Framework for AI in Education

General Guidelines and Considerations

A note for Alberta Educators

A note on First Steps

A note on Bias

Bias exists in all sources of information and AI is no different.  It is easily traced using four basic assumptions:

1. Software and Hardware are made by humans.

2. Humans have several biases that will influence development decisions.

3. Those biases, therefore, transfer to the software and hardware being developed.

4. The software or hardware provides a consistent experience to the user that includes built-in biases.

For this reason and others, including the overall faults possible with the algorithms that drive AI and other software, a "trust but verify" approach is advisable like all other levels of research conducted by staff and students.

Be aware that not all AI systems are created equal and do your homework on what they are using for a basis of information, for example, the language model the tool might be using. Like all research methods, information should be verified by multiple sources to improve accuracy. This also needs to be taken into consideration when the potential impacts of students accessing, using and participating in the use of AIs and LLMs.

A note on Indigenous Perspectives

Section under construction. There is currently not a significant body of research on Artificial Intelligence, Indigenous Perspectives and K-12 Education.

Guidelines for Indigenous-centred AI Design V.I (Lewis, et al, 2020, p. 20-22)

1. Locality: Indigenous knowledge is often rooted in specific territories. It is also useful in considering issues of global importance. AI systems should be designed in partnership with specific Indigenous communities to ensure the systems are capable of responding to and helping care for that community (e.g., grounded in the local) as well as connecting to global contexts (e.g. connected to the universal). 

2. Relationality and Reciprocity: Indigenous knowledge is often relational knowledge. AI systems should be designed to understand how humans and non-humans are related to and interdependent on each other. Understanding, supporting and encoding these relationships is a primary design goal. AI systems are also part of the circle of relationships. Their place and status in that circle will depend on specific communities and their protocols for understanding, acknowledging and incorporating new entities into that circle. 

3. Responsibility, Relevance and Accountability: Indigenous people are often concerned primarily with their responsibilities to their communities. AI systems developed by, with, or for Indigenous communities should be responsible to those communities, provide relevant support, and be accountable to those communities first and foremost. 

4. Develop Governance Guidelines from Indigenous Protocols: Protocol is a customary set of rules that govern behaviour. Protocol is developed out of ontological, epistemological and customary configurations of knowledge grounded in locality, relationality and responsibility. Indigenous protocol should provide the foundation for developing governance frameworks that guide the use, role and rights of AI entities in society. There is a need to adapt existing protocols and develop new protocols for designing, building and deploying AI systems. These protocols may be particular to specific communities, or they may be developed with a broader focus that may function across many Indigenous and non-Indigenous communities. 

5. Recognize the Cultural Nature of all Computational Technology: All technical systems are cultural and social systems. Every piece of technology is an expression of cultural and social frameworks for understanding and engaging with the world. AI system designers need to be aware of their own cultural frameworks, socially dominant concepts and normative ideals; be wary of the biases that come with them; and develop strategies for accommodating other cultural and social frameworks. Computation is a cultural material. Computation is at the heart of our digital technologies, and, as increasing amounts of our communication is mediated by such technologies, it has become a core tool for expressing cultural values. Therefore, it is essential for cultural resilience and continuity for Indigenous communities to develop computational methods that reflect and enact our cultural practices and values. 

6. Apply Ethical Design to the Extended Stack: Culture forms the foundation of the technology development ecosystem, or ‘stack.’ Every component of the AI system hardware and software stack should be considered in the ethical evaluation of the system. This starts with how the materials for building the hardware and for energizing the software are extracted from the earth, and ends with how they return there. The core ethic should be that of do-no-harm. 

7. Respect and Support Data Sovereignty: Indigenous communities must control how their data is solicited, collected, analysed and operationalized. They decide when to protect it and when to share it, where the cultural and intellectual property rights reside and to whom those rights adhere, and how these rights are governed. All AI systems should be designed to respect and support data sovereignty. Open data principles need to be further developed to respect the rights of Indigenous peoples in all the areas mentioned above, and to strengthen equity of access and clarity of benefits. This should include a fundamental review of the concepts of ‘ownership’ and ‘property,’ which are the product of non-Indigenous legal orders and do not necessarily reflect the ways in which Indigenous communities wish to govern the use of their cultural knowledge. 

Sources to be included:

A note on Ethics

FOIP and Personal Information

Recommendations for Policy

In this domain, the recommendation is clear: Follow Division and Provincial guidelines when dealing with third-party applications. Pay special attention to which tools use Single-Sign-On (SSO), Terms of Use and Privacy Impact Assessments. You must have permission from the parents of your students to use these applications as outlined in the Division policy above. Be prepared with an alternative method of instruction if permission is not acquired or revoked.

Remember that third-party applications that require use of a Google Chrome extension will need to be manually approved by the Division's TIPS team to be activated.

Course Outline Policies

Recommendations for Policy

AI Assessment Scale

Assessment, Cheating, Academic Honesty and "Detection"

Recommendations for Policy

Continue to utilize tools like "Originality Reports" in Google Classroom, conversations and establishing relationships with students before large-scale assessment where AI use is encouraged or probable. Clear expectations on the use of AI in student work and assessment will also potentially reduce misuse if correct use is modelled and encouraged. Use of Version History, DraftBack or WriteHuman to review how a document was put together on Google Docs is also a good method.

To help students navigate these scenarios, you may wish to employ a flowchart to help students make good decisions regarding AI like the one below. This chart was created based on the work done to create the decision-making flowchart for teachers found below:

Administrative Task Automation