SADIE Project Report

Currently, most AI features can be disabled at the institution level – though this may change if use of AI becomes normalised. Most features are included in the license costs though this may change over time as many large language models are pay by use. Given the rapid pace of development of AI features, a common process is required to monitor, test and evaluate AI developments in each service.

The process will be described in brief in the rest of this section but is available to read in full on the SADIE SharePoint site (appendix one shows a high-level process map of the process for determining whether AI features are enabled): SADIE: Management of AI in EdTech Process 

Service teams will identify any AI features or tools within their services – through supplier release notes and/or conversations with supplier account managers. The service team will then seek clarity from the supplier on the compliance of the feature(s) with University policy and applicable legislation. If required, data protection and information security checks will be made. If the AI feature passes these checks, then a testing environment, or limited user access, will be set up to enable testing of the feature – this testing will include colleagues from the learning technology community across the University.

As a result of the testing by the appropriate service team, a Red, Amber, Green (RAG) status will be assigned to the feature. The RAG status is determined as a product of likelihood of switching on a feature (on a scale of 1 to 5 where 1 is almost certain and 5 is almost certainly not) and an assessment of the consequences of switching it on (on a scale of 1 to 5 where 1 is insignificant and 5 is almost catastrophic). The RAG status will be used to determine the next steps – enable or escalate for a decision.

The escalation route is given in the table below:

All decisions will be documented and recorded in a SharePoint site and communicated to users via the normal service channels.

Once a feature has been approved, guidance on its use will be added to the service’s existing documentation and, where appropriate, the feature will be subject to a training session or incorporated into existing training.

The service teams will continue to review all AI features monthly within their service – both disabled and enabled – to monitor and evaluate any changes as they occur. This will be captured in a full AI Matrix to allow service teams to regularly track progress and view the risks and RAG status of AI tools or features in centrally supported services.

This structured approach to monitoring the AI features provides transparency regarding the adoption, management and development of AI across all services. All decisions and rationale will be visible to users via the AI Innovations Service Release Tracker on the SADIE SharePoint site and communicated via the normal service channels.

The wider adoption of AI tools will likely require examination of existing University policies to cover AI and the management of its risks. Likely requirements for policy relating to enabling AI in central Learning Technologies would include:

1. Scope. Clarification that the scope of the policy covers enabling Generative AI within central learning and teaching services, and how each AI tool or feature may be used if it is enabled. Clarification over whether policy would need to cover discriminative AI.

    

2. Governance. Policy that there should be a governance process to consider and approve recommendations on whether and to what extent AI tools or features should be made available for use within central University learning and teaching services. An outline of this process, and the process to be followed where a vendor provides an AI tool or feature without the ability for the University to manage its availability. See: Management of AI in EdTech Process

    

3. Decision Factors. Details of the factors to be considered when deciding whether to enable an AI tool or feature, and whether subsequent training for staff or students should be mandated or recommended. This may include, among other things:

    

   1. An agreed framework of risk criteria, tabulating at least the likely AI risk factors against the University’s risk appetite in each area. See: Risk Identification Categories

       

   2. Whether the construction of the AI, including the training data acquisition or human input practices, are ethical.

       

   3. The extent to which the AI is shared with third parties beyond the University, or between different EdTech services, and the extent to which the AI retains its inputs as further training data.

       

   4. Consideration of how we will know whether an output is AI- or human-produced. This is likely to be relevant regardless of whether the output is to be assessed.

       

4. Service Register. Requirement to keep a register of central learning and teaching AI services, tools or features that the University has decided to make or not make available to staff and students, and of those on which it has not yet decided, and of the reasons for each decision.

    

5. User Notification. Policy or guidance on how staff and students are made aware that AI is available and is being used within a service.

    

6. Training. Policy or guidance on how much University staff, visitors, students or learners need to understand how an AI works before they use it. This should inform the training required before (i) using the AI at all (ii) using the AI to enhance processes that involve personal data, such as marking. See: Recommendations

    

7. Data and Content. Clear criteria describing the circumstances in which it might be permissible for staff or student personal data or copyright content to be entered or absorbed into the AI model. It may also be relevant to consider other data or work such as commercially sensitive data or unpublished research results. A University position on ownership of AI-generated content, on how AI-generated outputs are identified once produced, and on how these outputs may be used by members of the University or made available publicly.

    

8. Incident Management. Consider the process for what happens when something goes wrong because of the use of a Generative AI system and whether existing incident management and escalation procedures are sufficient. 

1. Services adopt the process for reviewing AI tools/features in EdTech.SADIE: Management of AI in EdTech Process

    

2. Consult with existing University AI groups, when appropriate, on high-risk decisions.

    

3. The introduction of a staff training course introducing the use of Generative AI for learning and teaching. This should equip users with the practical knowledge and understanding of language surrounding AI so that they can interact/use AI tools responsibly. 

    

4. An overview of relevant AI groups within Schools, Colleges and University wide should be created and maintained to avoid duplication of effort and to encourage collaboration.

    

5. Policy recommendations:

    

   1. Innovation: Consider how each aspect of the policy and procedures can encourage responsible innovation in the use of Generative AI.

        

   2. Public AI: Consideration of whether to encourage a wider University Policy on the extent to which public or semi-public AI services can be used for University teaching and assessment purposes.

       

   3. School and College Adoption: Consider whether the policy or procedures can be tailored to allow Schools and Colleges to adopt or repurpose them for services managed locally.

The biggest barrier to adoption for AI tools is likely to be clear assurances from suppliers on the compliance of their AI features with University policy and legal obligations. A common process will allow us to be consistent in the evaluation and adoption of AI tools and features.

The processes described for the evaluation of AI tools are an extended reworking of existing processes for the introduction of new non-AI features into services. As such there should not be much of a burden on service teams to adopt these and extend them to AI features.

It is important that decisions on the enabling of AI features are transparent to users. The AI Innovations Service Release Tracker, and the wider SADIE SharePoint site will give the rationale behind the approach adopted and decisions made. It will also provide advice on the risks of using a tool even if it has been made available.

The adoption of AI tools and features will likely require a review of University policies, potentially including but not limited to the OER, Lecture Recording, Virtual Classroom and Learning Analytics policies, to take account of the risks identified as part of this project.

High-level process map of the process for determining whether AI features are enabled.