and iterating on key issues. See UNESCO Guidance for generative AI for education and research (https://unesdoc.unesco.org/ark:/48223/pf0000386693), European Commission’s Ethical Guidelines on the use of Artificial Intelligence and data in Teaching and Learning for Educators (https://education.ec.europa.eu/document/ethical-guidelines-on-the-use-of-ai-and-data-in-teaching-and-learning-for-educators)  for identification of education related issues.

[3] See: Berkmann Klein Center, Harvard, Principled Artificial Intelligence (https://cyber.harvard.edu/publication/2020/principled-ai), EdSAFE AI Alliance Policy Guidance Framework (https://www.edsafeai.org), UNESCO Guidance for generative AI for education and research(https://unesdoc.unesco.org/ark:/48223/pf0000386693)

[4] See: Montreal Declaration for responsible AI https://www.montrealdeclaration-responsibleai.com

[5] IAMAI Framework: https://sites.google.com/the.inter.net.au/iamai/home

[6] See Agua Fria School district (https://drive.google.com/file/d/1yP5YuEYpYPfwZ0hHpSVzvPav8lzGEv5V/view) or TeachAI resources (https://teachai.org)   ​

[7] UNESCO Guidance for generative AI for education and research (https://unesdoc.unesco.org/ark:/48223/pf0000386693)