# data[].attributes.headline
Exploring the ethics/practicalities of AI-assisted marking

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Exploring the ethics/practicalities of AI-assisted marking

# data[].attributes.body
Creating a future-focused AI and assessment strategy

# data[].attributes.body
Training and support for staff re AI. There are now a lot of free resources available, are people linking to them or creating their own stuff?

# data[].attributes.headline
Focus on the positives, not just the negatives of AI: what can help, what can improve

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Focus on the positives, not just the negatives of AI: what can help, what can improve

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Academic Integrity and Assessment design

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Academic Integrity and Assessment design

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Literacy, transparency, separation between learning and production (using AI)

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Literacy, transparency, separation between learning and production (using AI)

# data[].attributes.headline
Building institutional capabilities both staff and students

# data[].attributes.subject
Building institutional capabilities both staff and students

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1. Basic Staff AI Literacy support. 2. Supporting Institutional AI governance. 3. Encouraging innovation research in AI (RCA Research Centres)

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1. Basic Staff AI Literacy support. 2. Supporting Institutional AI governance. 3. Encouraging innovation research in AI (RCA Research Centres)

# data[].attributes.body
Agents to save time, streamline processes, support the student experience

# data[].attributes.headline
Creating compulsory, core AI literacy training for staff and students

# data[].attributes.subject
Creating compulsory, core AI literacy training for staff and students

# data[].attributes.body
Just showing a whole host of things at speed does not result in people using them effectively. Many need hand holding - showing on an individual basis how their day to day tasks can be done efficiently with AI. How do we do that at scale across institutions?

# data[].attributes.body
Including Digital Education as one of 5 'pillars'.

# data[].attributes.body
Staff training that likely would need to be trackable, yes like mandatory, but feel this is coming a bit like GDPR etc..

Staff/student sessions - its being used but where and when can it be used in assessments.

# data[].attributes.body
Institution wide assessment strategy/policy

# data[].attributes.headline
Safe and responsible use of AI across Professional Services (alongside the good work happening across L&T and Research

# data[].attributes.subject
Safe and responsible use of AI across Professional Services (alongside the good work happening across L&T and Research

# data[].attributes.body
Supporting staff to make effective use of AI for their own work, as well as supporting students to make effective/ethical/etc use of AI whether as a 'study support' or in ways they are likely to be expected to use it in the work place.

# data[].attributes.headline
Institution needs to get a handle on what tools it wants to get licenses for

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Institution needs to get a handle on what tools it wants to get licenses for

# data[].attributes.headline
Student disclosure, learning and reflection on AI use

# data[].attributes.subject
Student disclosure, learning and reflection on AI use

# data[].attributes.body
This academic year we've embedded F.A.I.R. for student reporting with assignment submissions. Find (what) Analyse (how you've used it) Inform (what impact AI has had in the assignment) and Reflect - how has it affected/developed learning?

# data[].attributes.body
Both with AI, and being confidence of learning outcomes met

# data[].attributes.body
We're going through a uni-wide revalidation process - helping colleagues think through their assessments in the light of GenAI

# data[].attributes.body
Clearer student guidance

AI Literacy

AI and Academic Integrity

# data[].attributes.headline
Share use cases, best practice and help us not reinvent the wheel :)

# data[].attributes.subject
Share use cases, best practice and help us not reinvent the wheel :)

# data[].attributes.headline
Continuing assessment redesign, targeting most AI-vulnerable assessment modes

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Continuing assessment redesign, targeting most AI-vulnerable assessment modes

# data[].attributes.headline
Developing assessments further to encourage AI collaboration and avoid AI collusion/exclusion

# data[].attributes.subject
Developing assessments further to encourage AI collaboration and avoid AI collusion/exclusion

# data[].attributes.body
Development of top-level guidance and policy, leading to strategy, around use of AI (being informed by what we have already done in the education space)

# data[].attributes.body
Aligning/streamlining AI approaches across research, education and professional services.

# data[].attributes.headline
Develop an improved cross-departmental understanding of AI tools and security

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Develop an improved cross-departmental understanding of AI tools and security

# data[].attributes.body
Assessing and fitting with institutional approval processes.

# data[].attributes.body
Colleagues in IT & Digital introducing tools to enable all staff and students to make use of wider variety of gen AI tools.

# data[].attributes.headline
Creating case studies from industry practice

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Creating case studies from industry practice

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The use of AI in the context of obtaining research funding 

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The use of AI in the context of obtaining research funding 

# data[].attributes.body
by professional services

by funders

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Review of assessments to make less suitable to AI being used

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Review of assessments to make less suitable to AI being used

# data[].attributes.headline
Creating an interactive chat model for department queries

# data[].attributes.subject
Creating an interactive chat model for department queries

# data[].attributes.headline
Framework of available digital tools to support teaching learning and assessement

# data[].attributes.subject
Framework of available digital tools to support teaching learning and assessement

# data[].attributes.body
Part of Jisc-Graide pilot - piloting Graide and evaluating usage with colleagues in formative assessment.

# data[].attributes.body
Not just using AI to cut corners

Understand learning is a choice and it matters to them and the lecturers

# data[].attributes.body
Would be useful if we could share the governance investigations institutions have covered already, to save the reinventions of the wheel - sure, for different institutions may have different nuances, but, generally things like accessibility might be the same.

# data[].attributes.headline
Acceptable use of AI in currciulum design (new courses)

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Acceptable use of AI in currciulum design (new courses)

# data[].attributes.body
How to select tools which will help make learning more inclusive and guide students/staff on how to use them within the bounds of academic integrity

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AI and ethics (moral, environmental, training using concerning employment methods)

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AI and ethics (moral, environmental, training using concerning employment methods)

# data[].attributes.headline
Getting staff past the idea that AI use will always save them time. Sometimes the context required will take more time to input, but what they get out may end up be much more effective or even transformational - not just more efficient. 

# data[].attributes.subject
Getting staff past the idea that AI use will always save them time. Sometimes the context required will take more time to input, but what they get out may end up be much more effective or even transformational - not just more efficient. 

# data[].attributes.headline
AI Literacy Framework, and Competency Framework

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AI Literacy Framework, and Competency Framework

# data[].attributes.body
At ARU, we have re-structured the AI groups to be more strategic / inform other strategies. One of my early jobs is to develop the two frameworks

# data[].attributes.headline
(Re)conceptualisation AI from 'tool' to  'collaborative' partner

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(Re)conceptualisation AI from 'tool' to  'collaborative' partner

# data[].attributes.body
To empower and inform educators so that they can harness their educational aims, values, disciplinary ways of thinking, doing, and being to make sound pedagogic decisions about AI in their teaching practice and in the curriculum.

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Curriculum Alignment with Industry Needs

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Curriculum Alignment with Industry Needs

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Collaboration between universities and AI industry partners is essential to ensure course content reflects emerging technologies, ethical considerations, and future skill requirements such as data literacy, digital ethics, creativity, and adaptability.